JP2017091447A - Data output constraint device for solid object modeling - Google Patents

Abstract

PROBLEM TO BE SOLVED: To provide a data output constraint device for solid object modeling with which it is possible to determine whether or not to restrict output at high speed with a small processing load when outputting a solid object on the basis of a polygon model.SOLUTION: The data output constraint device includes: a constraint model database 40 in which is registered an inscribed circle distribution that is the frequency distribution of the radius of the inscribed circle of a triangle formed by three polygons within a constraint model; frequency distribution calculation means 10 for calculating the radius of circumscribed circle and the radius of inscribed circle of a triangle formed by prescribed points on three polygons selected from within an object model, and calculating inscribed circle distribution that is the frequency distribution of the radius of the inscribed circle normalized by using the maximum value of the radius of the circumscribed circle; and frequency distribution collation means 20 for collating the calculated inscribed circle distribution with the inscribed circle distribution of the constraint model registered in the constraint model database 40, and determining whether or not to restrict output.SELECTED DRAWING: Figure 2

Description

  In the present invention, when using a three-dimensional object forming apparatus such as a 3D printer that processes a resin or the like to form a three-dimensional object based on data representing the three-dimensional object, it is determined which is inappropriate for output from the three-dimensional object forming apparatus. It relates to technology.

  In recent years, 3D printers that process and model resin, gypsum, and the like based on data representing a three-dimensional object have become widespread as three-dimensional object modeling apparatuses. The 3D printer outputs a three-dimensional object using a polygon model that represents a three-dimensional shape of the three-dimensional object as a set of polygons. As a medical application of 3D printer, 3D printer output modeling data (polygon model) is created based on CT / MRI image (DICOM format 3D voxel data) taken by medical diagnosis. Attempts have been made to output organ models for informed consent or to create artificial organs (blood vessels, bones, joints, etc.) for implantation in the body.

  3D printers have an innovation that can propagate not only information but also things remotely to the remote location via the Internet. For this reason, there has been a problem that dangerous materials such as guns and swords that have been regulated by customs have been distributed across the border in the form of data and can be easily obtained as 3D printers.

  Specifically, STL (Standard Triangulated Language) that can manufacture a handgun of a predetermined model using a 3D printer is disclosed free of charge at a certain WEB site. Although the range is somewhat inferior to that of metal, bullets made with 3D printers have some killing ability. Furthermore, the material that can be formed by the current 3D printer is limited to resin and gypsum, and the disadvantage that metal cannot be used appears behind the scenes, and it is pointed out that it is overlooked by the metal detector at the airport. In the future, as 3D printers for consumer use become widespread, it is required to have security functions such as output authentication of output data input to the 3D printer and output regulation of dangerous goods.

  On the other hand, a method of constructing a system that determines whether or not output is possible by using a method similar to an anti-virus tool is conceivable, and a technique for matching polygons is used for realizing the system (see Patent Document 1). However, the following problems have been known for polygon matching. Polygon data for 3D printer output (STL format, etc.), the 3D coordinate value is not an absolute dimension, so the scale varies, and the coordinate origin and angle are also set according to the characteristics of the CAD that uses the printer and the printer to be output. Is done. Therefore, even if the source is the same 3D model, if the polygon data creation method is different, the total number of polygons to be expressed also changes, and it is generally difficult to determine the identity by comparing the coordinate values of the polygon data.

On the other hand, as in Patent Document 2, a comparison method that does not depend on an angle scale based on characteristic parameters such as moment of inertia from the center of gravity has been proposed. Patent Document 3 proposes a method of extracting feature quantities such as an area distribution from the center of gravity of a polygon model and a crease angle with an adjacent surface and performing a comparison independent of an angle scale. Patent Document 4 proposes a technique of performing Fourier transform on an image obtained by two-dimensionally projecting a three-dimensional model onto a plurality of angles, extracting features, and comparing them.

Japanese Patent Laying-Open No. 2015-162167 Japanese Patent No. 3610270 JP 2000-222428 A Japanese Patent No. 5024767

  When applying the above-described conventional technique to output regulation, a polygon model to be regulated is registered in a database and compared with a polygon model requested to be output. The target of output regulation may be not only dangerous materials such as guns and swords, but also copyrighted materials such as characters. In any case, the produced polygon model becomes a copyrighted work with a copyright holder.

  However, in order to register a polygon model that is a copyrighted work in the database, the act itself requires the permission of the author. However, it is difficult to obtain permission from the copyright holder who produced dangerous materials such as guns and swords. For this reason, there is a problem that it is difficult to construct a database for regulating the output of a copyrighted work. On the other hand, a method is conceivable in which a feature vector, which is information serving as a feature, is created from a polygon model, and a database is constructed from the feature vector. However, since the feature vector has a smaller amount of information than the polygon model, it has been difficult to accurately determine whether the output should be restricted.

  Therefore, the present invention provides a three-dimensional object modeling data output regulation device capable of performing a high-speed determination with a small processing load whether or not the output should be regulated when outputting a three-dimensional object based on a polygon model. The issue is to provide.

In order to solve the above problems, in the first aspect of the present invention,
A device for determining whether or not to regulate when outputting a polygon model expressed as a set of polygons to a three-dimensional object modeling apparatus as three-dimensional object modeling data,
A database in which an inscribed circle distribution, which is a frequency distribution based on the radius of a triangular inscribed circle formed based on three polygons in a restricted model that is a polygon model whose output is to be regulated, is registered;
For the target model that is the polygon model to be output, calculate the circumscribed circle radius and the inscribed circle radius of the triangle formed by the predetermined points on the three polygons selected from the target model. A frequency distribution calculating means for calculating an inscribed circle distribution which is a frequency distribution of the radius of the inscribed circle normalized using the maximum value of the radius of the circle;
The calculated inscribed circle distribution is collated with the inscribed circle distribution of the regulation model registered in the database, and a frequency distribution matching means for determining whether or not the output should be regulated;
There is provided a three-dimensional object shaping data output regulation device characterized by comprising:

  According to the first aspect of the present invention, there is provided a database in which an inscribed circle distribution that is a frequency distribution of the radius of a triangular inscribed circle formed based on three polygons in a restriction model is registered, The radius of the inscribed circle normalized by using the maximum value of the circumscribed circle radius and the radius of the circumscribed circle of the triangle formed by the predetermined points on the three polygons selected from To calculate the inscribed circle distribution, which is the frequency distribution, and check the calculated inscribed circle distribution against the inscribed circle distribution of the regulation model registered in the database to determine whether the output should be regulated or not Therefore, it is possible to determine at a high speed with a small processing load whether the output should be regulated. In particular, since the inscribed circle distribution is a frequency distribution of the radius of the inscribed circle normalized by using the maximum value of the radius of the inscribed circle, it is sensitive to the shape of the polygon model whether the output should be regulated. It is possible to perform with high accuracy by using the reflected inscribed circle distribution. Here, the fact that the inscribed circle distribution is sharply reflected in the shape of the polygon model has the following meaning. In the sphere that is the basis of the 3D shape, a wide range of a predetermined ratio (preferably 35% to 50%, for example, 1/2 (50%)) from the radius 0 to the radius of the sphere (the maximum value of the circumscribed circle radius). Distribution. Therefore, by normalizing the inscribed circle distribution using the maximum value of the radius of the circumscribed circle, as the shape deviates from the sphere, the distribution is locally biased in the direction of radius 0, and the complex reacts clearly to the shape difference. Distribution. In addition, “the radius of the inscribed circle normalized using the maximum value of the radius of the circumscribed circle” is not the maximum value of the radius of the circumscribed circle itself, but is normalized by a predetermined ratio of the maximum value of the radius of the circumscribed circle It means to do.

  In the second aspect of the present invention, the frequency distribution calculating means prepares a one-dimensional array composed of a predetermined number of elements when calculating the inscribed circle distribution, and calculates the calculated circumscribed circle. A range normalized with a value of 35% to 50% of the maximum value of the radius is equally divided into the predetermined number, and the radius of each inscribed circle is determined based on the radius value of the inscribed circle. The inscribed circle distribution is calculated by assigning to a predetermined number of elements (md) and counting the number (Hd (md)) corresponding to the element.

  According to the second aspect of the present invention, in calculating the inscribed circle distribution, a one-dimensional array composed of a predetermined number of elements is prepared, and the calculated maximum value of the radius of the inscribed circle is 35% to The range normalized by the value of 50% is equally divided into a predetermined number, and the radius of each inscribed circle is assigned to one of the predetermined number of elements based on the value of the radius of the inscribed circle. Since the inscribed circle distribution is calculated by counting the number corresponding to the element, a distribution in which the scale ratio between the circumscribed circle radius and the inscribed circle radius is maintained is created, and the spread of the distribution is narrow. Sharpens and shape discrimination becomes high.

  Further, in the third aspect of the present invention, the frequency distribution calculating unit classifies the polygons in the target model into three groups of a first group, a second group, and a third group for the target model, The three polygons are selected by selecting one polygon from each group.

  According to the third aspect of the present invention, the polygons in the target model are classified into three groups of the first group, the second group, and the third group, and one polygon is selected from each group. A combination of three polygons for forming a triangle can be created without overlapping each other, and efficient processing can be performed. If a random selection is made without classifying into groups, duplication often occurs with a certain probability. In the second mode, one polygon is selected from each group so that no overlap occurs.

  Further, in the fourth aspect of the present invention, the frequency distribution calculating means, when the number of polygons in each group is N / 3, is a random number array in which N / 3 consecutive integers are randomly replaced (shuffled). And generating an inverted random number array in which the order of the random number array from the beginning to the end is reversed, and among the three groups, the random number array is included in one group, and the inverted random number array is included in the other group. The polygons are selected in order from the top of each group after the order of the polygons in the group is changed by applying each array.

  According to the fourth aspect of the present invention, an inverted random number array in which a random number array in which consecutive integers of the number of polygons N / 3 in each group are randomly replaced is created and the order from the beginning to the end of the random number array is reversed After applying the random number array to one group and the inverted random number array to the other group, and changing the order of the polygons in the group, the polygons in order from the top of each group Since the selection order of the polygons of the three groups can be set at random from the position in the three-dimensional space, the inscribed circles of triangles calculated by a combination of random and non-overlapping polygons The inscribed circle distribution can be obtained based on the radius.

In the fifth aspect of the present invention, the frequency distribution calculating means includes
The random number array is applied to the first group of polygons and the inverted random number array is applied to the second group of polygons to change the order, and the polygons are selected without changing the order of the third group of polygons. N / 3 triangles are formed,
The random number array is applied to the second group of polygons and the inverted random number array is applied to the third group of polygons to change the order, and the polygons are selected without changing the order of the first group of polygons. N / 3 triangles are formed,
The random number array is applied to the third group of polygons and the inverted random number array is applied to the first group of polygons to change the order, and the polygons are selected without changing the order of the second group of polygons. N / 3 triangles are formed.

  According to the fifth aspect of the present invention, the random number array is applied to the first group of polygons and the reverse random number array is applied to the second group of polygons to change the order of the polygons without changing the order of the third group of polygons. Select to form N / 3 triangles, apply a random number array to the second group of polygons, and apply an inverted random number array to the third group of polygons to change the order, and change the order of the first group of polygons The polygons are selected to form N / 3 triangles, the order is changed by applying a random number array to the third group of polygons and an inverted random number array to the first group of polygons, and the second group of polygons. Since polygons are selected without changing the order of N / 3 triangles, the number of totas equal to the number N of polygons in the polygon model is set. N pieces of triangular can be formed without overlapping random and each other.

In the sixth aspect of the present invention,
The frequency distribution calculating means includes:
Each time the inscribed circle distribution is calculated, the process of calculating the inscribed circle distribution again by changing the order of polygons in two of the three groups is repeated a predetermined number of times, The average value of the calculated inscribed circle distribution is the inscribed circle distribution to be verified.

  According to the sixth aspect of the present invention, every time the inscribed circle distribution is calculated, the process of calculating the inscribed circle distribution again by changing the order of the polygons in two of the three groups. Since the average value of the calculated inscribed circle distribution is repeated a predetermined number of times to be the inscribed circle distribution to be collated, new N pieces that do not overlap with the already calculated N triangles are used. Since the inscribed circle distribution is calculated based on the triangle and the inscribed circle distribution is updated, the inscribed circle distribution is biased to a peculiar form because there are too few triangle samples that are combinations of three polygons. As a result, it is possible to obtain an accurate inscribed circle distribution.

In the seventh aspect of the present invention,
The frequency distribution calculating means includes:
Each time the inscribed circle distribution is calculated, the order of polygons in two groups among the three groups is changed, and the inscribed circle distribution is repeatedly calculated.
When the inscribed circle distribution immediately after the calculation is compared with the inscribed circle distribution obtained immediately before that, and the similarity is found in the comparison result, the inscribed circle distribution immediately after the calculation is It is characterized by a tangent circle distribution.

  According to the seventh aspect of the present invention, every time the inscribed circle distribution is calculated, the process of calculating the inscribed circle distribution again by changing the order of the polygons in two of the three groups. Repeatedly, the inscribed circle distribution immediately after the calculation is compared with the inscribed circle distribution obtained immediately before that, and if the comparison shows similarities, the inscribed circle distribution immediately after the calculation is Since the inscribed circle distribution is set, the optimal inner area is automatically determined by determining whether the inscribed circle distribution tends to be biased to a peculiar form when there are too few triangular samples that are combinations of three polygons. It is possible to obtain a tangent circle distribution.

In the eighth aspect of the present invention,
In the database, in addition to the inscribed circle distribution, a circumscribed circle distribution that is a frequency distribution of the radius of the circumscribed circle of the triangle is registered.
The frequency distribution calculating means calculates a circumscribed circle distribution that is a frequency distribution of the radius of the calculated circumscribed circle in addition to the inscribed circle distribution for the target model,
The frequency distribution matching means collates the circumscribed circle distribution of the calculated target model with the circumscribed circle distribution of the regulation model registered in the database in addition to the collation of the inscribed circle distribution, and outputs an output It is characterized by determining whether or not to regulate.

  According to the eighth aspect of the present invention, in addition to the inscribed circle distribution, a circumscribed circle distribution that is a frequency distribution of the radius of the circumscribed circle is registered in the database. The circumscribed circle distribution, which is a frequency distribution of the calculated radius of the circumscribed circle, is calculated, and in addition to matching the inscribed circle distribution, the circumscribed circle distribution of the target model is added to the circumscribed circle of the regulatory model registered in the database. Since the comparison with the distribution is made to determine whether or not the output should be regulated, the similarity between the two polygon models is judged from a plurality of viewpoints, and the judgment as to whether or not the output should be regulated is more accurate. Can be performed.

In the ninth aspect of the present invention,
In calculating the circumscribed circle distribution, the frequency distribution calculating unit prepares a one-dimensional array composed of a predetermined number of elements, and sets the range of the calculated maximum value of the radius of the circumscribed circle to the predetermined number. After dividing equally, the radius of each circumscribed circle is allocated to any one of the predetermined number of elements (ma) based on the value of the radius of the circumscribed circle, and the number corresponding to the element (Ha (ma)) The circumscribed circle distribution is calculated by counting.

  According to the ninth aspect of the present invention, in calculating the circumscribed circle distribution, a one-dimensional array composed of a predetermined number of elements is prepared, and the range of the calculated maximum value of the radius of the circumscribed circle is set to the predetermined number. Dividing evenly, assigning the radius of each circumscribed circle to a predetermined number of elements based on the value of the radius of the circumscribed circle, and calculating the circumscribed circle distribution by counting the number corresponding to the element Therefore, the circumscribed circle distribution can be easily calculated based on the radius of the circumscribed circle of the triangle formed based on the three polygons.

  In the tenth aspect of the present invention, the frequency distribution calculating means weights the average number of areas of the three selected polygons when counting the number corresponding to the element.

  According to the tenth aspect of the present invention, when calculating the circumscribed circle distribution or the inscribed circle distribution, the number corresponding to the assigned element is weighted with the average value of the areas of the three selected polygons. As a result, the difference in definition of polygon division does not reflect much on the circumscribed circle distribution or inscribed circle distribution, and is the same for multiple polygon models with different polygon division details (output should be regulated) It can be determined that

  In the eleventh aspect of the present invention, the frequency distribution calculating means may calculate the value of each element (Hd (md), Ha (ma) based on a total area value (Ssum) of polygons constituting each target model. ).

  According to the eleventh aspect of the present invention, in calculating the circumscribed circle distribution or the inscribed circle distribution, the value of each element is divided by the total value of the areas of the polygons constituting each target model. It is possible to obtain a circumscribed circle distribution or an inscribed circle distribution excluding the influence of the surface area and volume of the model.

  The twelfth aspect of the present invention further includes target model separation means for separating a target model, which is a polygon model to be output, into a plurality of partial target models, and the frequency distribution calculating means is provided for the partial target model. It is characterized by performing processing.

  According to the twelfth aspect of the present invention, first, the target model is separated into a plurality of partial target models, and the circumscribed circle distribution and the inscribed circle distribution are calculated for each partial target model. When collating a target model representing an article composed of parts, it is possible to appropriately determine whether or not the output is appropriate even when the restriction model and the target model have different part configurations.

  In the thirteenth aspect of the present invention, the polygon has a triangular shape, and the target model separation unit is configured so that a polygon and three adjacent polygons sharing sides with the polygon belong to the same partial target model. It is characterized by separating.

