JP6638466B2 - Data output regulation device for 3D object modeling - Google Patents

Description

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

  2. Description of the Related Art In recent years, a 3D printer that processes a resin, a gypsum, or the like based on data representing a three-dimensional object and forms the object has been widely used as a three-dimensional object forming apparatus. The 3D printer outputs a three-dimensional object using a polygon model expressing the three-dimensional shape of the three-dimensional object by a set of polygons. As medical applications of 3D printers, modeling data (polygon models) for 3D printer output are created based on CT / MRI images (DICOM format 3D voxel data) taken by medical diagnosis, and surgery simulation, medical education, Attempts have been made to output organ models for informed consent and to create artificial organs (blood vessels, bones, joints, etc.) for implantation in the body.

  The 3D printer has an innovative feature that can indirectly transmit not only information but also objects to remote places via the Internet. For this reason, there has been a problem that dangerous goods such as guns and swords, which have been regulated by customs, are circulated across borders in data form, and can be easily obtained as goods by a 3D printer.

JP 2015-210873 A JP 2015-210789 A

  The 3D printer has an excellent feature that cannot be realized by the conventional manufacturing method that a complex figure including a surface pattern can be output collectively in a completed form. However, in the manufacture of dangerous goods such as handguns, the 3D printer is divided into parts. A method of outputting to a 3D printer is generally used. The reason is that there is a movable mechanism that requires accuracy that requires manual adjustment. In addition, when metal material parts or electronic parts that cannot be output with a 3D printer are required, when the output cannot be performed collectively due to restrictions on the printer output size, or when the form of the completed model is unstable and easily collapses during molding, etc. 3D printer output. For this reason, in realizing a 3D printer output restriction system as disclosed in Patent Document 1, it is required to perform partial collation between an entire model registered in a blacklist and a part model output by a 3D printer.

  However, the application of Patent Literature 1 is limited to a case in which polygon data having the same polygon configuration as the polygon model registered in the DB is included as a part. There was a problem that the correspondence could not be maintained and an appropriate determination could not be made. As another countermeasure, a method of registering polygon data of the entire model and all the component models on the DB side is conceivable. Thus, if the model to be output to the 3D printer is a part model registered in the DB, it can be prevented. However, it cannot cope with a case where a plurality of components are output in a 3D printer in a combined form. However, it is difficult to perform DB registration for any combination of parts when the number of components increases.

In view of this, Japanese Patent Application Laid-Open Publication No. H11-163873 proposes a method in which a given polygon model is divided into parts and collated. On the DB side, the feature vectors of the entire model and all component models are registered, and the 3D model to be output is divided into components, and collation is performed for each component. The technique of Patent Document 2 has the following two problems.
(1) In the case of a model composed of a large number of subdivided parts, it is difficult to determine the illegality of individual parts alone (since each part is used even with the proper model, the proper model is illegal) It is easy to judge incorrectly.)
(2) If a modification is made to connect the composite part to an inseparable form or a modification is made to separate the single part into a separable state, consistency with the parts registered in the DB will be lost. And it cannot be judged.

  Accordingly, the present invention provides a three-dimensional object forming model capable of appropriately determining whether or not output is appropriate even when a 3D polygon model to be output is output in a form composed of a plurality of parts. It is an object to provide a data output control device.

In order to solve the above problems, in the first aspect of the present invention,
When outputting a polygon model represented as a set of polygons to a three-dimensional object forming apparatus as three-dimensional object forming data, the device determines whether or not to be restricted,
A database in which a frequency distribution of parameters characterizing a triangle formed based on three polygons in the regulation model which is a polygon model whose output is to be regulated is registered;
For a target model, which is an output target polygon model, a parameter that characterizes a triangle formed by predetermined points on three polygons selected from the target model is calculated, and a frequency distribution of the parameter is calculated. Distribution calculation means;
A process of matching the scale of the parameter of the frequency distribution of the target model with the scale of the parameter of the frequency distribution of the regulatory model registered in the database, and generating a frequency distribution of the matched target model. Distribution matching means;
The frequency distribution of the matched target model is compared with the frequency distribution of the regulation model, and a frequency distribution matching unit that determines whether output should be regulated,
And a data output control device for three-dimensional object modeling.

  According to the first aspect of the present invention, there is provided a database in which a frequency distribution of parameters characterizing a triangle formed based on three polygons in the regulation model is registered, and a target model which is an output target polygon model is provided. Calculating a parameter characterizing a triangle formed by predetermined points on three polygons selected from the target model, calculating a frequency distribution of the parameter, and storing a scale of a parameter of the frequency distribution of the target model in a database. Perform processing to match the scale of the parameter of the frequency distribution of the registered regulatory model, create a frequency distribution of the matched target model, collate with the frequency distribution of the regulated model of the matched target model, and output Since it is determined whether or not to regulate, the 3D polygon model to be output is Even when made as such is output in the form, it is possible to properly determine the appropriateness of output.

  Further, in a second aspect of the present invention, the parameter characterizing the triangle is a circumscribed circle radius of the triangle formed by predetermined points of each of the three polygons.

  According to the second aspect of the present invention, since the parameter characterizing the triangle formed based on the three polygons in the regulation model is the circumcircle radius of the triangle formed by the predetermined points of each of the three polygons, When outputting a three-dimensional object based on a polygon model, it is possible to determine with high accuracy using the frequency distribution of the circumscribed circle radius that sharply reflects on the shape of the polygon model whether output should be regulated Become. Here, to be sharply reflected in the shape of the polygon model has the following meaning. For example, a sphere that is the basis of a 3D shape has a simple frequency distribution in which a peak appears at one location at a maximum distance corresponding to the radius of the sphere. However, as the 3D shape deviates from the sphere, peaks are added at various locations, resulting in a complex frequency distribution that clearly reacts to the shape difference.

  Further, in the third aspect of the present invention, the emphasis component creating means for creating an emphasis component which corrects the value of each frequency to fall within a predetermined range with respect to the frequency distribution of the matched target model, and Frequency distribution emphasizing means for respectively multiplying the frequency distribution of the target model and the frequency distribution of the regulatory model by the emphasis component to create a frequency distribution of the target model and a frequency distribution of the regulatory model. The frequency distribution matching unit is characterized in that the enhancement frequency distribution of the target model is compared with the enhancement frequency distribution of the regulation model.

  According to the third aspect of the present invention, an emphasized component in which the value of each frequency is corrected to fall within a predetermined range with respect to the frequency distribution of the matched target model is created, and the frequency distribution of the matched target model and By multiplying the frequency distribution of the regulatory model by each of the emphasis components, an enhancement frequency distribution of the target model and an enhancement frequency distribution of the regulation model are created, and the enhancement frequency distribution of the target model and the enhancement frequency distribution of the regulation model are calculated. Since the collation is performed, a component corresponding to the frequency distribution of the target model can be substantially extracted from the frequency distribution of the regulatory model, and can be partially collated with the frequency distribution of the regulatory model. Therefore, even if the target model corresponds to one component of the regulation model, it is possible to determine with high accuracy whether or not the output should be regulated.

  Further, in the fourth aspect of the present invention, the frequency distribution calculating means classifies the polygons in the target model into three groups of a first group, a second group, and a third group with respect to the target model, The three polygons are selected by selecting one polygon at a time from each group.

  According to the fourth aspect of the present invention, the polygons in the target model are classified into three groups of a first group, a second group, and a 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 they are selected at random without classifying them 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.

  In the fifth aspect of the present invention, when the number of polygons in each of the groups is N / 3, the frequency distribution calculating means randomly replaces (shuffles) N / 3 consecutive integers. And an inverted random number array in which the order from the beginning to the end of the random number array is reversed. One of the three groups is the random number array, and the other is the inverted random number array. After the arrangement of the polygons in the group is changed by applying the arrangement, the polygons are selected sequentially from the head of each group.