  According to the thirteenth aspect of the present invention, the polygon has a triangular shape, and the target model separation means separates the polygon and the three adjacent polygons sharing the sides with the polygon to belong to the same partial target model. Since it was made to do, it becomes possible to isolate | separate the object model expressing the articles | goods comprised by several components into several partial object model rapidly and accurately.

In the fourteenth aspect of the present invention,
The frequency distribution matching means, in matching the inscribed circle distribution calculated from the target model by the frequency distribution calculating means with the inscribed circle distribution registered in the database,
A correlation coefficient between the inscribed circle distributions is calculated, and when the calculated correlation coefficient is larger than a predetermined positive threshold, it is determined that the output should be regulated.

  According to the fourteenth aspect of the present invention, in comparing the inscribed circle distribution calculated from the target model with the inscribed circle distribution registered in the database, the correlation coefficient between the inscribed circle distributions is calculated and calculated. The output is determined to be regulated when the correlation coefficient is larger than a predetermined positive threshold, so the target model and the regulated model can be quickly and accurately matched. Is possible.

  Further, in the fifteenth aspect of the present invention, with respect to the restriction model, a radius of a circumscribed circle and a radius of the inscribed circle formed by predetermined points on three polygons selected from the restriction model are set. Receives the inscribed circle distribution calculated by the regulatory model frequency distribution calculation device that calculates the inscribed circle distribution that is the frequency distribution of the radius of the inscribed circle calculated and normalized using the maximum value of the radius of the inscribed circle. And a registration means for registering the received inscribed circle distribution in the database.

  According to the fifteenth aspect of the present invention, the radius of the circumscribed circle and the radius of the inscribed circle of the triangle formed by the predetermined points on the three polygons selected from the restricted model are calculated for the restricted model. In addition, a regulation model frequency distribution calculation device that calculates the inscribed circle distribution that is a frequency distribution of the radius of the inscribed circle normalized using the maximum value of the radius of the circumscribed circle is separately prepared. Since the inscribed circle distribution of the regulatory model is received and registered in the database, the database can be updated quickly from a remote location.

In the sixteenth aspect of the present invention,
The restriction model frequency distribution calculating device classifies the polygons in the restriction model into three groups of a first group, a second group, and a third group with respect to the restriction model, and assigns one polygon from each group. By selecting, the three polygons are selected.

  According to the sixteenth aspect of the present invention, when the inscribed circle distribution is calculated from the restriction model by the restriction model frequency distribution calculating device, the polygons in the restriction model are divided into three groups, the first group, the second group, and the third group. Since three polygons are selected by selecting one polygon from each group and selecting the inscribed circle distribution of the regulatory model, three polygons for forming a triangle are also selected. Polygon combinations can be created without overlapping each other, and efficient processing can be performed.

In the seventeenth aspect of the present invention,
Means for outputting the target model to a connected three-dimensional object shaping apparatus;
When it is determined that the output should be regulated (output inappropriate) by the frequency distribution matching unit executed in parallel with the three-dimensional object modeling process by the three-dimensional object modeling apparatus, Means for outputting an output stop command of the target model;
It further has these.

  According to the seventeenth aspect of the present invention, the target model is output to the connected three-dimensional object shaping apparatus, and it is determined that the output should be restricted as a result of the collation performed by the frequency distribution collation means executed in parallel. In this case, since the output stop command for the target model is output to the three-dimensional object modeling apparatus, the output can be canceled only when the output is inappropriate without delaying the modeling of the three-dimensional object that takes time. It becomes possible.

In the eighteenth aspect of the present invention,
The output control terminal and the processing server are connected via a network,
The output control terminal has the frequency distribution calculating means,
The processing server
The database;
Receiving means for receiving an inscribed circle distribution of the target model from the output control terminal via a network;
The frequency distribution matching means;
Output suitability data transmission means for transmitting data based on whether the output should be regulated or not, determined by the frequency distribution matching means, to the output control terminal;
It is characterized by having.

  According to the eighteenth aspect of the present invention, the target model receives data based on whether the output obtained by receiving the circumscribed circle distribution of the target model via the network and determining whether the output should be regulated or not. Since it is transmitted to the transmission source of the inscribed circle distribution, it is possible to provide the cloud type determination as to whether or not the output should be restricted, and to reduce the processing on the output side. In addition, the database can be centrally managed on the cloud side, and there is no need to manage the database in the output control terminal connected to each 3D object shaping device such as a 3D printer, so the output is always regulated based on the latest database. It is possible to determine whether or not to do so.

In the nineteenth aspect of the present invention,
The three-dimensional object shaping data output restriction device;
A three-dimensional object modeling apparatus that models a three-dimensional object using a polygon model whose output is permitted by the three-dimensional object modeling data output restriction device;
There is provided a three-dimensional object forming system.

  According to the nineteenth aspect of the present invention, the three-dimensional object modeling data output restricting device and the three-dimensional object modeling device that models the three-dimensional object using the polygon model whose output is permitted by the three-dimensional object modeling data output restricting device. Since the object modeling system is realized, the three-dimensional object modeling system can be provided in the form of a 3D printer or the like incorporating a board computer.

In the twentieth aspect of the present invention,
A device that creates a frequency distribution based on the polygon model in order to determine whether or not to restrict when a polygon model expressed as a set of polygons is output to the three-dimensional object modeling device as three-dimensional object modeling data. There,
For the target model that is the polygon model to be output, calculate the circumscribed circle radius and the inscribed circle radius of the triangle formed by the predetermined points on the three polygons selected from the target model. Provided is a frequency distribution calculation device that calculates an inscribed circle distribution that is a frequency distribution of the radius of an inscribed circle normalized using the maximum value of the radius of the circle.

  According to the twentieth aspect of the present invention, for a target model that is a polygon model to be output, a radius of a circumscribed circle of a triangle formed by predetermined points on three polygons selected from the target model, Since the radius of the inscribed circle is calculated and the inscribed circle distribution that is the frequency distribution of the radius of the inscribed circle normalized using the maximum value of the radius of the inscribed circle is calculated, the polygon model is shaped as a three-dimensional object. When outputting as three-dimensional object modeling data to the apparatus, it is possible to create a frequency distribution for determining whether or not to regulate at high speed with a small processing load.

  According to a twenty-first aspect of the present invention, there is provided a program for causing a computer to function as the three-dimensional object formation data output restriction device according to any one of the above aspects.

  According to the twenty-first aspect of the present invention, a program for causing a computer to function as a three-dimensional object shaping data output restriction device is provided. By incorporating this program, the computer becomes a three-dimensional object shaping data output restriction device. Function.

  According to the present invention, when outputting a three-dimensional object based on a polygon model, it is possible to determine at a high speed with a small processing load whether or not the output should be restricted.

1 is a hardware configuration diagram of a three-dimensional object formation system including a three-dimensional object formation data output restriction device 100 according to an embodiment of the present invention. It is a functional block diagram which shows the structure of the data output control apparatus for solid object modeling which concerns on one Embodiment of this invention. It is a flowchart which shows the process outline | summary of the data output control apparatus for solid object shaping which concerns on one Embodiment of this invention. It is a flowchart which shows the detail of the 1st method of the calculation process of the frequency distribution of step S100. It is a flowchart which shows the detail of the 2nd method of calculation processing of the frequency distribution of step S100. It is a figure which shows the circumscribed circle and the inscribed circle in the circumscribed circle distribution and the inscribed circle distribution obtained from the target model. It is a flowchart which shows the detail of the collation process of the frequency distribution of step S200. It is a flowchart which shows the detail of the object model separation process by an object model separation means. It is a figure which shows an example of the polygon group made into the object of a separation process. It is a figure which shows the vertex coordinate arrangement | sequence data for implement | achieving the polygon group shown in FIG. It is a flowchart which shows the detail of structuring of a polygon model. It is a figure which shows the relationship between a polygon group and a hash value. It is a figure which shows the state of each table at the time of initialization. It is a figure which shows the mode of a change of each table. It is a figure which shows the mode of a change of each table. It is a figure which shows the mode of a change of each table. It is a figure which shows the mode of a change of each table. It is a figure which shows the mode of a change of each table. It is a figure which shows the result of structuring of a polygon model. It is a flowchart which shows the detail of structuring of the polygon ridgeline of step S20. It is a figure which shows the state of each table at the time of the ridgeline structure of the polygon P1 being recorded. It is a figure which shows the state of each table at the time of the ridgeline structure of the polygon P2 being recorded. It is a figure which shows the state of each table at the time of the ridgeline structure of the polygon P3 being recorded. It is a figure which shows the state of each table at the time of the ridgeline structure of the polygon P18 being recorded. It is a figure which shows the completed ridgeline structure table and ridgeline table. It is a flowchart which shows the detail of preparation of the adjacent polygon table of step S30. It is a figure which shows the state of each table at the time of recording the edge line ID for each polygon of the polygon P2. It is a figure which shows the state of each table at the time of recording the edge line ID for each polygon of the polygon P4. It is a figure which shows the state of each table at the time of recording the edge line ID for each polygon of the polygon P18. It is a figure which shows the vertex structure table completed by step S10, the ridgeline structure table completed by step S20, and the adjacent polygon table completed by step S30. It is a flowchart which shows the detail of isolation | separation of the polygon model of step S40. It is a hardware block diagram of the solid object modeling system containing the data output control apparatus for solid object modeling in a modification. It is a hardware block diagram of a cloud type solid thing modeling system. It is a functional block diagram of a cloud type solid thing modeling system. It is a figure which shows the frequency distribution calculation example about a sphere. It is a figure which shows the frequency distribution calculation example about a sphere. It is a figure which shows the frequency distribution calculation example about a simulation handgun.

DESCRIPTION OF EXEMPLARY EMBODIMENTS Hereinafter, preferred embodiments of the invention will be described in detail with reference to the drawings.
<1. Device configuration>
FIG. 1 is a hardware configuration diagram of a three-dimensional object formation system including a three-dimensional object formation data output restriction device 100 according to an embodiment of the present invention. The three-dimensional object modeling data output restriction device 100 according to the present embodiment can be realized by a general-purpose computer. As shown in FIG. 1, a CPU (Central Processing Unit) 1 and a RAM (a main memory of the computer) Random Access Memory) 2, a large-capacity storage device 3 such as a hard disk or flash memory for storing programs and data executed by the CPU 1, a key input I / F (interface) 4 such as a keyboard and a mouse, and 3D A data input / output I / F (interface) 5 for data communication with an external device such as a printer or a data storage medium and a display unit 6 which is a display device such as a liquid crystal display are connected to each other via a bus. ing.

  The 3D printer 7 is a general-purpose 3D printer, which processes a material such as resin and gypsum on the basis of data for modeling a three-dimensional object, which is a polygon model expressing the three-dimensional shape of the three-dimensional object as a set of polygons. It is a three-dimensional object modeling apparatus to model. The 3D printer 7 includes a data processing unit 7a and an output unit 7b. The data processing unit 7a of the 3D printer 7 is connected to the data input / output I / F 5 so that the output unit 7b models the three-dimensional object based on the three-dimensional object modeling data received from the data input / output I / F 5. It has become.

  In FIG. 1, the three-dimensional object formation data output restriction device 100 and the 3D printer 7 are shown in a separated form, but most of the 3D printer products currently on the market include the three-dimensional object formation data output restriction device 100. Constituent elements, such as CPU1, RAM2, storage device 3, key input I / F4 (not a keyboard / mouse for general-purpose computers, but several buttons at a numeric keypad level), data input / output I / F5, display unit 6 (several lines) A small liquid crystal panel or a touch panel capable of displaying the above characters is often used as a key input I / F 4 in an overlapping manner. Accordingly, the 3D printer 7 itself can directly receive the three-dimensional object modeling data via the external storage medium, and can be operated to model the three-dimensional object alone (particularly this is more common in a 3D printer for consumer use). ). That is, the three-dimensional object modeling system shown in FIG. 1 is often housed in one housing and distributed as a product called “3D printer”.

  FIG. 2 is a functional block diagram illustrating a configuration of the three-dimensional object formation data output restriction device according to the present embodiment. In FIG. 2, 10 is a frequency distribution calculating means, 20 is a frequency distribution matching means, 30 is a target model storage means, and 40 is a regulation model database. The frequency distribution calculating means 10 calculates the circumscribed circle radius and the inscribed circle radius of the triangle formed by the polygons in the target model for the target model which is the polygon model to be output, and the circumscribed circle that is the calculation result Processing for calculating a circumscribed circle distribution that is a frequency distribution of the radius of the circle and an inscribed circle distribution that is a frequency distribution of the radius of the inscribed circle is performed. The frequency distribution matching unit 20 compares the circumscribed circle distribution and the inscribed circle distribution calculated for the target model with the circumscribed circle distribution and the inscribed circle distribution of the restriction model registered in the restriction model database 40, and outputs the result. It is determined whether or not to regulate, that is, whether the output is inappropriate or appropriate.

  The frequency distribution calculating unit 10 and the frequency distribution matching unit 20 are realized by the CPU 1 executing a program stored in the storage device 3. The target model storage unit 30 is a storage unit that stores a target model, which is a polygon model that is a determination target of whether or not output should be restricted, and is realized by the storage device 3. The polygon model is data representing a predetermined three-dimensional shape in a three-dimensional space by a set of polygons, and is output as three-dimensional object modeling data to a three-dimensional object modeling apparatus such as a 3D printer. In this embodiment, STL (Standard Triangulated Language) is adopted as the data format of the polygon model.

  The restriction model database 40 stores a circumscribed circle distribution and an inscribed circle distribution calculated as two types of frequency distributions with respect to a restriction model that is a polygon model whose output is to be restricted. This is realized by the device 3. The circumscribed circle distribution and the inscribed circle distribution, which are two kinds of frequency distributions, serve as feature vectors representing the features of the regulation model. That is, the difference between the original restriction models can be identified by the two types of frequency distributions, but the original restriction model cannot be restored. This is similar to the fact that individual differences can be identified by fingerprints, but the figure of the person itself cannot be restored. Therefore, the two frequency distributions do not play a role as a work, but also play a role as a so-called fingerprint that proves the identity of the two works. In the regulation model database 40, it is possible to register not only the circumscribed circle distribution and the inscribed circle distribution but also the regulation model itself that is a basis for calculating the circumscribed circle distribution and the inscribed circle distribution. The regulatory model itself is not registered due to copyright issues.

  Each component shown in FIG. 2 is actually realized by installing a dedicated program in hardware such as a computer and its peripheral devices as shown in FIG. That is, the computer executes the contents of each means according to a dedicated program. In this specification, the computer means an apparatus having an arithmetic processing unit such as a CPU and capable of data processing, and is incorporated not only in a general-purpose computer such as a personal computer but also in a “3D printer” as a product. Board computer included.

  In the storage device 3 shown in FIG. 1, a dedicated program for operating the CPU 1 and causing the computer to function as a three-dimensional object formation data output restriction device is mounted. By executing this dedicated program, the CPU 1 realizes functions as the frequency distribution calculating means 10 and the frequency distribution matching means 20. The storage device 3 not only functions as the target model storage unit 30 and the restriction model database 40 but also stores various data necessary for processing as the three-dimensional object formation data output restriction device.

  In the hardware configuration shown in FIG. 1, a frequency distribution calculating device for calculating a frequency distribution is provided by mounting only a dedicated program for causing the computer to function as the frequency distribution calculating means 10 in the storage device 3. Can also be realized. The frequency distribution calculation apparatus calculates and outputs two types of frequency distributions based on the target model read from the target model storage unit 30. The output frequency distribution is separately input to a device provided with the frequency distribution matching means 20 and the restriction model database 40, so that the matching and the output restriction can be determined.

<2. Processing action>
<2.1. Pretreatment>
Next, the processing operation of the three-dimensional object formation data output restriction device shown in FIGS. 1 and 2 will be described. First, a circumscribed circle distribution and an inscribed circle distribution, which are two types of frequency distributions, are created for a regulated model that is a polygon model to be regulated. The creation of the circumscribed circle distribution and the inscribed circle distribution from the polygon model can be performed in the same manner as the processing in the three-dimensional object formation data output restriction device described later. Creation of the circumscribed circle distribution and the inscribed circle distribution from the restriction model may be performed by the three-dimensional object formation data output restriction device, or may be performed by executing a similar program on another computer. The created circumscribed circle distribution and inscribed circle distribution are registered in the regulation model database 40.

  The target of output regulation may be not only dangerous materials such as guns and swords, but also copyrighted materials such as characters. In any case, the produced polygon model becomes a copyrighted work with a copyright holder. Therefore, in order to register a polygon model, which is a work, in the database, permission for the author is required for the act itself. Although the difference between the original polygon model can be identified by the forms of the circumscribed circle distribution and the inscribed circle distribution, the original polygon model cannot be restored. This is similar to the fact that individual differences can be identified by fingerprints, but the figure of the person itself cannot be restored, and the circumscribed circle distribution and inscribed circle distribution form is equivalent to the fingerprint. However, it does not fall under the copyrighted work. Thus, by registering in the form of circumscribed circle distribution and inscribed circle distribution as in the present invention, it is possible to construct a database that records the characteristics of the regulatory model without requiring the author's permission.

<2.2. Process Overview>
Next, the processing operation of the three-dimensional object formation data output restriction device shown in FIGS. 1 and 2 will be described. FIG. 3 is a flowchart showing a processing outline of the three-dimensional object formation data output restriction device according to the embodiment of the present invention. First, the frequency distribution calculating means 10 calculates a circumscribed circle distribution and an inscribed circle distribution that are two types of frequency distributions for the target model that is a polygon model (step S100). Then, the calculated frequency distribution of the target model is collated with the frequency distribution of the regulation model registered in the regulation model database 40 (step S200).