  According to the fifth aspect of the present invention, a random number array in which consecutive integers of the number of polygons N / 3 of each group are randomly replaced is created, and an inverted random number array in which the order of the random number array from the beginning to the end is reversed. , And apply the random number array to one of the three groups and the inverted random number array to the other group to change the order of the polygons in the group. Is selected, the arrangement order of the polygons of the three groups can be set at random from the position in the three-dimensional space, and the parameters that characterize the triangle calculated by the combination of polygons that are random and do not overlap with each other Based on this, the frequency distribution can be obtained.

Further, in a sixth aspect of the present invention, the frequency distribution calculating means includes:
The random number array is applied to the polygons of the first group, and the inverted random number array is applied to the polygons of the second group to change the order, and the polygons are selected without changing the order of the polygons in the third group. Form N / 3 triangles,
The random number array is applied to the polygons of the second group, and the inverted random number array is applied to the polygons of the third group to change the order, and the polygons are selected without changing the order of the polygons of the first group. Form N / 3 triangles,
The random number array is applied to the polygons of the third group, and the inverted random number array is applied to the polygons of the first group to change the order, and the polygons are selected without changing the order of the polygons of the second group. It is characterized in that N / 3 triangles are formed.

  According to the sixth aspect of the present invention, the random number array is applied to the polygons of the first group and the inverted random number array is applied to the polygons of the second group to change the order, and the polygons of the third group are changed without changing the order. The selection is performed to form N / 3 triangles, the random number array is applied to the polygons of the second group, and the inverted random number array is applied to the polygons of the third group, and the order is changed, and the order of the polygons of the first group is changed. The polygons of the second group are changed by applying polygon selection to form N / 3 triangles, applying a random number array to the polygons of the third group, and applying an inverted random number array to the polygons of the first group. Of polygons is selected without changing the order of the polygons, and N / 3 triangles are formed. N pieces of triangular can be formed without overlapping random and each other.

In the seventh aspect of the present invention,
The frequency distribution calculating means,
Each time the frequency distribution is calculated once, the process of re-calculating the frequency distribution by changing the order of the polygons in two of the three groups is repeated a predetermined number of times. The average value of the distribution is a frequency distribution to be compared.

  According to the seventh aspect of the present invention, every time the frequency distribution is calculated once, the process of re-calculating the frequency distribution by changing the order of the polygons in two of the three groups is performed a predetermined number of times. The frequency distribution is calculated based on N new triangles that do not overlap with the already calculated N triangles because the average value of the calculated frequency distribution is repeatedly set and used as the frequency distribution to be compared. Since the frequency distribution is updated, it is possible to obtain an accurate frequency distribution so that the frequency distribution is not biased to a peculiar form due to too few triangular samples being a combination of three polygons. .

In an eighth aspect of the present invention,
The frequency distribution calculating means,
Each time the frequency distribution is calculated once, the order of the polygons in two of the three groups is changed, and the process of calculating the frequency distribution again is repeated.
The frequency distribution immediately after calculation and the frequency distribution obtained immediately before are compared, and when similarity is recognized in the comparison result, the frequency distribution immediately after calculation is set as the frequency distribution to be compared. It is characterized by the following.

  According to the eighth aspect of the present invention, every time the frequency distribution is calculated once, the process of re-calculating the frequency distribution by changing the order of the polygons in two of the three groups is repeated. The frequency distribution immediately after the frequency distribution was compared with the frequency distribution obtained immediately before the frequency distribution, and when the similarity was recognized in the comparison result, the frequency distribution immediately after the calculation was used as the frequency distribution to be compared. It is possible to obtain an optimal frequency distribution while automatically judging whether or not the frequency distribution is likely to be biased to a peculiar form due to too few triangle samples, which are combinations of two polygons.

In the ninth aspect of the present invention,
In calculating the frequency distribution, the frequency distribution calculation unit prepares a one-dimensional array including a predetermined number of elements, and equally divides the range of the maximum value of the calculated parameter into the predetermined number. Above, each parameter is assigned to any one of the predetermined number of elements (ma) based on the value of the parameter, and the number (Ha (ma)) corresponding to the element is counted, whereby the frequency distribution is calculated. It is characterized in that it is calculated.