<2.3. Frequency distribution calculation processing>
<2.3.1. First Method>
First, the frequency distribution calculation process in step S100 will be described. There are two types of frequency distribution calculation processing: the first method and the second method. First, the first method will be described. FIG. 4 is a flowchart showing details of the frequency distribution calculation processing by the first method. Here, the number of polygons of the target model is N, and the variable i (i = 0,..., N−1) for specifying the polygon is used, and the vertices of each polygon i are (Xu (i), Yu (i), Zu ( i)). u indicates a vertex number. In this embodiment, since the polygon is triangular, three values of u = 0, 1, and 2 are taken. Therefore, for example, Xu (i) is also expressed as X0 (i), X1 (i), and X2 (i). The polygons and normal vectors specifying the vertices are necessary for output by a 3D printer, which is a three-dimensional object forming apparatus, and the direction of the normal vectors indicates the outside of the forming surface (from the material to the air). The normal vector is normally recorded for each polygon in a polygon model for output to a 3D printer. The frequency distribution calculating means 10 first calculates the total area Ssum, which is the sum of the areas S (i) and N polygons S (i) of each polygon i constituting the target model, according to the following [Equation 1]. The calculation is performed by executing the process (step S501).

[Formula 1]
Vx = [Y1 (i) -Y0 (i)] [Z2 (i) -Z0 (i)]-[Z1 (i) -Z0 (i)] [Y2 (i) -Y0 (i)]
Vy = [Z1 (i) -Z0 (i)] [X2 (i) -X0 (i)]-[X1 (i) -X0 (i)] [Z2 (i) -Z0 (i)]
Vz = [X1 (i) -X0 (i)] [Y2 (i) -Y0 (i)]-[Y1 (i) -Y0 (i)] [X2 (i) -X0 (i)]
S (i) = [Vx (i) ² + Vy (i) ² + Vz (i) ² ] ^1/2
Ssum = Σ _{i = 0, N-1} S (i)

  In the above [Equation 1], the subscript “i = 0, N−1” of Σ indicates that the sum is obtained when i is an integer from 0 to N−1. Next, for each polygon constituting the target model, an average point that is the average of the vertices is calculated (step S502). Specifically, a polygon average point (Xc (i), Yc (i), Zc (i)) that is an average point of each polygon is calculated by executing processing according to the following [Equation 2]. .

[Formula 2]
Xc (i) = {X0 (i) + X1 (i) + X2 (i)} / 3
Yc (i) = {Y0 (i) + Y1 (i) + Y2 (i)} / 3
Zc (i) = {Z0 (i) + Z1 (i) + Z2 (i)} / 3

  In the above [Equation 2], X0 (i), X1 (i), and X2 (i) represent the cases of vertex numbers u = 0, 1, and 2 in Xu (i), respectively, and Y0 (i), Y1 ( i) and Y2 (i) respectively indicate the cases of vertex numbers u = 0, 1, and 2 in Yu (i), and Z0 (i), Z1 (i), and Z2 (i) are in Zu (i), respectively. , Vertex numbers u = 0, 1, and 2 are shown. As shown in the above [Equation 2], the polygon average point is calculated as a point having the average coordinates of each vertex of the polygon.

  Next, a set of polygons constituting the polygon model is divided into three groups, and the order of the polygons in each group is changed (step S503). As a method of dividing into three groups, any method may be used as long as it can be divided into the same number. In this embodiment, based on the order in the polygon model data array, the first group of polygons in the order in which the remainder is 0 when divided by 3 and the second group of polygons in the order in which the remainder is 1 after being divided by 3 are used. It is divided into a group and a third group of polygons in the order in which the remainder is 2 divided by 3. As a result, N polygons constituting the polygon model are divided into groups of N / 3. When the remainder of 3 of the original polygon number N is 2 so that the number of polygons in each group is the same, the top polygon 0 is overlapped as the last polygon N = polygon 0, and the total number of polygons is Modify to be N + 1. If the remainder of 3 of the original polygon number N is 1, the first polygon 0 and the second polygon 1 are overlapped as the second polygon N = polygon 0 from the tail and the last polygon N + 1 = polygon 1 In any case, the total number of polygons is corrected to N + 2. Even if the number of polygons is duplicated and the total number of polygons is N + 1 or N + 2 so that the number of polygons in each group is the same, the total number of polygons will be replaced with N in the following. I will explain.

  By grouping by the remainder divided by 3, three arrays G1 (h) = 3h, G2 (h) = 3h + 1, G3 (h) = 3h + 2 (h = 0,..., N / 3-1) Is obtained. Next, the arrangement order of the polygons in each group is changed. The order is changed randomly. That is, it is shuffled. Any method may be used as long as the order of polygon arrangement can be changed. In this embodiment, the order of polygon arrangement is changed by the following method. First, three types of random number arrays R1 (k), R2 (k), and R3 (k) are created. Specifically, k = 0,..., N / 3-1 are initialized to R1 (k) = k. Subsequently, a real-valued uniform random number is generated N / 3 times within a range of 0 ≦ Rnd (k) <1, and every time it is generated, pp = Rnd (k) × N / 3 is calculated as 0 ≦ An integer value of pp ≦ N / 3 is calculated, and the process of exchanging the value of R1 (k) and the value of R1 (pp) is repeated N / 3 times. As a result of repetition, a random number array R1 (k) in which N / 3 consecutive integers are randomly replaced is obtained. Accordingly, for example, h = R1 (k) is given to the polygon array G1 (h) of the first group, and G1 (R1 (k)) = 3R1 (k) (k = 0,..., As in (N / 3-1), an array in which the order is changed can be obtained. The second random number array R2 (k) is given by R2 (k) = R1 (N / 3-1-k). That is, the random number array R2 (k) is obtained by reversing the order from the beginning to the end of the first random number array R1 (k). The random number array R1 (k) and the random number array R2 (k) are the other inverted random number arrays. In addition, the random number array R3 (k) = k is set to a sequential value of k = 0,..., N / 3-1 without using random numbers. In this way, by rearranging the polygon arrangement order, the polygon arrangement order of each group can be set randomly from the polygons in the target model that is a three-dimensional space, and a combination of points on the random polygons. The circumscribed circle distribution and the inscribed circle distribution can be obtained based on the radius of the circumscribed circle and the radius of the inscribed circle of the triangle created in step (1).

  The reason why the polygons are classified into the three groups in this way is to prevent the polygons from overlapping each other when the three polygons are randomly selected to create a triangle. Although it is possible to sequentially form triangles by combining all polygons constituting the polygon model, if the number of polygons is large, the number of combinations becomes enormous, which is not practical. A method of forming a triangle by randomly selecting three polygons from all the polygons constituting the polygon model without grouping is also conceivable, but the same triangle is generated with a certain probability. If the number of triangle samples extracted at random is not large enough, a biased frequency distribution may be calculated. As in this embodiment, the three polygons for forming a triangle are overlapped with each other by classifying into three groups, selecting one polygon from each group and selecting a total of three. It is possible to select efficiently while providing randomness.

  Next, initial setting is performed (step S504). Specifically, the circumscribed circle distribution to be calculated, the inscribed circle distribution are initialized, and the loop counter is initialized. The circumscribed circle distribution calculated in the present embodiment is a distribution of the radius of a triangular circumscribed circle obtained by weighting the area of the polygon. The number of elements is MD, and each element md (md = 0,..., MD− The frequency of 1) is expressed as Hd (md). Hd (md) is a one-dimensional array composed of a predetermined number of MD elements. After preparing such a one-dimensional array, the frequency distribution calculating means 10 sets the initial value as Hd (md) = 0. The inscribed circle distribution calculated in the present embodiment is a distribution of the radius of a triangular inscribed circle obtained by weighting the area of a polygon. The number of elements is MA, and each element ma (ma = 0,..., The frequency of MA-1) is expressed as Ha (ma). The element number MA may be the same as the element number MD. Ha (ma) is a one-dimensional array composed of a predetermined number of elements. After preparing such a one-dimensional array, the frequency distribution calculating means 10 sets the initial value as Ha (ma) = 0. For the loop counter LC, an initial value LC = 0 is set.

  Next, the frequency distribution calculating unit 10 creates a triangle having the vertexes of the respective polygon average points among the three groups (step S505). When creating a triangle using three polygons, various points can be used as points on the polygon, not limited to the polygon average point. However, since the polygon average point is preferable as a point representing one polygon, in this embodiment, a triangle is created with the polygon average point as a vertex. The point on the polygon means a point located on a plane passing through all the vertices constituting the polygon and within a range surrounded by these vertices. In step S505, three combinations are created by changing the combinations between groups so that the number of triangles is the same as the total number N of polygons. Specifically, the first group G1 (h = 0,..., N / 3-1) -th polygon G1 (h) is randomly replaced by a random number array R1 (k) (G1 ( R1 (k)) average points (Xc (G1 (R1 (k))), Yc (G1 (R1 (k))), Zc (G1 (R1 (k)))), the corresponding order of the second group The average points (Xc (G2 (R2 (k))), Yc (G2 (R2) of G2 (R2 (k)) obtained by randomly changing the order of the polygon G2 (h) of h by the random number array R2 (k). (K))), Zc (G2 (R2 (k)))), G3 (3) in which the order is randomly changed by the random number array R3 (k) with respect to the polygon G3 (h) in the corresponding order h of the third group. R3 (k)) average points (Xc (G3 (R3 (k))), Yc (G3 (R3 (k))), Z (G3 (R3 (k)))) the first triangle to N / 3 pieces created as vertices the three points.

  Similarly, the average point (Xc (G2 (R1 (k)) of G2 (R1 (k)) obtained by randomly changing the order of the h-th polygon G2 (h) of the second group by the random number array R1 (k). )), Yc (G2 (R1 (k))), Zc (G2 (R1 (k)))), and the third group corresponding to the polygon G3 (h) in the order h by the random number array R2 (k). G3 (R2 (k)) average points (Xc (G3 (R2 (k))), Yc (G3 (R2 (k))), Zc (G3 (R2 (k)))) whose order was randomly changed The average point (Xc (G1 (R3 (k)) of G1 (R3 (k)) obtained by randomly changing the order of the polygon G1 (h) corresponding to the order h in the first group by the random number array R3 (k). )), Yc (G1 (R3 (k))), Zc (G1 (R3 (k)))) The second triangle to a point N / 3 pieces to create.

  Further, the average point (Xc (G3 (R1 (k))) of G3 (R1 (k)) obtained by randomly changing the order of the h-th polygon G3 (h) of the third group by the random number array R1 (k). ), Yc (G3 (R1 (k))), Zc (G3 (R1 (k)))), random corresponding to the polygon G1 (h) in the corresponding order h of the first group by the random number array R2 (k). G1 (R2 (k)) average points (Xc (G1 (R2 (k))), Yc (G1 (R2 (k))), Zc (G1 (R2 (k)))), The average point (Xc (G2 (R3 (k))) of G2 (R3 (k)) in which the order is randomly changed by the random number array R3 (k) with respect to the polygon G2 (h) in the corresponding order h of the second group. ), Yc (G2 (R3 (k))), Zc (G2 (R3 (k)))) A third triangle to a point N / 3 pieces to create.

  Next, the frequency distribution calculating means 10 calculates the radius of a circumscribed circle of a triangle having the polygon average point as a vertex (step S506). Specifically, first, by executing the processing according to the following [Equation 3], the area S (k) of the first triangle is set in the range of k = 0,..., N / 3-1. calculate.

[Formula 3]
D12 = [[Xc (G2 (R2 (k)))-Xc (G1 (R1 (k)))] ² + [Yc (G2 (R2 (k)))-Yc (G1 (R1 (k))) ] ² ] ^1/2
D23 = [[Xc (G3 (R3 (k))) − Xc (G2 (R2 (k)))] ² + [Yc (G3 (R3 (k))) − Yc (G2 (R2 (k))) ] ² ] ^1/2
D31 = [[Xc (G1 (R1 (k))) − Xc (G3 (R3 (k)))] ² + [Yc (G1 (R1 (k))) − Yc (G3 (R3 (k))) ] ² ] ^1/2
D = (D12 + D23 + D31) / 2
S (k) = [D · (D−D12) · (D−D23) · (D−D31)] ^1/2

  In the above [Equation 3], “·” indicates multiplication. The same applies to the following [Formula]. Then, the radius D (k) of the circumscribed circle of the first triangle is calculated in the range of k = 0,..., N / 3-1 by executing the processing according to the following [Equation 4]. .

[Formula 4]
D (k) = D12 · D23 · D31 / (4 · S (k))

  Similarly, the frequency distribution calculating means 10 executes a process according to the following [Equation 5], whereby the area S (2) of the second triangle in the range of k = 0,..., N / 3-1. k) is calculated.

[Formula 5]
D12 = [[Xc (G3 (R2 (k))) − Xc (G2 (R1 (k)))] ² + [Yc (G3 (R2 (k))) − Yc (G2 (R1 (k))) ] ² ] ^1/2
D23 = [[Xe (G1 (R3 (k)))-Xc (G3 (R2 (k)))] ² + [Yc (G1 (R3 (k)))-Yc (G3 (R2 (k))) ] ² ] ^1/2
D31 = [[Xc (G2 (R1 (k))) − Xc (G1 (R3 (k)))] ² + [Yc (G2 (R1 (k))) − Yc (G1 (R3 (k))) ] ² ] ^1/2
D = (D12 + D23 + D31) / 2
S (k) = [D · (D−D12) · (D−D23) · (D−D31)] ^1/2

  Then, the radius D (k) of the circumscribed circle of the second triangle is calculated in the range of k = 0,..., N / 3-1 by executing the processing according to the above [Equation 4].

  Similarly, the frequency distribution calculating means 10 executes the processing according to the following [Equation 6], whereby the area S (3) of the third triangle in the range of k = 0,..., N / 3-1. k) is calculated.

[Formula 6]
D12 = [[Xc (G1 (R2 (k)))-Xc (G3 (R1 (k)))] ² + [Yc (G1 (R2 (k)))-Yc (G3 (R1 (k))) ] ² ] ^1/2
D23 = [[Xc (G2 (R3 (k))) − Xc (G1 (R2 (k)))] ² + [Yc (G2 (R3 (k))) − Yc (G1 (R2 (k))) ] ² ] ^1/2
D31 = [[Xc (G3 (R1 (k))) − Xc (G2 (R3 (k)))] ² + [Yc (G3 (R1 (k))) − Yc (G2 (R3 (k))) ] ² ] ^1/2
D = (D12 + D23 + D31) / 2
S (k) = [D · (D−D12) · (D−D23) · (D−D31)] ^1/2

  Then, by executing the processing according to the above [Equation 4], the radius D (k) of the circumscribed circle of the third triangle is calculated in the range of k = 0,..., N / 3-1.

  In the above [Equation 3], [Equation 5] and [Equation 6], only the triangle in which the lengths D12, D23 and D31 of each side of the triangle satisfy all the six conditions shown in the following [Equation 7] The target of the frequency distribution of the radius. Therefore, a triangle that does not satisfy even one of the six conditions shown in [Formula 7] is not subject to the frequency distribution of the circumscribed circle radius. This is because the radius D (k) of the circumscribed circle becomes extremely large when the six conditions shown in the following [Equation 7] are not satisfied, and a highly accurate frequency distribution cannot be obtained.

[Formula 7]
D12> 0
D23> 0
D31> 0
| D12-D23-D31 | / D12> 0.1
| D23-D31-D12 | / D23> 0.1
| D31-D12-D23 | / D31> 0.1

  As shown in [Expression 7] above, the first three conditions out of the six conditions mean that all sides take positive values other than 0, that is, form a triangle. The latter half of the six conditions means that the triangle to be created does not have an extremely flat shape. If the triangle becomes extremely flat, the radius of the circumscribed circle will approach infinity, and a highly accurate frequency distribution will not be obtained. When the six conditions shown in [Expression 7] are not satisfied, a triangle is not formed (when either side is 0), or the radius D (k) of the circumscribed circle is extremely large, and the frequency is high in accuracy. This is because the distribution cannot be obtained.

  The radius of the circumscribed circle of N / 3 first triangles, the radius of the circumscribed circle of N / 3 second triangles, and the circumscribed circle of N / 3 third triangles calculated by the above [Equation 4] The N / 3 array D (k) of the second triangle is offset by N / 3 for the N / 3 array D of the second triangle, and the N / 3 array of the third triangle is added to the N / 3 array of the third triangle. Is added with an offset of 2N / 3, and is grouped with a serial number i, the range is reset to i = 0,..., N−1, and the circumscribed circle radius D (i) ( i = 0,..., N-1). And the largest one among the N circumscribed circle radii D (i) of i = 0,..., N−1 is set as the maximum circumscribed circle radius Dmax.

  Next, the frequency distribution calculation means 10 calculates the radius of the inscribed circle of the triangle having the polygon average point as a vertex (step S507). Specifically, by executing the processing according to the following [Equation 8], within the range of k = 0,..., N / 3-1, the first triangle, the second triangle, and the third triangle. The radius A (k) of the tangent circle is calculated.

[Formula 8]
A (k) = 2 · S (k) / (D12 + D23 + D31)

  In the above [Expression 8], D12, D23, D31 and S (k) are the first triangle, the second triangle, the third triangle calculated by the processing according to the above [Expression 3], [Expression 5] and [Expression 6]. The length and area of the three sides of the triangle. Note that a triangle that does not satisfy any one of the six conditions shown in [Equation 7] is not subject to the frequency distribution of the inscribed circle radius.