  According to the ninth aspect of the present invention, in calculating the frequency distribution, a one-dimensional array composed of a predetermined number of elements is prepared, and the range of the maximum value of the calculated parameter is equally divided into the predetermined number. Above, each parameter is assigned to one of a predetermined number of elements based on the value of the parameter, and the frequency distribution is calculated by counting the number corresponding to the element. It is possible to easily calculate the frequency distribution based on the parameters of the triangle formed as follows.

  Further, in a tenth aspect of the present invention, the frequency distribution calculating means weights an average value of the 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 frequency distribution, when the number corresponding to the assigned element is counted, weighting is performed with the average value of the areas of the three selected polygons. The difference in the definition of the division is not so much reflected in the frequency distribution, and it is possible to determine that a plurality of polygon models having different definition of the polygon are the same (output should be regulated).

  Further, in the eleventh aspect of the present invention, the frequency distribution calculating means calculates a value of each of the elements (Hd (md), Ha (ma) by using a sum total (Ssum) of areas of polygons constituting each of the target models. ) Is divided.

  According to the eleventh aspect of the present invention, when calculating the frequency 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 frequency distribution excluding the influence.

  In a twelfth aspect of the present invention, two parameters, a first parameter and a second parameter, are used as parameters characterizing the triangle, and the frequency distribution is a first frequency distribution corresponding to a first parameter. , A second frequency distribution corresponding to the second parameter.

  According to the twelfth aspect of the present invention, two parameters, a first parameter and a second parameter, are used as parameters characterizing a triangle, and the frequency distribution is a first frequency distribution corresponding to the first parameter. Since there are two kinds of frequency distributions of the second frequency distribution corresponding to the two parameters, the similarity of the two polygon models is determined from a plurality of viewpoints, and the determination of whether to restrict the output is more accurately performed. It is possible to do.

  In a thirteenth aspect of the present invention, the radius of the circumscribed circle of the triangle is used as the first parameter, the radius of the circumscribed circle of the triangle is used as the second parameter, and the circumscribed circle is used as the first frequency distribution. A circumscribed circle distribution, which is a frequency distribution of the radius of, is used, and an inscribed circle distribution, which is a frequency distribution of the radius of the inscribed circle, is used as the second frequency distribution.

  According to the thirteenth aspect of the present invention, the radius of the circumscribed circle of the triangle is used as the first parameter, the radius of the inscribed circle of the triangle is used as the second parameter, and the frequency distribution of the radius of the circumscribed circle is used as the first frequency distribution. Since a certain circumcircle distribution is used and the inscribed circle distribution, which is a frequency distribution of the radius of the inscribed circle, is used as the second frequency distribution, the output is restricted when a three-dimensional object is output based on the polygon model. It is possible to determine with high accuracy using a circumscribed circle distribution that reflects the shape of the polygon model sharply, and to determine the similarity between two polygon models from multiple viewpoints. In addition, it is possible to more accurately determine whether the output should be regulated.

  Further, in the fourteenth 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 circumscribed circle of the calculated circumscribed circle. After equally dividing the range normalized by the value of 35% to 50% of the maximum value of the radius into the predetermined number, the radius of each inscribed circle is determined based on the value of the radius of the inscribed circle. The inscribed circle distribution is calculated by allocating to a predetermined number of elements and counting the number corresponding to the elements.

  According to the fourteenth aspect of the present invention, when calculating the inscribed circle distribution, a one-dimensional array composed of a predetermined number of elements is prepared, and 35% or more of the calculated maximum value of the radius of the circumscribed circle is prepared. After equally dividing the range normalized by the value of 50% into the predetermined number, the radius of each inscribed circle is divided into any of the predetermined number of elements based on the value of the radius of the inscribed circle. Allocation, by counting the number corresponding to the element, the inscribed circle distribution was calculated, so that a distribution in which the scale ratio between the circumscribed circle radius and the inscribed circle radius was maintained, the distribution Is narrow and sharp, and shape discrimination is enhanced.

  Further, in the fifteenth aspect of the present invention, the frequency distribution matching unit calculates a peak position that gives a maximum value when matching the frequency distribution of the matched target model with the frequency distribution of the regulated model, It is characterized in that it is determined whether or not the output should be regulated based on the calculated difference between the peak positions.

  According to the fifteenth aspect of the present invention, in matching the frequency distribution of the matched target model with the frequency distribution of the regulation model, the peak position that gives the maximum value is calculated, and based on the difference between the calculated peak positions. It is determined whether or not the output should be regulated, so even if the frequency distribution of the regulated model represents the characteristics of an object composed of a large number of components, the component represented by the frequency distribution of the matched target model Can be specified from the frequency distribution of the regulatory model, and accurate matching can be performed.

  In a sixteenth aspect of the present invention, a parameter characterizing a triangle formed by predetermined points on three polygons selected from within the regulation model is calculated for the regulation model, and a frequency distribution of the parameter is calculated. And a registration unit that receives the frequency distribution calculated by the regulatory model frequency distribution calculation device that calculates the frequency distribution, and registers the received frequency distribution in the database.

  According to the sixteenth aspect of the present invention, a parameter characterizing a triangle formed by predetermined points on three polygons selected from within the regulatory model is calculated for the regulatory model, and a frequency distribution of the parameter is calculated. A regulation model frequency distribution calculation device is separately prepared, and the frequency distribution of the regulation model is received from the regulation model frequency distribution calculation device and registered in the database, so that the database can be updated quickly from a remote place. It becomes possible.

In a seventeenth aspect of the present invention,
Means for outputting the target model to a connected three-dimensional object forming apparatus,
When it is determined that the output should be regulated (output inappropriate) by the frequency distribution matching means executed in parallel with the three-dimensional object modeling process by the three-dimensional object modeling device, the three-dimensional object modeling device includes: Means for outputting an output stop instruction of the target model,
Is further provided.

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

In the eighteenth aspect of the present invention,
An output control terminal and a processing server connected via a network,
The output control terminal includes the frequency distribution calculation unit,
The processing server,
Said database;
Receiving means for receiving the frequency distribution of the target model from the output control terminal via a network,
Said frequency distribution matching means,
Output suitability data transmitting means for transmitting data based on whether or not output should be regulated, 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 frequency distribution of the target model is received via the network, and the determination result obtained by determining whether the output should be regulated is transmitted to the transmission source of the frequency distribution of the target model. With this configuration, it is possible to provide a determination as to whether or not output should be regulated in a cloud type, thereby reducing processing on the output side. Furthermore, since the database can be centrally managed on the cloud side, there is no need to manage the database in the output control terminal connected to each 3D modeling 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,
Said three-dimensional object modeling data output regulation device,
A three-dimensional object modeling device that models a three-dimensional object using a polygon model permitted to be output by the three-dimensional object modeling data output control device,
And a three-dimensional object forming system characterized by having:

  According to the nineteenth aspect of the present invention, a three-dimensional object modeling data output control device and a three-dimensional object modeling device for forming a three-dimensional object using a polygon model permitted to be output by the three-dimensional object modeling data output control device are used. Since the object modeling system is realized, it is possible to provide a three-dimensional object modeling system in a form such as a 3D printer incorporating a board computer.

In the twentieth aspect of the present invention,
When outputting a polygon model represented as a set of polygons to a three-dimensional object forming apparatus as three-dimensional object forming data, a device that creates a frequency distribution based on the polygon model in order to determine whether or not the restriction is required. So,
For a target model, which is an output target polygon model, a parameter that characterizes a triangle formed by predetermined points on three polygons selected from the target model is calculated, and a frequency distribution of the parameter is calculated. A distribution calculation device is provided.

  According to the twentieth aspect of the present invention, for a target model which is a polygon model to be output, a parameter characterizing a triangle formed by predetermined points on three polygons selected from the target model is calculated. Since the frequency distribution of the parameter is calculated, when the polygon model is output to the three-dimensional object molding apparatus as three-dimensional object molding data, the frequency distribution for determining whether or not to restrict the frequency is reduced with a small processing load. It can be created at high speed.

  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 forming data output restriction device according to any one of the above-described aspects.

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

  According to the present invention, when outputting a three-dimensional object based on a polygon model, even in a case where output is performed in a state of being divided into component parts, determination of whether or not output should be regulated can be performed at high speed with a small processing load. Can be performed.