  The radius A (k) of the inscribed circle of N / 3 first triangles calculated by the above [Equation 8], the radius A (k) of the inscribed circle of N / 3 second triangles, and N / 3 of N / 3 arrays A (k) of inscribed circle radius A (k) of 3rd 3 triangles, N / 3 for N / 3 arrays of 2nd triangle Add an offset only, add an offset of 2N / 3 to the N / 3 array for the third triangle, and reset the range of serial number i to i = 0, ..., N-1 The radius A (i) (i = 0,..., N−1) of N inscribed circles of N triangles is obtained.

  Next, the frequency distribution calculating means 10 calculates a circumscribed circle distribution that is a frequency distribution of the radius of the circumscribed circle of the triangle (step S508). Specifically, the frequency Hd (md) of the element md is calculated for i = 0,..., N−1 by executing processing according to the following [Equation 9].

[Formula 9]
If Dmax · md / MD ≦ D (i) <Dmax · (md + 1) / MD,
Hd (md) ← Hd (md) + S
When 0 ≦ i ≦ N / 3-1 S = [σ (G1 (R1 (i))) + σ (G2 (R2 (i))) + σ (G3 (R3 (i)))] / 3
In the case of N / 3 ≦ i ≦ 2N / 3-1 S = [σ (G2 (R1 (i))) + σ (G3 (R2 (i))) + σ (G1 (R3 (i)))] / 3
When 2N / 3 ≦ i ≦ N−1 S = [σ (G3 (R1 (i))) + σ (G1 (R2 (i))) + σ (G2 (R3 (i)))] / 3

  In the above [Equation 9], σ (p) is the area of the polygon p classified into three groups. [Equation 9] is obtained when the circumscribed circle radius D (i) of the triangle is equal to or larger than the value obtained by multiplying the maximum circumscribed circle radius Dmax by md / MD and smaller than the value obtained by multiplying (md + 1) / MD. This means that the average area S of three polygons having vertices as polygon average points is added to Hd (md).

  The circumscribed circle distribution Hd (md) (md = 0,..., MD-1) is calculated by executing the processing according to the above [Equation 9] for N triangles. Since each element is added not with a simple frequency but with the area of a polygon, the circumscribed circle distribution Hd (md) represents an area-weighted circumscribed circle distribution. It is possible to simply add 1 instead of area, but in that case, the circumscribed circle distribution does not take into account the area of the polygon, and when the polygon configuration is coarse and fine for the same polygon model, There will be a significant difference in the circumscribed circle distribution. Therefore, in the present embodiment, the area of the polygon is added when calculating the circumscribed circle distribution. When only 1 is added instead of the area, the circumscribed circle distribution is calculated by counting the number Hd (md) corresponding to a certain element md.

  Next, the frequency distribution calculation means 10 calculates an inscribed circle distribution that is a frequency distribution of the radius of the inscribed circle of each triangle (step S509). Specifically, the frequency Ha (ma) of the element ma is calculated for i = 0,..., N−1 by executing processing according to the following [Equation 10].

[Formula 10]
If Dmax · ma / 2MA ≦ A (i) <Dmax · (ma + 1) / 2MA,
Ha (ma) ← Ha (ma) + S

  As for the average area S of the three polygons in [Expression 10], the average area S calculated by the process according to [Expression 9] is used. [Expression 10] is obtained when the radius A (i) of the inscribed circle of the triangle is equal to or larger than the value obtained by multiplying the maximum circumscribed circle radius Dmax by ma / 2MA and smaller than the value obtained by multiplying (ma + 1) / 2MA. This means that the average area S of three polygons having the vertices of triangles as average points is added to Ha (ma). This is because the vertex of the triangle is taken as the average point when the value is equal to or greater than the value obtained by multiplying Dmax / 2, which is half the maximum circumscribed circle radius Dmax, by ma / MA and less than (ma + 1) / MA. This is the same as adding the average area S of the three polygons to Ha (ma).

  Eventually, by executing the processing according to the above [Equation 10], the frequency distribution calculating means 10 prepares a one-dimensional array composed of elements of a predetermined number MA, and calculates the maximum radius of the circumscribed circle calculated. A range normalized by a value of 50% of the value (maximum circumscribed circle radius Dmax) is equally divided into a predetermined number MA, and the radius A (i) of each inscribed circle is determined based on the value. The inscribed circle distribution is calculated by assigning to any element ma of the number MA and counting the number corresponding to the element ma.

Since the circumscribed circle radius and the inscribed circle radius depend on the scale of the polygon model, if the distribution is created by normalizing the horizontal axis to the range of the maximum value, a distribution independent of the scale can be obtained. At this time, if you create an independently normalized distribution with different maximum values for the circumscribed circle radius and the inscribed circle radius, you can create a distribution that does not depend on the scale between the circumscribed circle radius and the inscribed circle radius. On the other hand, the robustness against deformation becomes strong, but the shape discrimination becomes weak. In the present invention, since the distribution in which the inscribed circle radius is normalized using the maximum circumscribed circle radius, which is the same reference value as the inscribed circle radius, is created, a scale between the inscribed circle radius and the inscribed circle radius is obtained. A distribution in which the ratio is maintained is obtained, and the spread of the distribution becomes narrower and sharper and the shape discriminability becomes higher than in the case of independently normalizing with maximum values different between the circumscribed circle radius and the inscribed circle radius. Actually, since the inscribed circle radius does not take a value that is 1/2 (50%) or more of the maximum value of the circumscribed circle radius (maximum circumscribed circle radius), normalization should be performed within the range of 1/2 of the maximum value. To. In the present embodiment, as shown in the above [Equation 10], normalization is performed with Dmax / 2, which is 1/2 the maximum circumscribed circle radius Dmax. The normalization range can be set as appropriate, but is preferably 35% to 50% of the maximum value of the circumscribed circle radius, that is, 7/20 to 10/20 (1/2). If it is less than 35%, the ratio of the inscribed circle radius exceeding the maximum value of the element ma (scale) (35% of the maximum value of the circumscribed circle radius) increases and the distribution is concentrated on the maximum value of the element ma. Does not accurately reflect the shape. Further, when it exceeds 50%, a distribution in which the blank on the maximum value side of the element ma is large is formed.

  The inscribed circle distribution Ha (ma) (ma = 0,..., MA−1) is calculated by executing the processing according to the above [Equation 10] for N triangles. Since the polygon area is added to each element, not just the frequency, the inscribed circle distribution Ha (ma) indicates an area-weighted inscribed circle distribution. It is possible to simply add 1 instead of the area, but in that case, the inscribed circle distribution does not take the area of the polygon into account, and the polygon configuration is rough and fine for the same polygon model. A noticeable difference occurs in the inscribed circle distribution. Therefore, in this embodiment, the area of the polygon is added when calculating the inscribed circle distribution. When only 1 is added instead of the area, the inscribed circle distribution is calculated by counting the number Ha (ma) corresponding to a certain element ma.

  If the circumscribed circle distribution and the inscribed circle distribution are calculated, it is next determined whether or not the loop counter LC is equal to or greater than a specified value (step S510). As a result of the determination, if the loop counter LC is less than the specified value, a process of shifting the arrangement of the two groups by 1 is performed (step S511). For example, in the case where the order of the first group is fixed, in the present embodiment, the random number array R2 (k) used for the two groups of the second and third groups is executed by executing the processing according to the following [Equation 11]. ), The order of the random number array R3 (k) is shifted.

[Formula 11]
Ro = R2 (0), Ro3 = R3 (0),
For k = 0, ..., N / 3-2,
R2 (k) ← R2 (k + 1), R3 (k) ← R3 (k + 1) are repeated,
It is assumed that R2 (N / 3-1) = Ro and R3 (N / 3-1) = Ro3.

  When the order of the second group is fixed or when the order of the third group is fixed, the relationship between the groups is changed and the process according to [Formula 11] is executed. In addition, a process of incrementing the loop counter LC (LC ← LC + 1) is also performed. When there is a frequency distribution that has already been calculated, the frequency value corresponding to each element is added to each element of the frequency distribution. In step S511, after shifting the arrangement of the two groups, the combination of the polygons of the two groups and the other group is changed from the first, and the processing of steps S505 to S509 is executed, and the circumscribed circle distribution and the inner Calculate the tangent circle distribution. The circumscribed circle distribution and the inscribed circle distribution calculation process in steps S505 to S509 is repeated a predetermined number of times set as the specified value. The specified value can be set as appropriate, but is about 10 when the total number of polygons N = 10000.

  That is, every time the circumscribed circle distribution and the inscribed circle distribution are calculated once, the process of calculating the circumscribed circle distribution and the inscribed circle distribution again by changing the order of the polygons in two of the three groups is performed. Repeatedly. When the loop counter LC reaches the specified value, it is determined in step S510 that the loop counter LC is equal to or greater than the specified value, so that the two frequency distributions are normalized (step S512).

  Specifically, the unit is normalized to the area ratio [%] by multiplying Hd (md) calculated by the above [Equation 9] by 100 / {Ssum × LC} and replacing it with Hd (md). ing. That is, the value Hd (md) of each element is divided by the multiplication value of the total area value Ssum of the polygon area and the loop count LC. Thereby, even polygon models having different total area values of polygons, that is, different scales, are converted into circumscribed circle distributions of the same scale, and can be determined as polygon models based on the same scale. In addition, since it is divided by the loop number LC, an average value for the loop number is obtained.

  Further, the unit is normalized to the area ratio [%] by multiplying Ha (ma) calculated by the above [Equation 10] by 100 / {Ssum × LC} and replacing it with Ha (ma). That is, the value Ha (ma) of each element is divided by the multiplication value of the total area value Ssum of the polygon area and the loop count LC. Thereby, even polygon models having different total area values of polygons, that is, different scales, are converted to inscribed circle distributions of the same scale, and can be determined as polygon models based on the same scale. In addition, since it is divided by the loop number LC, an average value for the loop number is obtained.

  In this embodiment, when calculating the radius of the circumscribed circle of the triangle and the radius of the inscribed circle of the triangle, a triangle having three vertexes as the average point of the polygon is created. The vertices of the triangles are preferably the average points of the vertices of the polygons, but can also be triangles connecting predetermined points on the polygons other than the average points. The predetermined points on the polygon include, for example, any vertex constituting the polygon, the center of gravity of the polygon, a point having a value that is exactly halfway between the maximum value and the minimum value of each vertex of the polygon, and the like. Can be used.

<2.3.2. Second method>
Next, the second method will be described. FIG. 5 is a flowchart showing details of the frequency distribution calculation processing by the second method. The same processes as those in the first method are denoted by the same reference numerals and description thereof is omitted. Also in the second method, steps S501 to S509 are performed in the same manner as in the first method. Then, as in step S512 of the first method, normalization of the two types of frequency distribution is performed (step S512). In the second method, normalization is forcibly performed after the calculation of the two types of frequency distributions. Specifically, the unit is normalized to the area ratio [%] by multiplying Hd (md) calculated by the above [Equation 9] by 100 / Ssum and replacing it with Hd (md). Further, the unit is normalized to the area ratio [%] by multiplying Ha (ma) calculated by the above [Equation 10] by 100 / Ssum and replacing it with Ha (ma). Unlike the first method, division by the loop counter LC is not performed.

  If normalization of the circumscribed circle distribution and the inscribed circle distribution is performed, it is next determined whether or not the loop counter LC is 1 or more (step S521). This determines whether or not the frequency distribution is calculated for the first time. When the loop counter LC is less than 1, it means that the frequency distribution is calculated for the first time, so that the process of shifting the arrangement of the two groups by 1 is performed (step S511). Specifically, similarly to step S511 of the first method, by executing the processing according to [Formula 11], two of the arrays R1 (k), R2 (k), and R3 (k) The order of the array is shifted.

  In step S511, after shifting the arrangement of the two groups, the combination of the polygons of the two groups and the other group is changed from the first time, and the processes of steps S505 to S509, S512, and S521 are executed. Normalize circular distribution and inscribed circle distribution. That is, every time the circumscribed circle distribution and the inscribed circle distribution are calculated once, the process of calculating the circumscribed circle distribution and the inscribed circle distribution again by changing the order of the polygons in two of the three groups is performed. Repeat. In the second and subsequent times, in step S521, since the loop counter LC is determined to be 1 or more, the immediately preceding circumscribed circle distribution and inscribed circle distribution are compared (step S522). First, the processing according to the following [Equation 12] is executed to calculate the Euclidean distance DD between the calculated circumscribed circle distribution and the immediately preceding circumscribed circle distribution. The Euclidean distance is a distance in the Euclidean space given as a value obtained by squaring the absolute value of the difference between the frequencies of each element. The Euclidean distance DD is obtained as a distance between two points in the MD dimensional Euclidean space.

[Formula 12]
DD = Σ _{md = 0, MD-1} [{Hd (md) −Hdp (md)} ² / MD] ^1/2

In the above [Equation 12], the subscript “md = 0, MD-1” of Σ is the following [...] ^1/2 when md takes all integers from 0 to MD-1. The sum is calculated. Hdp (md) represents the circumscribed circle distribution just before.

  Subsequently, by executing processing according to the following [Equation 13], the Euclidean distance DA between the calculated inscribed circle distribution and the immediately preceding inscribed circle distribution is calculated. The Euclidean distance DA is obtained as a distance between two points in the MA dimensional Euclidean space.

[Formula 13]
DA = Σ _{ma = 0, MA-1} [{Ha (ma) −Hap (ma)} ² / MA] ^1/2

In the above [Formula 13], the subscript “ma = 0, MA-1” of Σ is the following [...] ^1/2 when ma takes all integers from 0 to MA-1. The sum is calculated. Hap (ma) indicates the immediately preceding inscribed circle distribution.

  Next, the frequency distribution calculating means 10 compares the circumscribed circle distribution calculated in step S522, the Euclidean distance of the inscribed circle distribution, and the respective determination threshold values (step S523). Specifically, when both of the Euclidean distances DD and DA are smaller than the determination threshold value, the saturated state is determined. When either one of the Euclidean distances DD and DA is equal to or greater than the determination threshold value, the generation is in progress. judge. The determination threshold can be arbitrarily set, but here it is set to 0.05.

  When it is determined in step S523 that the state is saturated, that is, when similarity is recognized between the two, the frequency distribution calculating unit 10 officially converts the circumscribed circle distribution and the inscribed circle distribution calculated last into two types. The frequency distribution is determined (step S524). That is, the circumscribed circle distribution and the inscribed circle distribution immediately after the calculation are compared with the circumscribed circle distribution and the inscribed circle distribution obtained immediately before the circumscribed circle immediately after the calculation. The distribution and the inscribed circle distribution are set as a circumscribed circle distribution and an inscribed circle distribution to be verified.

  If it is determined in step S523 that the group is in the process of generation, a process for shifting the arrangement of the two groups by 1 is performed (step S511), and then the processes in steps S505 to S509, S512, S521, and S522 are repeatedly executed.

  FIG. 6 shows circumscribed circles and inscribed circles in the circumscribed circle distribution and inscribed circle distribution obtained from the target model. FIG. 6 shows a polygon model when the polygon is triangular. For the three polygons selected from the polygon model, a triangle is created by connecting the average points having the average coordinates of the vertices of each polygon. The polygon model and the triangle in FIG. 6A and FIG. 6B are the same. Then, a circumscribed circle and an inscribed circle of a triangle are obtained. FIG. 6A shows the relationship between the triangle, its circumscribed circle, and the radius, and FIG. 6B shows the relationship between the triangle, its inscribed circle, and the radius. 6A and 6B, downward arrows indicate the radius of the circumscribed circle and the radius of the inscribed circle, respectively.

In general, when the polygon model has a shape close to a sphere, the circumscribed circle of a triangle connecting the centers of three randomly selected polygons is a circle when the sphere is cut by a plane formed by the three centers. Become. That is, in the case of a sphere, the circumscribed circle radius often coincides with the radius of the sphere, and in the circumscribed circle distribution, a peak appears at one location corresponding to the radius of the sphere. However, as the polygon density gets closer to the polyhedron, the circumscribed circle distribution spreads toward the shorter circumscribed circle radius, and the circumscribed circle distribution becomes more complex as the polygon model deviates from the sphere. Therefore, the circumscribed circle distribution using the circumscribed circle radius is extremely effective for identifying the 3D shape. On the other hand, the inscribed circle radius has various values even when the polygon model is a sphere, and the inscribed circle distribution has a wide spread form. Therefore, the inscribed circle radius is more robust to modification of the 3D shape than the circumscribed circle distribution. On the other hand, shape discrimination is weak. Therefore, in the present invention, the scale of the radius of the horizontal axis of the circumscribed circle distribution and the inscribed circle distribution is maintained at a predetermined ratio. As a result, the inscribed circle distribution is locally biased in the direction of radius 0, and the distinguishability of the inscribed circle distribution is improved.

<2.4. Frequency distribution matching process>
Next, the frequency distribution matching process in step S200 will be described. FIG. 7 is a flowchart showing the frequency distribution matching process. First, the frequency distribution matching unit 20 extracts the frequency distribution registered in the record re (re = 0,..., RE−1) from the RE records stored in the restriction model database 40. (Step S610). As the frequency distribution, circumscribed circle distribution Hdo (re, md) (md = 0,..., MD-1), inscribed circle distribution Hao (re, ma) (ma = 0,..., MA-1). To extract.