It is a figure showing the relation with the feature vector of the component of 3D shape. 1 is a hardware configuration diagram of a three-dimensional object forming system including a three-dimensional object forming data output control device 100 according to an embodiment of the present invention. It is a functional block diagram showing the composition of the data output control device for three-dimensional object modeling concerning one embodiment of the present invention. It is a flow chart which shows the processing outline of the data output regulation device for three-dimensional object modeling concerning one embodiment of the present invention. It is a figure showing a situation of collation of a target model and a regulation model. 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 the calculation process 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 figure showing the relation between a frequency distribution (histogram) and a peak position. FIG. 11 is a hardware configuration diagram of a three-dimensional object forming system including a three-dimensional object forming data output control device according to a modification. FIG. 2 is a hardware configuration diagram of a cloud type three-dimensional object forming system. It is a functional block diagram of a cloud type solid thing modeling system. It is a figure showing the example of collation of the whole model and its parts. It is a figure showing the example of collation of the whole model and its parts. It is a figure showing the example of collation of the whole model and its parts. It is a figure showing the example of collation of the whole model and its parts. It is a figure showing the example of collation of the whole model and its parts. It is a figure showing the example of collation of the whole model and its parts. It is a figure showing the example of collation of the whole model and its parts. It is a figure showing the example of collation of the whole model and its parts. It is a figure showing the example of collation of the whole model and its parts. It is a figure showing the example of collation of the whole model and its parts. It is a figure showing the example of collation of the whole model and its parts. It is a figure showing the example of collation of the whole model and its parts.

<1. Basic concept of the present invention>
First, the basic concept of the present invention will be described. FIG. 1 is a diagram illustrating a relationship between a 3D component and a feature vector of the component. In FIG. 1, the 3D shape is shown in a two-dimensionally projected state for convenience of explanation. The upper pentagon in FIGS. 1A and 1B is a component A composed of ten polygons (only five polygons are visible in the drawing, but five polygons are hidden behind). The lower hexagon in FIGS. 1A and 1B is composed of 12 polygons (only 6 polygons are visible in the drawing, but 6 polygons are hidden behind). 2 shows a component B to be used. FIG. 1A shows a case where a composite component composed of a decahedral component and a dodecahedral component is treated as a single unit, and FIG. 1B shows a decahedral component and a dodecahedral component. The case where it is treated as a single unit is shown.

  FIG. 1A shows three triangles composed of points on three polygons, and FIG. 1B shows two triangles composed of points on three polygons. 1A and 1B, the circumscribed circle and the inscribed circle of each of these five triangles are indicated by broken lines. As will be described later, the radius of the circumscribed circle and the radius of the inscribed circle play a role as a feature vector expressing the features of the three-dimensional polygon model.

  When a triangle composed of points on three polygons is to be selected as a basis of a feature vector, it is needless to say that, for a single part as shown in FIG. You. On the other hand, in the composite part as shown in FIG. 1A, a triangle is selected between the components, but a triangle is selected within each component. As can be seen by comparing FIG. 1 (a) and FIG. 1 (b), some of the triangles selected for the composite part as shown in FIG. Often, only triangles selected by themselves are included. However, since the selection of triangles is performed randomly for both the composite part and the component parts, all triangles selected in each component part of FIG. 1B are not necessarily selected for the composite part of FIG. It is not included in the triangle. That is, since the feature vector of the polygon model of the composite component includes the component of the feature vector of the polygon model of each component, if the feature vector of the polygon model is registered in the database in the form of the composite component, When outputting a three-dimensional shape using a polygon model, it is possible to check whether the feature vector of the polygon model for each component partially matches the feature vector of the polygon model of the composite component registered in the database. The output can be regulated for those that match. In the present invention, based on such a concept, a feature vector of a polygon model representing a composite part is registered in a database, and a process suitable for matching with a feature vector of a polygon model serving as a component is performed. .

Hereinafter, preferred embodiments of the present invention will be described in detail with reference to the drawings.
<2. Device Configuration>
FIG. 2 is a hardware configuration diagram of a three-dimensional object forming system including the three-dimensional object forming data output control device 100 according to one embodiment of the present invention. The three-dimensional object modeling data output control device 100 according to the present embodiment can be realized by a general-purpose computer, and as shown in FIG. 2, a CPU (Central Processing Unit) 1 and a RAM ( A large-capacity storage device 3 such as a hard disk or a 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; 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 as a display device such as a liquid crystal display are provided, and are connected to each other via a bus. ing.

  The 3D printer 7 is a general-purpose 3D printer. The 3D printer 7 processes a material such as a resin or a plaster based on a three-dimensional object forming data which is a polygon model expressing a three-dimensional shape of the three-dimensional object by a set of polygons to produce a three-dimensional object. It is a three-dimensional object modeling device for modeling. The 3D printer 7 has 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, and the output unit 7b forms a three-dimensional object based on the three-dimensional object forming data received from the data input / output I / F 5. It has become.

  In FIG. 2, the three-dimensional object modeling data output restricting device 100 and the 3D printer 7 are shown in a separated form, but most of the 3D printer products currently on the market have the three-dimensional object modeling data output restricting device 100. Constituent elements: CPU1, RAM2, storage device 3, key input I / F4 (not a keyboard / mouse for general-purpose computer, but several buttons of numeric keypad level), data input / output I / F5, display unit 6 (several lines) Are often used as key input I / Fs 4 by superimposing a small liquid crystal panel or a touch panel capable of displaying the characters (). Therefore, it is possible for the 3D printer 7 itself to directly receive the data for forming a three-dimensional object via an external storage medium and to form a three-dimensional object by itself (especially for a consumer 3D printer, this form is more common). ). That is, the three-dimensional object modeling system shown in FIG. 2 is often housed in one housing and distributed as a product called “3D printer”.

  FIG. 3 is a functional block diagram illustrating a configuration of the data output restriction device for three-dimensional object modeling according to the present embodiment. In FIG. 3, 10 is a frequency distribution calculating means, 13 is a frequency distribution matching means, 15 is an emphasis component creating means, 17 is a frequency distribution emphasizing means, 20 is a frequency distribution matching means, 30 is a target model storage means, and 40 is a regulatory model. It is a database. The frequency distribution calculating means 10 calculates a parameter characterizing a triangle formed by predetermined points on three polygons selected from the target model, for a target model which is a polygon model to be output, and Calculate the frequency distribution. In the present embodiment, two types of parameters, a first parameter and a second parameter, are used. More specifically, the radius of a circumscribed circle of a triangle formed by predetermined points on three polygons selected from within the target model is used as the first parameter, and the radius is selected from within the target model as the second parameter. The radius of the inscribed circle of the triangle formed by the predetermined points on the three polygons is used. Therefore, the frequency distribution calculating means 10 calculates, as the first frequency distribution, a circumscribed circle distribution that is a frequency distribution of the radius of the circumscribed circle, and a second frequency distribution of the inscribed circle distribution that is the frequency distribution of the radius of the inscribed circle. Two types of frequency distributions will be calculated.

  The frequency distribution matching unit 13 performs a process of matching the radius scale of the parameter of the horizontal axis of the frequency distribution of the target model with the radius scale of the parameter of the horizontal axis of the frequency distribution of the regulatory model registered in the database. Since the maximum value on the horizontal axis of the frequency distribution is normalized by the maximum value of the circumscribed circle radius calculated for each model, the scale of the horizontal axis of the frequency distribution does not usually match between the target model and the regulated model. Therefore, when the frequency distribution matching unit 13 outputs each of the target model and the regulated model by the 3D printer, the frequency distribution matching unit 13 sets the actual dimension corresponding to the maximum value on the horizontal axis of the frequency distribution of the target model to the actual dimension α, the frequency of the regulated model, When the actual dimension corresponding to the maximum value of the horizontal axis of the distribution is the actual dimension β, the process of reducing the frequency distribution in the horizontal direction so that the horizontal axis of the frequency distribution of the target model is reduced by the actual dimension α / the actual dimension β. I do. Although the frequency distribution is composed of a circumscribed circle distribution and an inscribed circle distribution, the scales of the horizontal axes of the two coincide with each other, so that the horizontal axis of both frequency distributions is reduced by the actual dimension α / actual dimension β. Good. The emphasis component generation means 15 generates an emphasis component in which the value of each frequency is corrected to fall within a predetermined range with respect to the frequency distribution of the matched target model. The frequency distribution emphasizing means 17 multiplies the matched frequency distribution of the target model and the frequency distribution of the regulation model by the respective emphasis components, and creates an emphasis frequency distribution of the target model and an emphasis frequency distribution of the regulation model. As the emphasis frequency distribution, as described above, two types of emphasis frequency distributions are calculated: a circumscribed circle distribution that is a frequency distribution of a radius of a circumscribed circle, and an inscribed circle distribution that is a frequency distribution of a radius of an inscribed circle. Will be. The frequency distribution matching means 20 checks the emphasis frequency distribution calculated for the target model with the emphasis frequency distribution calculated for the regulation model, and determines whether output should be regulated, that is, whether the output is inappropriate or appropriate. Is determined.