  Next, the frequency distribution matching unit 20 calculates a correlation coefficient between each element between the circumscribed circle distribution calculated from the target model and the circumscribed circle distributions of the restriction model extracted from the restriction model database 40 (step S620). ). The correlation coefficient is an index indicating the strength of the relationship between two array elements. Here, it is used to determine the strength of similarity between the target model and the regulation model. First, by executing the processing according to the following [Formula 14], the average value Ad of the circumscribed circle distribution Hd (md) calculated from the target model, the circumscribed circle distribution of the regulated model extracted from the regulated model database 40 An average value Ado (re) of Hdo (re, md) is calculated.

[Formula 14]
Ad = Σ _{md = 0, MD-1} Hd (md) / MD
Ado (re) = Σ _{md = 0, MD-1} Hdo (re, md) / MD

  Subsequently, by executing processing according to the following [Equation 15], the standard deviation Sd of the circumscribed circle distribution Hd (md) calculated from the target model, the circumscribed circle of the restriction model extracted from the restriction model database 40 A standard deviation Sdo (re) of the distribution Hdo (re, md) is calculated.

[Formula 15]
Sd = [Σ _{md = 0, MD-1} (Hd (md) −Ad) ² ] ^1/2
Sdo (re) = [Σ _{md = 0, MD-1} (Hdo (re, md) −Ado (re)) ² ] ^1/2

  Strictly speaking, the standard deviation needs to be further divided by a constant MD within the brackets [] in [Formula 15]. However, since the purpose here is to calculate the correlation coefficient, it is necessary to divide by the constant MD in the term for calculating the covariance of the numerator in [Equation 16] to be described later. The operation of dividing by the constant MD is canceled (the denominator is divided twice by the square root of the constant MD).

  Next, by using the calculated average value and standard deviation, the process according to the following [Equation 16] is executed, whereby the circumscribed circle distribution of the target model and the circumscribed circle distribution of the restriction model corresponding to the record re A correlation coefficient Dd (re) is calculated.

[Formula 16]
Dd (re) = [Σ _{md = 0, MD-1} (Hd (md) −Ad) · (Hdo (re, md) −Ado (re))] / (Sdo (re) · Sd)

  In the above [Equation 16], the subscript “md = 0, MD-1” of Σ indicates that the sum is obtained when md takes all integers from 0 to MD-1. The correlation coefficient Dd (re) is obtained by dividing the covariance of the frequency distributions Hd (md) and Hdo (re, md) by their standard deviations. As in the calculation of [Formula 16], performing the division of the square root of the denominator twice generally has a high processing load. For this reason, in the field of image processing, what is required only by the product-sum operation of numerators is called “correlation coefficient”, and Dd (re) shown in the above [Equation 16] is called “normalized correlation coefficient”. There are also customs.

  Next, the frequency distribution matching unit 20 compares the correlation coefficient Dd (re) of the circumscribed circle distribution calculated in step S620 with the determination threshold value (step S630). When the correlation coefficient Dd (re) is greater than or equal to the determination threshold value, it is determined as conforming, and when the correlation coefficient Dd (re) is smaller than the determination threshold value, it is determined as non-conforming. The determination threshold value can be arbitrarily set as long as it is a positive value, but here, when the correlation coefficient value taking a range of +5.0 (−1 to +1) is multiplied by 100 and expressed in% dimension ).

  If it is determined in step S630 that the data is suitable, the frequency distribution matching unit 20 uses the inscribed circle distribution calculated from the target model and the inscribed circle distributions of the restriction model extracted from the restriction model database 40, respectively. A correlation coefficient between elements is calculated (step S640). First, by executing the processing according to the following [Equation 17], the average value Aa of the inscribed circle distribution Ha (ma) calculated from the target model, the inscribed of the restriction model extracted from the restriction model database 40 An average value Aao (re) of the circle distribution Hao (re, ma) is calculated.

[Formula 17]
Aa = Σ _{ma = 0, MA-1} Ha (ma) / MA
Aao (re) = Σ _{ma = 0, MA-1} Hao (re, ma) / MA

  Subsequently, by executing the processing according to the following [Equation 18], the standard deviation Sa of the inscribed circle distribution Ha (ma) calculated from the target model, the restriction model extracted from the restriction model database 40, A standard deviation Sao (re) of the tangent circle distribution Hao (re, ma) is calculated.

[Formula 18]
Sa = [ _{Σma = 0, MA-1} (Ha (ma) −Aa) ² ] ^1/2
Sao (re) = [Σma _{= 0, MA-1} (Hao (re, ma) −Aao (re)) ² ] ^1/2

  Strictly speaking, the standard deviation needs to be further divided by a constant MA within [] in [Formula 18]. However, since the purpose here is to calculate the correlation coefficient, it is necessary to divide by the constant MA in the term for calculating the covariance of the numerator in [Equation 19], which will be described later. The operation of dividing by the constant MA is canceled (the denominator is divided by the square root of the constant MA twice).

  Next, by using the calculated average value and standard deviation, the process according to the following [Equation 19] is executed, whereby the inscribed circle distribution of the target model and the inscribed circle of the restriction model corresponding to the record re A correlation coefficient Da (re) of the distribution is calculated.

[Formula 19]
Da (re) = [Σma _{= 0, MA-1} (Ha (ma) -Aa). (Hao (re, ma) -Aao (re))] / (Sao (re) .Sa)

  In the above [Equation 19], the subscript “ma = 0, MA−1” of Σ indicates that the sum is obtained when ma takes all integers from 0 to MA−1. The correlation coefficient Da (re) is obtained by dividing the covariance of the frequency distributions Ha (ma) and Hao (re, ma) by the respective standard deviations. In general, the processing load is high when dividing the square root of the denominator twice as in the calculation of [Formula 19]. For this reason, in the field of image processing, what is required only by the product-sum operation of numerators is called “correlation coefficient”, and Da (re) shown in the above [Equation 19] is called “normalized correlation coefficient”. There are also customs.

  Next, the frequency distribution matching unit 20 compares the correlation coefficient Da (re) of the inscribed circle distribution calculated in step S640 with the determination threshold value (step S650). If the correlation coefficient Da (re) is greater than or equal to the determination threshold value, it is determined as conforming, and if the correlation coefficient Da (re) is smaller than the determination threshold value, it is determined as non-conforming. The determination threshold value can be arbitrarily set as long as it is a positive value, but here, when the correlation coefficient value taking a range of +5.0 (−1 to +1) is multiplied by 100 and expressed in% dimension ). If it is determined to be suitable in step S650, the frequency distribution matching unit 20 determines that the output should be restricted (output inappropriate) because the target model and the restriction model of the record re are compatible. Do.

  When it is determined as non-conforming in either step S630 or S650, the frequency distribution matching unit 20 finishes matching the target model with the restriction model of the record re, and proceeds to matching with the restriction model of the next record re + 1. . When the process according to the flowchart of FIG. 7 is executed for all records in the restriction model database 40 and there is at least one applicable restriction model, the output should be restricted to the target model (output inappropriate) ) ", The collation process in step S200 is terminated.

<Data output to 3.3D printer>
In step S200, when the frequency distribution matching unit 20 determines that “the output should not be restricted (output appropriate)”, the target model is output to the 3D printer 7 which is a three-dimensional object formation apparatus. That is, the target model that is a polygon model whose output is permitted by the three-dimensional object modeling data output restriction device is output to the three-dimensional object modeling device that models the three-dimensional object. On the other hand, if the frequency distribution matching means 20 determines that “the output should be regulated (output inappropriate)”, the target model is not output to the 3D printer 7 which is a three-dimensional object forming apparatus. If it takes a long time to determine whether output is appropriate or not, whether the target model is output to the 3D printer 7 and whether the output is appropriate or inappropriate in parallel with the output process (three-dimensional object modeling process) of the 3D printer 7. If the output is unsuitable, an output stop command may be output to the 3D printer 7. At this time, from the user's point of view, it is only possible to confirm that the three-dimensional object shaping process in the 3D printer is started by issuing a single command to output the target model. I do not notice that the execution of the process for is started.

<4. Target model separation processing>
Prior to the frequency distribution calculation process shown in FIG. 3, a target model separation process for separating the target model may be performed. By separating the target model in advance, it is possible to appropriately determine whether the output is appropriate or inappropriate even if the component model is different between the regulated model and the target model. The target model separation process prior to the frequency distribution calculation process will be described below. The target model separation process is performed by a target model separation unit realized by the CPU 1 illustrated in FIG. 1 executing a program stored in the storage device 3.

<4.1. Overview of separation process>
The target model separation means performs a target model separation process for separating the target model into a plurality of partial target models. More specifically, the target model separation means separates a certain polygon and three adjacent polygons sharing the side with the polygon so that they belong to the same partial target model. FIG. 8 is a flowchart showing details of target model separation processing by the target model separation means. First, the target model separation unit reads the polygon model before separation from the target model storage unit 30 and structures the polygon model (step S10). Next, the target model separating means performs structuring of the polygon ridge line (step S20). Next, the target model separation unit creates an adjacent polygon table (step S30). Then, the target model separation means separates the polygon model for which the adjacent polygon table has been created into a plurality of groups (step S40). In addition, <4. Also in the target model separation process>, the term “polygon ID” that identifies a “group” or a polygon appears. However, these are <4. This is effective only within the target model separation processing><4. It is unrelated to the three “groups” that appear in other than the target model separation process> and the “variable i” that identifies the polygon.

  First, a target model, which is a polygon model to be processed, has a three-dimensional value in space and may be composed of tens of thousands of polygons. However, here, for convenience of explanation, a polygon group projected on a two-dimensional space is used. FIG. 9 is a diagram illustrating an example of a polygon group to be processed. In the example of FIG. 9, a polygon group consisting of 18 triangular polygons is shown. In FIG. 9, uppercase alphabets A to N are shown not for belonging to a specific polygon but for identifying a shared vertex shared by a plurality of polygons. Uppercase alphabets A to N are attached for convenience in order to identify shared vertices in the entire polygon group. Vertex coordinate array data, a vertex configuration table, a head vertex table, a vertex coordinate table, and a ridge line configuration to be described later It is not recorded in the table, the head ridge line table, the ridge line table, the polygon table by ridge line ID, and the adjacent polygon table (some parts shown in the drawings are used for convenience in order to facilitate understanding). .

  FIG. 10 is a diagram showing vertex coordinate array data for realizing the polygon group shown in FIG. The vertex coordinate array data is data defining a polygon model, and a polygon model that is an aggregate of polygons is defined by an array structure. That is, FIG. 10 shows a structure in which normal vector information is deleted from the STL data. As shown in FIG. 10, the vertex coordinate array data is associated with polygon IDs P1 to P18 which are polygon identification information for identifying polygons, and the three vertices of the first vertex, the second vertex, and the third vertex constituting the polygon. Each coordinate value is recorded in order.

  9 and 10 show polygon-specific vertex IDs “V1a” to “V18c” in which the polygons and the three vertices constituting each polygon are associated with the polygon IDs P1 to P18 in the order of the vertices. ing. The polygon-specific vertex IDs “V1a” to “V18c” are provided for convenience of explanation, and include vertex coordinate array data, head vertex table, vertex configuration table, vertex coordinate table, edge line configuration table, head edge line table, edge line table, It is not recorded in the polygon table by edge line ID and the adjacent polygon table.

  The vertex ID for each polygon expresses the vertex in a format in which the polygon ID of the polygon to which the vertex belongs and the vertex order in the polygon are specified. Specifically, as shown in FIG. 9, after V indicating a vertex, numbers included in the corresponding polygon ID, and a, b, and c indicating the vertex order of the first vertex, the second vertex, and the third vertex. Express with one of the lowercase alphabets. For example, as shown in the first line of FIG. 10, the first vertex of the polygon (polygon P1) with the polygon ID “P1” is expressed as “V1a”. Since the number “1” is entered in the vertex order “V1a”, it is clear that it is the vertex of the polygon P1. This vertex ID for each polygon is actually expressed by a serial number, and the coordinate value of each vertex is recorded in the vertex coordinate array data according to the serial number, but the correspondence relationship with FIG. 9 and which polygon the vertex belongs to In order to clarify whether this is done, a symbol is added to the vertex ID for each polygon for convenience and is shown in the drawing. Further, in the vertex coordinate array data of FIG. 10, since the coordinates of each vertex correspond to any of the shared vertices A to N shown in FIG. 9, the shared vertex A to N corresponding to the coordinate value of each vertex is identified. It is expressed with the lowercase letters of the alphabet. For example, as shown in the first line of FIG. 10, the coordinate value of the first vertex V1a of the polygon P1 is expressed as “(Xd, Yd, Zd)” because “D” is displayed in FIG. The For each polygon, the coordinate values of the three vertices are recorded in association with the three vertices configured.

  Since it is created according to the rules as described above, in the vertex coordinate array data, vertices with the same coordinates are recorded redundantly at a plurality of locations. For example, since the vertex D shown in FIG. 9 is shared by six polygons P1, P2, P4, P5, P6, and P7, the coordinate values “(Xd, Yd, Zd)” are the vertexes V1a, V2a, V4b. , V5c, V6c, V7c are recorded at six locations.

<4.2. Structure of polygon model>
The structuring of the polygon model in step S10 will be described. Polygon model structuring refers to the vertex relationship (topology / phase information in the polygon model) by converting the vertex coordinate array data, which is the data that realizes the polygon model, into a structure consisting of a vertex configuration table and a vertex coordinate table. ) Is separated from the coordinate value (numerical data), and it becomes easy to search for the vertices of the surrounding polygons affected by the correction of the vertices of a certain polygon, and it is rearranged into a structure easy to edit.

  FIG. 11 is a flowchart showing details of the structuring of the polygon model in step S10. The structuring of the polygon model in step S10 is performed by the target model separation unit. First, the vertex configuration table, vertex coordinate table, and vertex ID are initialized (S101). The vertex configuration table expresses each vertex included in each polygon by a vertex ID having the same value as the shared vertex of the same coordinate, and expresses each vertex constituting the polygon by the vertex ID of the shared vertex. It is the table which matched and recorded by ID and vertex order. The vertex ID is vertex identification information for identifying the shared vertex, and any vertex can be used as long as the shared vertex can be uniquely specified. In this example, a serial integer value starting from 0 is used. The vertex coordinate table is a table in which the vertex ID and the coordinate value recorded in the vertex configuration table are recorded in association with each other. Initialization of the vertex configuration table and the vertex coordinate table means that each item is not recorded. Also, the current vertex ID = 0 is set as the initialization of the vertex ID. Furthermore, the top vertex table in which the hash value and the link destination vertex ID are associated is also initialized. The initial vertex table is initialized by setting all link destination vertex IDs to “−1”. A vertex coordinate table by hash value is realized by two tables, a vertex coordinate table and a head vertex table.

  Next, in extracting the vertex coordinate value from the vertex coordinate array data, the polygon ID is initialized to “P1” and the vertex order is initialized to “0” (S101). Then, one vertex coordinate value corresponding to the current polygon ID and the vertex order is extracted from the vertex coordinate array data (S102). For example, the polygon ID “P1” and the vertex coordinate values (Xd, Yd, Zd) of the first vertex are extracted from the vertex coordinate array data shown in FIG.

  Subsequently, a hash value is calculated from the extracted vertex coordinate values (Xd, Yd, Zd) (S103). Specifically, first, the maximum values Xmax, Ymax, Zmax and the minimum values Xmin, Ymin, Zmin of the three-dimensional coordinate values x, y, z of all the polygons in the polygon model are obtained. This is to obtain the maximum value and the minimum value for each coordinate of the vertex coordinate array data as shown in FIG. Then, the coordinate values x, y, and z are classified into equal T stages, and converted into integer values hx, hy, hz (0 ≦ hx, hy, hz ≦ T−1). As the integer T, for example, T = 32 can be set. Specifically, hx, hy, hz are calculated by executing processing according to the following [Equation 20]. However, after the processing according to [Equation 20], values obtained by rounding down the decimals are obtained as integer values hx, hy, hz.

[Formula 20]
hx = (x−Xmin) · T / (Xmax−Xmin)
hy = (y−Ymin) · T / (Ymax−Ymin)
hz = (z−Zmin) · T / (Zmax−Zmin)

  And the hash value h is calculated by performing the process according to the following [Formula 21].

[Formula 21]
h = hx + hy · T + hz · T ² (0 ≦ h ≦ T ³ −1)

  Next, the head vertex table is referred to using the calculated hash value (S104). Then, it is determined whether or not the link destination vertex ID corresponding to the hash value is registered in the head vertex table (S105). When the link destination vertex ID corresponding to the hash value (indicated as “link vertex ID” in the figure) is “−1”, that is, in the initial state, the link destination vertex ID corresponding to the hash value is not registered, that is, already exists. Means not registered.

  If the link destination vertex ID is not registered, the vertex ID is recorded in the head vertex table (S106). Specifically, the current vertex ID (“0” in the initial state) is written in the link destination vertex ID corresponding to the hash value of the head vertex table. Subsequently, the coordinate value extracted in step S102 is written in the position of the current vertex ID in the vertex coordinate table (S107). At this time, the vertex coordinate table also has a column for writing the link destination vertex ID, but the initial state is “−1”. Further, the current vertex ID is written in the vertex configuration table in association with the polygon ID extracted in step S102 and the position in the vertex order. Here, it is recorded as a new vertex ID. Then, the current vertex ID is incremented.

  On the other hand, if the link destination vertex ID corresponding to the hash value is not “−1” in step S105, it means that the link destination vertex ID corresponding to the hash value is already registered. If the link destination vertex ID is already registered, it is determined whether the coordinate value extracted in step S102 matches the coordinate value corresponding to the registered link destination vertex ID in the vertex coordinate table (S108). If the two coordinate values match as a result of the determination, the registered link destination vertex ID is recorded at the position of the polygon ID and the vertex order in the vertex configuration table (S109). If the two coordinate values are different as a result of the determination in step S108, the link destination vertex ID in the vertex coordinate table is referred to and it is confirmed whether the link destination vertex ID is 0 or more (S110).

  As a result of the confirmation, if the link destination vertex ID is 0 or more, the value is set as the vertex ID, the process returns to step S108, and the already registered coordinate value in the vertex coordinate table corresponding to the vertex ID is extracted in step S102. Match coordinate values. As a result of the confirmation in step S110, if the link destination vertex ID is a negative value (eg, “−1”), the current vertex ID is recorded in the link destination vertex ID corresponding to the vertex ID in the vertex coordinate table, and step Returning to S107, the coordinate value extracted in step S102 is recorded at the position of the current vertex ID in the vertex coordinate table. At this time, the link destination vertex ID at the current vertex ID in the vertex coordinate table is left as “−1” in the initial state. Also, the current vertex ID is recorded in the vertex configuration table at the position of the polygon ID extracted in step S102 and the vertex order. Then, the vertex ID is incremented.

  When the process of step S107 or S109 is completed, it is determined whether or not the process has been completed for all the polygons (S111). Specifically, first, the vertex order is incremented. When the vertex order exceeds “2”, the polygon ID is incremented to set the vertex order to “0”. For example, if the current polygon ID is “P1” and the polygon ID is incremented, the polygon ID becomes “P2”. If the polygon ID does not exceed the total number of polygons, the process returns to step S102 and is repeated. If the polygon ID exceeds the total number of polygons, it is determined that the processing has been completed for all the polygons, and the processing ends.

  The target model separation means includes a hash value calculation means, a coordinate value collation means, a vertex configuration table processing means, and a vertex coordinate table processing means, and these means cooperate in the polygon model structuring process shown in FIG. It is realized by doing.

  A specific example of the polygon model structuring process in step S10 shown in FIG. 11 will be described. The case where the polygon group shown in FIG. 9 takes a hash value as shown in FIG. 12 based on the coordinate value of the vertex will be described as an example. In the example of FIG. 12, it is expressed in two dimensions for convenience of explanation, and if T = 2 in the above [Equation 21], h = hx + hy · 2 is obtained, and the hash values of the vertices A, B, D, E are h = 0 , The hash values of vertices C and F are h = 1, the hash values of vertices G, H, J, L, and M are h = 2, and the hash values of vertices I, K, and N are h = 3 Yes. Actually, in the above [Equation 21], T = 32 is set, h = hx + hy · 32 + hz · 1024, and the hash value h takes a value of 0 to 32767, but in the following, in order to simplify the explanation, The hash value will be described assuming that it takes the values 0 to 3 described above.

  FIG. 13 is a diagram showing the state of each table at the time of initialization in S101. FIG. 13A shows a vertex configuration table, FIG. 13B shows a top vertex table, and FIG. 13C shows a vertex coordinate table. In the head vertex table, a vertex ID is associated with a hash value and recorded as a link destination vertex ID. Here, since the hash value takes a value of 0 to 3 as shown in FIG. 12, “−1” is recorded as the link destination vertex ID in association with all the hash values 0 to 3 in the head vertex table. Has been. In the vertex coordinate table, vertex coordinates are recorded in association with vertex IDs, and link destination vertex IDs are recorded.

  As shown in FIG. 13A, in the vertex configuration table, three vertex IDs are recorded in association with the polygon ID and the vertex order. The vertex order is 0, 1, 2 from the left. These correspond to the first vertex, the second vertex, and the third vertex shown in FIG. At the time of initialization in step S101, the vertex order is initialized to zero. It is assumed that the coordinate value (Xd, Yd, Zd) of the first vertex of the polygon P1 is extracted from the vertex coordinate array data in step S102, and the hash value “0” is calculated in step S103. If the head vertex table is referenced with the hash value “0” in step S104, since the link destination vertex ID is not registered in step S105, the hash value “0” in the head vertex table is associated with the hash value “0” in step S106. The current vertex ID “0” is recorded as the link destination.

  In step S107, the coordinate value (Xd, Yd, Zd) extracted in step S102 is recorded at the position of the current vertex ID “0” in the vertex coordinate table, and “−1” is recorded as the link destination vertex ID. To do. In step S107, the current vertex ID “0” is recorded at the position of the current polygon ID “P1” in the current vertex order “0”. As a result, each table is in a state as shown in FIG. Then, the vertex ID is incremented to change the current vertex ID from “0” to “1”. Also, the vertex order is incremented to change the current vertex order from “0” to “1”. Since the current polygon ID is “P1” and does not exceed “P18”, it is determined in step S111 that processing has not been completed for all polygons, and the process returns to step S102.

  In step S102, the coordinate value (Xb, Yb, Zb) of the second vertex of the polygon P1 is extracted from the vertex coordinate array data based on the current polygon ID “P1” and the current vertex order “1”. In step S103, the hash value “0” is calculated for the coordinate values (Xb, Yb, Zb). If the head vertex table is referenced with the hash value “0” in step S104, “0” is already registered as the link destination vertex ID in step S105. Therefore, in step S108, the registered vertex ID “0” is set. The coordinate values (Xd, Yd, Zd) in the corresponding vertex coordinate table are collated with the coordinate values (Xb, Yb, Zb) extracted in step S102. As a result of the collation, there is a mismatch, so in step S110, it is confirmed whether the vertex ID of the link destination in the vertex coordinate table is 0 or more or a negative value.

  As shown in FIG. 14, since the vertex ID of the link destination is “−1”, the coordinate value (Xb, X) extracted in step S102 is added to the current vertex ID “1” in the vertex coordinate table in step S107. Yb, Zb) is recorded, and “−1” is recorded as the link destination vertex ID. Further, in step S107, the current vertex ID “1” is recorded at the position of the current polygon ID “P1” in the current vertex order “1” in the vertex configuration table. As a result, each table is in a state as shown in FIG. Then, the vertex ID is incremented to change the current vertex ID from “1” to “2”. Also, the vertex order is incremented to change the current vertex order from “1” to “2”. Since the current polygon ID is “P1” and does not exceed “P18”, it is determined in step S111 that processing has not been completed for all polygons, and the process returns to step S102.

  The polygon ID “P1”, the vertex ID “2”, and the vertex order “2” are processed in the same manner as the polygon ID “P1”, the vertex ID “1”, and the vertex order “1”. The state is as shown in FIG. Then, the vertex ID is incremented to change the current vertex ID from “2” to “3”. Since the vertex order takes only three values “0”, “1”, and “2” for one polygon, the polygon ID is incremented to “P1” → “P2”, and the vertex order is reset to the current The vertex order of “2” → “0”. Since the current polygon ID is “P2” and does not exceed “P18”, it is determined in step S111 that processing has not been completed for all polygons, and the process returns to step S102.

  In step S102, the coordinate value (Xd, Yd, Zd) of the first vertex of the polygon P2 is extracted from the vertex coordinate array data based on the current polygon ID “P2” and the current vertex order “0”. In step S103, the hash value “0” is calculated for the coordinate values (Xd, Yd, Zd). If the head vertex table is referenced with the hash value “0” in step S104, “0” is already registered as the link destination vertex ID in step S105. Therefore, in step S108, the registered vertex ID “0” is set. The coordinate values (Xd, Yd, Zd) in the corresponding vertex coordinate table are collated with the coordinate values (Xd, Yd, Zd) extracted in step S102. As a result of the collation, in step S109, the vertex ID “0” having the same coordinate value in the vertex coordinate table is set as the current vertex order “0” position of the current polygon ID “P2” in the vertex configuration table. To record. As a result, each table is in a state as shown in FIG.

  As described above, the process is performed up to the polygon P18, and the process is terminated by the determination in step S111. At this time, each table is in a state as shown in FIG. That is, a single vertex ID in which other coordinate values having the same hash value are recorded closest to the vertex ID is associated as a link destination. As described above, in the polygon model structuring process, coordinate values are collated in step S108, and if they do not match, they are registered in the vertex coordinate table, but if they match, the collation is repeated one after another. Go. In this process, the load increases as the vertex coordinate table increases and the number of registered coordinate values increases. In this embodiment, it is obvious that the hash value does not match different coordinate values without referring to all the coordinate values in the vertex coordinate table by referring to the hash value-specific vertex coordinate table. Therefore, it can be excluded from the verification target, and only the vertex coordinate table having the same hash value needs to be referred to. In the present embodiment, the coordinate value having the same hash value in the vertex coordinate table is at most about ¼, but as described above, in many cases, in the above [Equation 21], T = 32 is often set. Then, the coordinate value having the same hash value in the vertex coordinate table is reduced to about 1/32767, and the verification time is also reduced to about 1/32767 compared with the case of matching with all cases. For this reason, the structuring process of the polygon model can be performed at an extremely high speed.

  In the example of FIG. 18, when the hash value is “0”, only the vertex IDs “0”, “1”, “2”, and “6” are collated according to the link destination vertex ID in the vertex coordinate table of FIG. Then, the vertex coordinates can be registered. By deleting the head vertex table shown in FIG. 18B and deleting the link destination vertex ID column from the vertex coordinate table shown in FIG. 18C, the vertex configuration table as shown in FIG. A vertex coordinate table can be obtained and the structuring of the polygon model is completed.

<4.3. Polygon ridge structure>
The structuring of the polygon ridge line in step S20 will be described. Polygon ridge line structuring means that a ridge line ID is assigned to a vertex ID pair of two vertices adjacent in the order of vertices in a given polygon, and each ridge line constituting the polygon is expressed by a ridge line ID. And the ridge line configuration table recorded in association with the ridge line order, and the ridge line table in which the vertex ID pairs corresponding to the ridge line IDs assigned to the ridge line configuration table are recorded in association with each other. Here, the ridge line order corresponds to the vertex order. For example, two adjacent vertices “0” and “1” in the vertex order correspond to the ridge line order “0”, and “1” and “2” in the vertex order. Two adjacent vertices correspond to the ridge line order “1”, and two adjacent vertices “2” and “0” in the vertex order correspond to the ridge line order “2”. At this time, it is important that the same vertex ID pair is not recorded repeatedly on a plurality of ridge line IDs in the ridge line table. Since the ridge lines of adjacent polygons sharing the same vertex ID pair are in principle reverse in the order of the vertex ID pairs, the vertex ID pairs are set in ascending order (numbered). Then, when assigning a ridge line ID to each vertex ID pair, it is searched whether or not it is already registered in the ridge line table. If it is already registered, the corresponding ridge line ID is assigned, and if not registered, a new ridge line ID is assigned. Is added, and the process of additionally registering in the ridge line table is repeated.

  FIG. 20 is a flowchart showing details of structuring of polygon ridge lines in step S20. In step S20, the polygon ridge line is structured by the target model separation unit. First, the ridge line configuration table, the ridge line table by vertex ID, and the ridge line ID are initialized (S201). The ridge line configuration table is a table in which each ridge line included in each polygon is expressed by a ridge line ID and associated with the polygon and the ridge line order. The ridge line ID is ridge line identification information for identifying the ridge line, and any ridge line ID may be used as long as it can uniquely identify the ridge line. A serial integer value starting from 0 is used. The ridge line table is a table in which ridge line IDs and vertex ID pairs assigned to the ridge line configuration table are recorded in association with each other. Initialization of the ridge line configuration table and the ridge line table means that each item is not recorded. Also, the current ridge line ID = 0 is set as the initialization of the ridge line ID. Furthermore, the head edge line table in which the vertex ID of the smaller vertex ID pair is associated with the link destination edge line ID is also initialized. Initialization of the head ridge line table is performed by setting all link destination ridge line IDs to “−1”. A vertex line-specific ridge line table is realized by two tables, a ridge line table and a head ridge line table.

  Next, in extracting the vertex ID pair from the vertex configuration table, the polygon ID is initialized to “P1” and the vertex order is initialized to “0” (S201). Then, two vertex ID pairs consisting of the vertex ID corresponding to the current polygon ID and the vertex order and the adjacent vertex ID corresponding to the next vertex order are extracted from the vertex configuration table (S202). The next vertex order is obtained by adding 1 to the current vertex order. When the vertex order is “2”, the next vertex order is “0”. For example, a pair of polygon ID “P1” and vertex IDs “0” and “1” is extracted from the vertex configuration table shown in FIG.

  Next, the leading edge line table is referenced using the smaller vertex ID of the extracted vertex ID pairs (S204). Then, it is confirmed whether or not the link destination ridge line ID corresponding to the vertex ID is registered in the head ridge line table (S205). When the link destination ridge line ID (indicated as “link ridge line ID” in the figure) corresponding to the vertex ID is “−1”, that is, in the initial state, the link destination ridge line ID corresponding to the vertex ID is not registered, that is, already exists. Means not registered.

  When the link destination ridge line ID is not registered, the ridge line ID is recorded in the head ridge line table (S206). Specifically, the current ridge line ID (“0” in the initial state) is written in the link destination ridge line ID corresponding to the vertex ID of the head ridge line table. Subsequently, the vertex ID pair extracted in step S202 is written at the position of the current ridge line ID in the ridge line table (S207). At this time, there is a column for writing the link destination ridge line ID in the ridge line table, but the initial state is “−1”. In addition, the current ridge line ID is written in the ridge line configuration table at the position in the ridge line order which is the same as the vertex order and the polygon ID extracted in step S202. Here, it is recorded as a new ridge line ID. Then, the current ridge line ID is incremented.

  On the other hand, if the link destination ridge line ID corresponding to the vertex ID is not “−1” in step S205, it means that the link destination ridge line ID corresponding to the vertex ID is already registered. In this case, it is determined whether the vertex ID pair extracted in step S202 matches the vertex ID pair corresponding to the registered link destination ridge line ID in the vertex coordinate table (S208). As a result of the determination, if both vertex ID pairs match, the current ridge line ID is recorded at the position of the ridge line order equivalent to the polygon ID and vertex order of the ridge line configuration table (S209). If the two coordinate values are different as a result of the determination in step S208, the link destination ridge line ID in the ridge line table is referred to and it is confirmed whether the link destination ridge line ID is 0 or more (S210). As a result of the confirmation, if the link destination ridge line ID is 0 or more, the value is set as the ridge line ID, the process returns to step S208, and the vertex ID pair corresponding to the ridge line ID is compared with the vertex ID pair extracted in step S202. . As a result of the confirmation in step S210, if the link destination ridge line ID is a negative value (eg, “−1”), the current ridge line ID is recorded in the link destination ridge line ID corresponding to the ridge line ID in the ridge line table, and step S207. The vertex ID pair extracted in step S202 is recorded at the position of the current ridge line ID in the ridge line table. At this time, the link destination ridge line ID in the current ridge line ID of the ridge line table is left as “−1” in the initial state. Further, the current ridge line ID is recorded in the ridge line configuration table in the position of the ridge line order that is the same as the vertex order and the polygon ID extracted in step S202. Then, the ridge line ID is incremented.

  When the process of step S207 or S209 is completed, it is determined whether or not the process has been completed for all the polygons (S211). Specifically, first, the vertex order is incremented. When the vertex order exceeds “2”, the polygon ID is incremented to set the vertex order to “0”. If the polygon ID does not exceed the total number of polygons, the process returns to step S202 and the process is repeated. If the polygon ID exceeds the total number of polygons, it is determined that the processing has been completed for all the polygons, and the processing ends.

  The target model separating unit includes a vertex ID collating unit, a ridge line configuration table updating unit, and a ridge line table updating unit. The polygon ridge line structuring process shown in FIG. 20 is realized by cooperation of these units. .

  FIG. 21 is a diagram showing the state of each table at the time when the ridge line configuration of the polygon P1 is recorded. 21A shows a vertex configuration table, FIG. 21B shows a ridge line configuration table, FIG. 21C shows a head ridge line table, and FIG. 21D shows a ridge line table. In the head ridge line table, the ridge line ID is associated with the vertex ID of the smaller vertex ID pair and recorded as the link destination ridge line ID. Here, the vertex ID takes a value of 0 to 13, but since there is no vertex ID pair in which the final vertex ID 13 is the smaller vertex ID, all the vertices except the final vertex ID 13 are included in the leading edge line table. Link destination ridge line IDs are recorded in association with IDs 0-12. In the initial state, all “−1” are recorded. In the ridge line table, a vertex ID pair (indicated as “vertex pair” in the figure) is recorded in association with the ridge line ID, and a link destination ridge line ID is recorded.

  As shown in FIG. 21B, in the ridge line configuration table, three ridge line IDs are recorded in association with the polygon ID and the ridge line order, but the ridge line order is the same as the vertex order and is 0 from the left. , 1 and 2. At the time of initialization in step S201, the vertex order = ridge line order = 0 is initialized.

  FIG. 22 is a diagram illustrating the state of each table at the time when the ridge line configuration of the polygon P2 is recorded. FIG. 23 is a diagram showing the state of each table at the time when the ridge line configuration of the polygon P3 is recorded. FIG. 24 is a diagram showing the state of each table at the time when the ridge line configuration of the polygon P18 is recorded. By executing the processing according to the flowchart shown in FIG. 20, the processing is performed up to the state shown in FIG.

  In the example of FIG. 24, when the vertex ID of the smaller vertex ID pair is “0”, in the ridge line table of FIG. 24D, the ridge line ID “0” “2” “3” “ If only 8 "" 10 "" 12 "are collated, the vertex ID pair can be registered. The head ridge line table shown in FIG. 24C is deleted, and the column of the link destination ridge line ID is deleted from the ridge line table shown in FIG. A table can be obtained and the structuring of the polygon ridge is completed.

<4.4. Creating an adjacent polygon table>
The creation of the adjacent polygon table in step S30 will be described. To create an adjacent polygon table, search for adjacent polygons that share a ridge line shared with each ridge line (adjacent vertex pair) of the polygon, and describe the vertex coordinate array data in the polygon ID of the adjacent polygon by associating the polygon ID with the ridge line order. This process creates the adjacent polygon table. This search processing has a problem that it takes a processing time in proportion to the square of the number of polygons. In step S20, an adjacent polygon table can be created at high speed by creating an edge line configuration table.

  FIG. 26 is a flowchart showing details of the creation of the adjacent polygon table in step S30. The creation of the adjacent polygon table in step S30 is performed by the target model separation unit. First, the adjacent polygon table, the polygon table by edge line ID, the polygon ID, and the edge line order are initialized (S301). The adjacent polygon table is a table that records each polygon and the polygon ID of the adjacent polygon in association with each other. The polygon table by ridge line ID is a table in which a ridge line ID, a polygon including the ridge line, and a ridge line ID by polygon for identifying the ridge line order of the ridge line in the polygon are associated and recorded. The initialization of the adjacent polygon table means that each item is not recorded. Further, as initialization of the polygon ID and the ridge line order, the current polygon ID is set to “P1”, and the ridge line order is set to “0”. Initialization of the polygon table by edge line ID is performed by setting all polygon edge IDs to “−1”.

  Next, the polygon ID and the ridge line ID in the current ridge line order corresponding to the polygon ID are extracted from the ridge line configuration table (S302). For example, the polygon ID “P1” and the ridge line ID “0” are extracted from the ridge line configuration table shown in FIG.

  Next, the ridge line ID-specific polygon table is referred to using the extracted ridge line ID (S304). Then, it is confirmed whether or not the polygonal ridgeline ID corresponding to the ridgeline ID is registered in the ridgeline ID specific polygon table (S305). When the polygonal ridgeline ID corresponding to the ridgeline ID is “−1”, that is, in the initial state, it means that the polygonal ridgeline ID corresponding to the ridgeline ID is not registered, that is, not registered.

  When the polygonal ridgeline ID is not registered, the current polygonal ridgeline ID is recorded in the polygonal table by ridgeline ID (S306). Specifically, the polygonal ridgeline ID specified by the current polygon ID (“P1” in the initial state) and the ridgeline order (“0” in the initial state) is associated with the ridgeline ID of the polygon table by ridgeline ID. Write.

  On the other hand, if the polygonal ridgeline ID corresponding to the ridgeline ID is not “−1” in step S305, it means that the polygonal ridgeline ID corresponding to the ridgeline ID has already been registered, and the registered polygonal ridgeline ID. This means that the ridge line corresponding to the ID and the ridge line corresponding to the current polygon ID and the ridge line order are shared by the ridge line ID. When the polygonal ridge line ID is already registered, the polygonal ridge line ID corresponding to the ridge line ID is acquired, and the polygon ID specified by the polygonal ridge line ID and the position in the ridge line order in the adjacent polygon table are obtained. The polygon ID is recorded (S307). In step S307, the polygon ID specified by the polygonal ridge line ID is further recorded at the position of the current polygon ID and the current ridge line order in the adjacent polygon table.

  When the process of step S306 or S307 is completed, it is determined whether or not the process has been completed for all the polygons (S308). Specifically, first, the ridge line order is incremented, and when the ridge line order exceeds “2”, the polygon ID is incremented to set the ridge line order to “0”. If the polygon ID does not exceed the total number of polygons, the process returns to step S302 and the process is repeated. If the polygon ID exceeds the total number of polygons, it is determined that the processing has been completed for all the polygons, and the processing ends.

  The target model separation unit includes a polygon table registration unit and an adjacent polygon table registration unit, and the process of creating the adjacent polygon table shown in FIG. 26 is realized by the cooperation of these units.

  FIG. 27 is a diagram showing the state of each table at the time when the polygonal ridge line ID of the polygon P2 is recorded. FIG. 27A shows an edge line configuration table, FIG. 27B shows an edge polygon ID-specific polygon table, and FIG. 27C shows an adjacent polygon table. The ridge line configuration table in FIG. 27A is the same as the ridge line configuration table in FIG. In the polygon table by edge line ID, the edge line ID by polygon is recorded in association with the edge line ID. Here, since the ridge line ID takes a value of 0 to 30, the ridge line ID is recorded in the ridge line ID polygon table in association with all the ridge line IDs 0 to 30. In the polygon table by edge line ID, “−1” is all recorded as the edge line ID by polygon in the initial state. In the blank of FIG. 27B, in reality, “−1” is recorded as the ridge line ID for each polygon which is the initial value. In the adjacent polygon table, the adjacent polygon ID is recorded in association with the polygon ID.

  As shown in FIG. 27 (c), in the adjacent polygon table, the polygon IDs of three adjacent polygons are recorded in association with the polygon IDs, but the ridge line order is 0, 1, 2 from the left. It has become. At the time of initialization in step S301, the edge order is initialized to zero. In the adjacent polygon table, polygon IDs are always recorded so as to be paired. That is, as shown in FIG. 27C, when the polygon P2 is recorded as an adjacent polygon in association with the polygon P1, the polygon P1 is also recorded as an adjacent polygon in association with the polygon P2.

  FIG. 28 is a diagram showing the state of each table at the time when the polygonal ridge line ID of the polygon P4 is recorded. FIG. 29 is a diagram showing the state of each table at the time when the polygonal ridge line ID of the polygon P18 is recorded. By executing the processing according to the flowchart shown in FIG. 26, the processing is performed up to the state shown in FIG. Thereby, the adjacent polygon table as shown in FIG. 29C is completed.

  FIG. 30 is a diagram showing the vertex configuration table completed in step S10, the ridge line configuration table completed in step S20, and the adjacent polygon table completed in step S30.

<4.5. Polygon model separation>
The polygon model separation in step S40 will be described. In the separation of the polygon model, the adjacent polygon table created in step S30 is used. By using the adjacent polygon table, polygon models can be separated at high speed.

FIG. 31 is a flowchart showing details of polygon model separation in step S40. The polygon model separation in step S40 is performed by the target model separation means. First, provisional setting of a group ID is performed (S401). Specifically, a group ID that is group identification information for identifying a group is newly defined, one polygon with no group ID set is extracted, and a new group ID is temporarily set as the polygon ID of the polygon. Here, “the group ID is not set” means a state in which neither the main setting nor the temporary setting is performed.

Next, the main setting of the group ID is performed (S402). Specifically, first, all the polygons for which the group ID is temporarily set are extracted, and the temporarily set group ID is set for the extracted polygon ID of each polygon. Then, referring to the adjacent polygon table, all the polygons that are not set with the group ID adjacent to each of the polygons that have been set are extracted, and the group ID that is the same as the set group ID is extracted to all the extracted polygons. Is temporarily set to the polygon ID.

Next, it is determined whether or not there is a polygon whose group ID is temporarily set (S403). As a result of the determination, if there is a polygon for which the group ID is temporarily set, the process returns to step S402, and the temporarily set group ID is set for the polygon ID of the polygon for which the group ID is temporarily set. The process of setting, referring to the adjacent polygon table, and temporarily setting the same group ID as the polygon ID of the polygon for which the adjacent group ID is not set is repeated.

If the result of determination in step S403 is that there is no polygon for which the group ID is temporarily set, it is determined whether or not the group ID has been completely set for all polygons (S404). As a result of the determination, if the group ID is not set for all the polygons, that is, there is a polygon for which the group ID is not set, the process returns to step S401 to define a new group ID, and the group ID is not set. One polygon ID is extracted, and a new group ID is provisionally set to the polygon ID.

As a result of the determination in step S404, if the group ID is set for all the polygons, the polygon model is separated using the relationship between the polygon ID and the group ID (S405). Specifically, the polygon ID and the coordinate value of the vertex coordinate array data shown in FIG. 10 are separated into different files for the same group ID. In addition, when a binary STL format file is used, an identification flag that can specify color information can be set for each polygon. Therefore, the group ID is set in the identification flag for each polygon without separating the files. be able to. When a plurality of separated STL format files or a single STL format file in which a group ID is set for each polygon is output to a 3D printer, a different material (including support material) or a different color material (transparent) for each group ID (Including materials) can be specified, and a model can be created in which multiple colors or contents can be seen through. In this way, the polygon model separation is not only separating the polygon ID and coordinate value of the vertex coordinate array data into different files for the same group ID, but also clearly distinguishing them within one file. It is a concept that includes. As described above, the target model separation unit separates the target model into a plurality of partial target models. The target model separation means can also separate the restriction model, which is a polygon model whose output should be restricted, into the partial restriction model in the same manner.

<5. Calculation and registration of regulatory model frequency distribution>
As a modified example, the circumscribed circle distribution and the inscribed circle distribution calculated with respect to the regulated model at a place different from the three-dimensional object shaping data output restriction device provided with the frequency distribution matching means 20 are used as the three-dimensional object shaping data. You may make it transmit and register to an output control apparatus via a network.

  FIG. 32 is a hardware configuration diagram of a three-dimensional object formation system including a three-dimensional object formation data output restriction device according to a modification. In the three-dimensional object formation system shown in FIG. 32, the three-dimensional object formation data output restriction device 101 is configured to communicate with a public network such as the Internet in the configuration of the three-dimensional object formation data output restriction device 100 shown in FIG. The network communication unit 8 is provided. The regulation model frequency distribution calculation device 102 can be realized by a general-purpose computer. As shown in FIG. 32, a CPU (Central Processing Unit) 1a, a RAM (Random Access Memory) 2a that is a main memory of the computer, A large-capacity storage device 3a such as a hard disk or flash memory for storing programs and data executed by the CPU 1a, a key input I / F (interface) 4a such as a keyboard and a mouse, and an external device such as a data storage medium A data input / output I / F (interface) 5a for data communication, a display unit 6a that is a display device such as a liquid crystal display, and a network communication unit 8a for communicating with a public network such as the Internet are provided. Connected through. The network communication unit 8 of the three-dimensional object formation data output restriction device 101 and the network communication unit 8a of the restriction model frequency distribution calculation device 102 communicate with each other, and the restriction model frequency distribution calculation device 102 transmits the three-dimensional object formation data output restriction device 101. Thus, it is possible to transmit two types of frequency distributions calculated for the regulatory model.

  In FIG. 32, the three-dimensional object formation data output restriction device 101 and the 3D printer 7 are shown in a separated form, but most of the currently available 3D printer products include the three-dimensional object formation data output restriction device 101. Constituent elements, such as CPU1, RAM2, storage device 3, key input I / F4 (not a keyboard / mouse for general-purpose computers, but several buttons at a numeric keypad level), data input / output I / F5, display unit 6 (several lines) A small liquid crystal panel capable of displaying the above-mentioned characters, a touch panel is often overlapped to serve also as a key input I / F 4), and a network communication unit 8 (wireless LAN function) is also provided in a small but overlapping manner. Accordingly, the 3D printer itself can directly receive the data for modeling a three-dimensional object via an external storage medium or the Internet, and can be operated to model a three-dimensional object alone (especially in the case of a 3D printer for consumer use, this form is preferred. Many). That is, the three-dimensional object formation data output restriction device 101 and the 3D printer 7 shown in FIG. 32 are often housed in one housing and distributed as a product called “3D printer”.

  In the regulated model frequency distribution calculation device 102, the CPU 1a executes a program stored in the storage device 3a, thereby realizing a second frequency distribution calculation unit and a frequency distribution transmission unit. The second frequency distribution calculation means realized by the restriction model frequency distribution calculation device 102 has the same function as the frequency distribution calculation means 10 realized by the three-dimensional object formation data output restriction device 100, and is compatible with the restriction model. The circumscribed circle distribution and the inscribed circle distribution are calculated as two types of frequency distribution. The frequency distribution transmission unit transmits the circumscribed circle distribution and the inscribed circle distribution, which are two types of frequency distributions calculated for the restriction model, to the three-dimensional object formation data output restriction device 101. In the three-dimensional object shaping data output restriction device 101, when the network communication unit 8 receives the frequency distribution from the restriction model frequency distribution calculation device 102, the CPU 1 executes a predetermined program and functions as a frequency distribution registration unit. The frequency distribution is registered in the regulation model database 40 realized by the storage device 3.

<6. Cloud-type 3D object modeling system>
The present invention can also be applied to a cloud-type three-dimensional object modeling system. FIG. 33 is a hardware configuration diagram of a cloud-type three-dimensional object modeling system. In the three-dimensional object formation system shown in FIG. 33, the three-dimensional object formation data output restriction device is realized by the output control terminal 201 and the processing server 202. In the three-dimensional object formation system shown in FIG. 33, the output control terminal 201 has the same hardware configuration as the computer shown as the three-dimensional object formation data output restriction device 101 in FIG. That is, the output control terminal 201 includes a CPU 11, a RAM 12, a storage device 13, a key input I / F 14, a data input / output I / F 15, a display unit 16, and a network communication unit 18, which are connected to each other via a bus. .

  The processing server 202 can be realized by incorporating a dedicated program into a general-purpose computer. As shown in FIG. 33, a CPU 11a, a RAM 12a, a storage device 13a, a key input I / F 14a, a data input / output I / F 15a, a display unit 16a, and a network communication unit 18a are connected to each other via a bus. The network communication unit 18 of the output control terminal 201 and the network communication unit 18a of the processing server 202 communicate with each other, and send output suitability data from the processing server 202 to the output control terminal 201 based on the judgment of output suitability. It is possible. In FIG. 33, the output control terminal 201 and the 3D printer 7 are shown in a separated form. However, as in the example of FIG. 32, the CPU 11, RAM 12, The storage device 13, the key input I / F 14, the data input / output I / F 15, the display unit 16, and the network communication unit 18 may be provided in an overlapping manner.

  FIG. 34 is a functional block diagram of a cloud-type three-dimensional object modeling system. The processing server 202 constituting the cloud-type three-dimensional object modeling system includes a target model receiving unit 50 and an output suitability data transmitting unit 60 in addition to each unit of the three-dimensional object modeling data output restriction device shown in FIG. It has a configuration. The target model storage unit 31 stores not the target model itself but two kinds of frequency distributions calculated for the target model. Further, the frequency distribution calculating means 10 is configured to be provided not in the processing server 202 but in the output control terminal 201.

  Components having the same functions as those of the three-dimensional object formation data output restriction device shown in FIG. The target model receiving means 50 is means for receiving the frequency distribution of the target model transmitted from the output control terminal 201 and registering it in the target model storage means 31. The CPU 11a executes a predetermined program, and the network communication section This is realized by controlling 18a. The output suitability data transmission means 60 is means for sending the output suitability data to the output control terminal 201 based on “whether or not the output should be regulated” determined by the frequency distribution matching means 20, and the CPU 11a This is realized by executing a predetermined program and controlling the network communication unit 18a.

  The processing server 202 is connected to a network such as the Internet and is accessible from a number of output control terminals. The “cloud type” of the cloud-type three-dimensional object modeling system determines whether the output should be regulated at the remote computer via the network from the output side instead of the output side that outputs the three-dimensional object by the 3D printer It means to do. In the conventional server type computer, when the access of many users is concentrated, the responsiveness becomes slow and it often causes trouble for the user. In the cloud type, the physical configuration of the computer is dynamically changed by the virtualization technology. This makes it possible to always maintain a stable response. Generally, the processing server 202 is physically realized by a plurality of computers.

  The processing operation of the cloud-type three-dimensional object modeling system shown in FIGS. 33 and 34 will be described. In the output control terminal 201, when the user specifies a target model to be output via the key input I / F 14, the CPU 11 extracts the specified target model stored in the storage device 13 and specifies the target model. A model ID which is identification information to be assigned is assigned. Then, the CPU 11 performs processing on the target model to which the model ID is assigned according to the flowchart shown in FIG. 4 or 5, and is configured by a circumscribed circle distribution and an inscribed circle distribution. Generate a frequency distribution. Further, the CPU 11 transmits the generated frequency distribution to an address such as a URL set in advance in the storage device 13 via the network communication unit 18. By using a method of transmitting a frequency distribution with a significantly smaller amount of information than polygon data, not only the transmission time is greatly shortened, but also the polygon data that is a copyrighted work is not transmitted to the processing server 202 side. Since no copy remains on the processing server 202 side, copyright infringement can be avoided.

  In parallel, the CPU 11 transmits the target model to which the model ID is assigned to the data processing unit 7a of the 3D printer 7 via the data input / output I / F 15. The target model received from the output control terminal 201 is held in the printer queue in the data processing unit 7a, and enters a standby state as an output job. At this time, it is possible to execute only the process of converting the polygon format data, which is the pre-processing in the 3D printer output, into the data in the stack format, and to enter the standby state when the data is converted into the stack format data. . Since this data processing load is also reasonably high, if the output suitability determination is completed during this time, it is possible to prevent the user from feeling an extra waiting time.

  In the processing server 202, when the target model receiving unit 50 receives the frequency distribution of the target model transmitted from the output control terminal 201, it stores the frequency distribution in the target model storage unit 31. Here, according to the flowchart shown in FIG. 3, the frequency distribution matching unit 20 performs processing, and determines whether the output of the target model corresponding to the received frequency distribution is appropriate. Output propriety data as a result of output propriety is passed from the frequency distribution matching unit 20 to the output propriety data transmitting unit 60. Then, the output suitability data transmission means 60 transmits the output suitability data with the model ID added to the output control terminal 201 that is the transmission source (access source) of the frequency distribution.

  In the output control terminal 201, when the network communication unit 18 receives the output suitability data from the processing server 202, the CPU 11 sends the received output suitability data to the data processing unit 7a of the 3D printer 7 via the data input / output I / F 15. Send to. The data processing unit 7a refers to the output job in the printer queue by using the model ID given to the received output suitability data. If the output suitability data is “appropriate (output proper)”, the data processing unit 7a changes the output job from the standby state to the output state, and outputs the target model to the output unit 7b. If the output suitability data is “No (output unsuitable)”, the data processing unit 7a discards the output job. That is, it is deleted from the printer queue.

<7. Frequency distribution calculation example>
FIG. 35 and FIG. 36 show frequency distribution calculation examples for a sphere (actually, a polyhedron approximate to a sphere) by the three-dimensional object formation data output restriction device according to the embodiment. In both FIG. 35 and FIG. 36, the polygon model is shown on the left end and the frequency distribution is shown on the right side. In the frequency distribution, the upper part shows a circumscribed circle distribution (OutRadius), and the lower part shows an inscribed circle distribution (InRadius).

  35A shows a polygon model (5040 polygon) of a sphere a (actually a polyhedron), and FIG. 35B shows an inscribed circle based on the maximum inscribed circle radius of the inscribed circle distribution. FIG. 35C is a frequency distribution of a sphere a having a radius distribution, and FIG. 35C shows a sphere according to the present invention (the inscribed circle distribution is a distribution of the inscribed circle radius normalized using the maximum circumscribed circle radius). It is a frequency distribution of a. FIG. 35D shows a polygon model (1224 polygon) of a sphere b (actually a polyhedron), and FIG. 35E shows an inscribed circle based on the maximum inscribed circle radius of the inscribed circle distribution. FIG. 35 (f) shows a frequency distribution of a sphere b having a radius distribution, and FIG. 35 (f) shows a sphere according to the present invention (the inscribed circle distribution is a distribution of the inscribed circle radius normalized using the maximum circumscribed circle radius). It is a frequency distribution of b. 36A shows a polygon model (120 polygons) of a sphere c (actually a polyhedron), and FIG. 36B shows an inscribed circle based on the maximum inscribed circle radius of the inscribed circle distribution. FIG. 36C is a frequency distribution of a sphere c having a radius distribution. FIG. 36C shows a sphere c according to the present invention (the inscribed circle distribution is a distribution of the inscribed circle radius normalized using the maximum circumscribed circle radius). This is a frequency distribution.

  35 and 36, for the circumscribed circle distribution, FIG. 35 (b) and FIG. 35 (c), FIG. 35 (e) and FIG. 35 (f), FIG. 36 (b) and FIG. Each is the same. When the inscribed circle distributions of the spheres a, b, and c are compared, the circumscribed circle distribution based on the circumscribed circle radius of the triangle formed based on the three polygons in the present invention, and the inside of the triangle formed based on the three polygons The inscribed circle distribution based on the tangent circle radius is similar to each other without being influenced by the number of polygons. This is because the inscribed circle distribution based on the inscribed circle radius of the triangle is an inscribed circle radius distribution based on the maximum inscribed circle radius, and is normalized using the maximum inscribed circle radius. The same applies to the radius distribution. For this reason, it turns out that it is easy to find a suitable polygon model and to perform output regulation easily. When the inscribed circle distribution of the spheres a, b, and c is compared with the inscribed circle radius distribution based on the maximum inscribed circle radius and the inscribed circle radius distribution normalized using the maximum inscribed circle radius. For a polygon model that is not biased depending on the direction, such as a sphere, there is no significant difference.

  FIG. 37 (a) is a polygon model (192 polygon) of the simulated handgun d, and FIG. 37 (b) is a distribution of the inscribed circle radius based on the maximum inscribed circle radius. FIG. 37C shows the frequency distribution of the simulated handgun d. FIG. 37C shows the frequency of the simulated handgun d according to the present invention (the inscribed circle distribution is a distribution of the inscribed circle radius normalized using the maximum circumscribed circle radius). Distribution. FIG. 37D shows a polygon model (168 polygons) of the simulated handgun e, and FIG. 37E shows the inscribed circle distribution as a distribution of the inscribed circle radius based on the maximum inscribed circle radius. FIG. 37 (f) shows the frequency distribution of the simulated handgun e. FIG. 37 (f) shows the frequency distribution of the simulated handgun e according to the present invention (the inscribed circle distribution is a distribution of the inscribed circle radius normalized using the maximum circumscribed circle radius). It is. In FIG. 37, regarding the circumscribed circle distribution, FIGS. 37 (b) and 37 (c), FIGS. 37 (e) and 37 (f) are the same.

  The simulated handgun e is one in which one part is missing from the simulated handgun d. When the inscribed circle distributions of the simulated pistols d and e are compared, the circumscribed circle distribution based on the circumscribed circle radius of the triangle formed based on the three polygons in the present invention, and the inscribed circle of the triangle formed based on the three polygons The inscribed circle distribution based on the circle radius is similar to each other without being affected by missing parts. This is because the inscribed circle distribution based on the inscribed circle radius of the triangle is an inscribed circle radius distribution based on the maximum inscribed circle radius, and is normalized using the maximum inscribed circle radius. The same applies to the radius distribution. For this reason, it turns out that it is easy to find a suitable polygon model and to perform output regulation easily.

  As described above, the inscribed circle distribution between the spheres a, b, and c in FIGS. 35 and 36 and the two inscribed circle distributions between the simulated pistols d and e in FIG. 37 are based on the maximum inscribed circle radius. There was no significant difference in correlation between the inscribed circle radius distribution and the inscribed circle radius distribution normalized using the maximum circumscribed circle radius. However, when the inscribed circle distributions of the sphere a and the simulated handgun d or the sphere b and the simulated handgun d are collated, a significant negative correlation is found in each case, but the normalized inner diameter is normalized using the maximum circumscribed circle radius. There is a significant difference in the correlation coefficient (Da (re)) between the distribution of the tangent circle radius and the distribution of the inscribed circle radius based on the maximum inscribed circle radius. The number was remarkably small, the difference was shown more clearly, and the shape discrimination was large. The reason can be explained as follows. Comparing the inscribed circle distributions in the lower part of FIG. 37 (b) and the lower part of FIG. 37 (c), unlike the case of the polygon model having no deviation depending on the direction as in the sphere shown in FIGS. A space appears in the right half. In this way, the blank in half of the inscribed circle distribution in the axial direction makes the substantial spread of the inscribed circle distribution narrow and sharp, and the absolute value of the correlation coefficient calculated between the inscribed circle distributions Becomes larger and the shape distinguishability becomes higher. In the three-dimensional object formation data output restriction device 100, the display unit 6 outputs not only the suitability / nonconformity but also the correlation coefficient calculated by the frequency distribution matching unit 20 in steps S620 and S640 shown in FIG. You can also do it. By visually confirming the correlation coefficient displayed and output by the display unit 6, for example, in the case of non-conformity, it is possible to determine not only the non-conformity but also the degree of non-conformity.

<8. Modified example>
The preferred embodiments of the present invention have been described above. However, the present invention is not limited to the above embodiments, and various modifications can be made. For example, in the above embodiment, the polygon to be processed is a triangle, but it may be a polygon more than a quadrangle.

  In the above embodiment, a polygon average point having the average coordinates of each vertex of the polygon is used as one point on the polygon for constituting the triangle. However, if the point is on the polygon, a point other than the polygon average point is used. It may be. For example, as a point on the polygon, a barycentric point of the polygon, a point having a value that is exactly halfway between the maximum value and the minimum value of each vertex of the polygon, and the like can be used.

  In the above-described embodiment, the circumscribed circle distribution and the inscribed circle distribution are used as the frequency distribution of the comparison target of the target model and the regulation model. However, only the inscribed circle distribution is used without using the circumscribed circle distribution. Good.

1, 1a, 11, 11a... CPU (Central Processing Unit)
2, 2a, 12, 12a ... RAM (Random Access Memory)
3, 3a, 13, 13a ... Storage device 4, 4a, 14, 14a ... Key input I / F
5, 5a, 15, 15a ... Data I / O I / F
6, 6a, 16, 16a ... display unit 7 ... 3D printer (three-dimensional object modeling apparatus)
7a: Data processing unit 7b: Output unit 8, 8a, 18, 18a: Network communication unit 10: Frequency distribution calculating means (frequency distribution calculating device)
DESCRIPTION OF SYMBOLS 20 ... Frequency distribution collating means 30, 31 ... Target model storage means 40 ... Regulatory model database 50 ... Target model receiving means 60 ... Output propriety data transmitting means 100, 101 ... Three-dimensional object Modeling data output restriction device 102 ... restriction model frequency distribution calculation device 201 ... output control terminal 202 ... processing server

Claims (21)

A device for determining whether or not to regulate when outputting a polygon model expressed as a set of polygons to a three-dimensional object modeling apparatus as three-dimensional object modeling data,
A database in which an inscribed circle distribution that is a frequency distribution of the radius of the inscribed circle of a triangle formed based on three polygons in a regulated model that is a polygon model whose output is to be regulated;
For the target model that is the polygon model to be output, calculate the circumscribed circle radius and the inscribed circle radius of the triangle formed by the predetermined points on the three polygons selected from the target model. A frequency distribution calculating means for calculating an inscribed circle distribution which is a frequency distribution of the radius of the inscribed circle normalized using the maximum value of the radius of the circle;
The calculated inscribed circle distribution is collated with the inscribed circle distribution of the regulation model registered in the database, and a frequency distribution matching means for determining whether or not the output should be regulated;
A data output regulating device for three-dimensional object formation characterized by comprising:   In calculating the inscribed circle distribution, the frequency distribution calculating unit prepares a one-dimensional array composed of a predetermined number of elements, and 35% to 50% of the maximum radius of the calculated circumscribed circle The range normalized by the value of is equally divided into the predetermined number, and the radius of each inscribed circle is assigned to any element of the predetermined number based on the value of the radius of the inscribed circle, 2. The three-dimensional object formation data output restriction device according to claim 1, wherein the inscribed circle distribution is calculated by counting the number corresponding to the element.   The frequency distribution calculating unit classifies the polygons in the target model into three groups of a first group, a second group, and a third group, and selects one polygon from each group. The three-dimensional object shaping data output restriction device according to claim 1 or 2, wherein the three polygons are selected.   The frequency distribution calculating means creates a random number array in which N / 3 consecutive integers are randomly replaced when the number of polygons in each group is N / 3, and from the beginning to the end of the random number array. The reverse random number array is created by reversing the order of the above, and the random number array is applied to one of the three groups, and the reverse random number array is applied to the other group, and the order of the polygons in the group is determined. 4. The three-dimensional object formation data output restriction device according to claim 3, wherein the polygons are selected in order from the top of each group after the replacement. The frequency distribution calculating means includes:
The random number array is applied to the first group of polygons and the inverted random number array is applied to the second group of polygons to change the order, and the polygons are selected without changing the order of the third group of polygons. N / 3 triangles are formed,
The random number array is applied to the second group of polygons and the inverted random number array is applied to the third group of polygons to change the order, and the polygons are selected without changing the order of the first group of polygons. N / 3 triangles are formed,
The random number array is applied to the third group of polygons and the inverted random number array is applied to the first group of polygons to change the order, and the polygons are selected without changing the order of the second group of polygons. 5. The three-dimensional object formation data output restriction device according to claim 4, wherein N / 3 triangles are formed. The frequency distribution calculating means includes:
Each time the inscribed circle distribution is calculated, the process of calculating the inscribed circle distribution again by changing the order of polygons in two of the three groups is repeated a predetermined number of times, 6. The three-dimensional object formation data output regulation device according to claim 4, wherein the calculated average value of the inscribed circle distribution is an inscribed circle distribution to be collated. The frequency distribution calculating means includes:
Each time the inscribed circle distribution is calculated, the order of polygons in two groups among the three groups is changed, and the inscribed circle distribution is repeatedly calculated.
When the inscribed circle distribution immediately after the calculation is compared with the inscribed circle distribution obtained immediately before that, and the similarity is found in the comparison result, the inscribed circle distribution immediately after the calculation is 6. The three-dimensional object formation data output restriction device according to claim 4, wherein the three-dimensional object formation data output restriction device has a tangent circle distribution. In the database, in addition to the inscribed circle distribution, a circumscribed circle distribution that is a frequency distribution of the radius of the circumscribed circle of the triangle is registered.
The frequency distribution calculating means calculates a circumscribed circle distribution that is a frequency distribution of the radius of the calculated circumscribed circle in addition to the inscribed circle distribution for the target model,
The frequency distribution matching means collates the circumscribed circle distribution of the calculated target model with the circumscribed circle distribution of the regulation model registered in the database in addition to the collation of the inscribed circle distribution, and outputs an output The three-dimensional object formation data output restriction device according to any one of claims 1 to 7, wherein it determines whether or not to restrict.   In calculating the circumscribed circle distribution, the frequency distribution calculating unit prepares a one-dimensional array composed of a predetermined number of elements, and sets the range of the calculated maximum value of the radius of the circumscribed circle to the predetermined number. After dividing equally, the radius of each circumscribed circle is assigned to any one of the predetermined number of elements based on the value of the radius of the circumscribed circle, and the number corresponding to the element is counted to thereby calculate the circumscribed circle. The distribution is calculated, and the three-dimensional object formation data output restriction device according to claim 8.   10. The three-dimensional object modeling data output according to claim 9, wherein the frequency distribution calculating unit weights an average value of areas of the three selected polygons when counting the number corresponding to the element. Regulatory device.   11. The three-dimensional object formation data output restriction device according to claim 10, wherein the frequency distribution calculation unit divides the value of each element by the total value of the areas of the polygons forming the target models. A target model separating means for separating the target model into a plurality of partial target models;
The three-dimensional object formation data output restriction device according to any one of claims 1 to 11, wherein the frequency distribution calculating unit performs processing on the partial target model.   The polygon is triangular, and the target model separation means separates a polygon and three adjacent polygons that share sides with the polygon so that they belong to the same partial target model. Item 13. A three-dimensional object modeling data output restriction device according to Item 12. The frequency distribution matching means, in matching the inscribed circle distribution calculated from the target model by the frequency distribution calculating means with the inscribed circle distribution registered in the database,
The correlation coefficient between the inscribed circle distributions is calculated, and when the calculated correlation coefficient is larger than a predetermined positive threshold, it is determined that the output should be regulated. The data output restriction device for three-dimensional object formation according to any one of claims 1 to 13.   For the restriction model, the radius of the circumscribed circle and the radius of the circumscribed circle of the triangle formed by the predetermined points on the three polygons selected from within the restriction model are calculated, and the maximum value of the radius of the circumscribed circle is calculated. The inscribed circle distribution calculated by the regulatory model frequency distribution calculating device that calculates the inscribed circle distribution that is the frequency distribution of the radius of the inscribed circle normalized using the database is received, and the received inscribed circle distribution is received from the database. The three-dimensional object formation data output restriction device according to any one of claims 1 to 14, further comprising a registration unit for registering the three-dimensional object.   The restriction model frequency distribution calculating device classifies the polygons in the restriction model into three groups of a first group, a second group, and a third group with respect to the restriction model, and assigns one polygon from each group. 16. The three-dimensional object formation data output restriction device according to claim 15, wherein the three polygons are selected by selection. Means for outputting the target model to a connected three-dimensional object shaping apparatus;
When it is determined that the output should be regulated by the frequency distribution matching unit that is executed in parallel with the three-dimensional object modeling process by the three-dimensional object modeling apparatus, the three-dimensional object modeling apparatus includes the target model. Means for outputting an output stop command;
The three-dimensional object formation data output restriction device according to any one of claims 1 to 16, further comprising: The output control terminal and the processing server are connected via a network,
The output control terminal has the frequency distribution calculating means,
The processing server
The database;
Receiving means for receiving an inscribed circle distribution of the target model from the output control terminal via a network;
The frequency distribution matching means;
Output suitability data transmission means for transmitting data based on whether the output should be regulated or not, determined by the frequency distribution matching means, to the output control terminal;
The three-dimensional object formation data output restriction device according to any one of claims 1 to 17, wherein the three-dimensional object formation data output restriction device is provided. A data output regulation device for three-dimensional object modeling according to any one of claims 1 to 18,
A three-dimensional object modeling apparatus that models a three-dimensional object using a polygon model whose output is permitted by the three-dimensional object modeling data output restriction device;
A three-dimensional object forming system characterized by comprising: A device that creates a frequency distribution based on the polygon model in order to determine whether or not to restrict when a polygon model expressed as a set of polygons is output to the three-dimensional object modeling device as three-dimensional object modeling data. There,
For the target model that is the polygon model to be output, calculate the circumscribed circle radius and the inscribed circle radius of the triangle formed by the predetermined points on the three polygons selected from the target model. A frequency distribution calculation device that calculates an inscribed circle distribution that is a frequency distribution of a radius of an inscribed circle normalized using a maximum value of the radius of the circle.   A program for causing a computer to function as the three-dimensional object formation data output restriction device according to any one of claims 1 to 18.