  The frequency distribution calculating means 10, the frequency distribution matching means 13, the emphasis component creating means 15, the frequency distribution emphasizing means 17, and the frequency distribution matching means 20 are realized by the CPU 1 executing a program stored in the storage device 3. You. The target model storage unit 30 is a storage unit that stores a target model that is a polygon model to be determined whether output should be regulated or not, and is realized by the storage device 3. The polygon model is data expressing a predetermined three-dimensional shape in a three-dimensional space by a set of polygons, and is output as three-dimensional object forming data to a three-dimensional object forming device such as a 3D printer. In this embodiment, STL (Standard Triangulated Language) is adopted as the data format of the polygon model.

  The regulation model database 40 stores a circumscribed circle distribution and an inscribed circle distribution calculated as two types of frequency distributions with respect to a regulated model which is a polygon model whose output is to be regulated, and is stored in a database. This is realized by the device 3. The circumscribed circle distribution and the inscribed circle distribution, which are two kinds of frequency distributions, play a role as a feature vector expressing the features of the regulatory model. That is, the difference between the original regulation models can be identified by the two types of frequency distributions, but the original regulation model cannot be restored. This is similar to the fact that individual differences can be identified by a fingerprint, but the figure itself cannot be restored. Therefore, the two kinds of 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 two works. In the regulation model database 40, it is possible to register not only the circumcircle distribution and the inscribed circle distribution but also the regulation model itself on which the calculation of the circumscribed circle distribution and the inscribed circle distribution is based. Has not registered the regulatory model itself due to copyright issues.

  Each component shown in FIG. 3 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 the dedicated program. In this specification, a computer means a device having an arithmetic processing unit such as a CPU and capable of data processing, and is incorporated not only into a general-purpose computer such as a personal computer but also into a “3D printer” as a product. Also includes a board computer.

  In the storage device 3 shown in FIG. 2, a dedicated program for operating the CPU 1 and causing the computer to function as a three-dimensional object modeling data output control device is mounted. By executing this dedicated program, the CPU 1 realizes functions as the frequency distribution calculating means 10, the frequency distribution matching means 13, the emphasis component creating means 15, the frequency distribution emphasizing means 17, and the frequency distribution matching means 20. Become. The storage device 3 not only functions as the target model storage unit 30 and the regulation model database 40, but also stores various data necessary for processing as a data output regulation device for three-dimensional object modeling.

  In the hardware configuration shown in FIG. 2, by installing only a dedicated program for causing a computer to function as the frequency distribution calculating unit 10 in the storage device 3, a frequency distribution calculating device for calculating the frequency distribution Can also be realized. The frequency distribution calculating device calculates and outputs two types of frequency distributions based on the target model read from the target model storage means 30. The output frequency distribution is separately input to a device including a frequency distribution matching unit 13, an emphasis component creating unit 15, a frequency distribution emphasizing unit 17, a frequency distribution matching unit 20, and a regulation model database 40, and is used for matching and output regulation. A determination can be made.

<3. Processing Operation>
<3.1. Pre-processing>
Next, the processing operation of the three-dimensional object modeling data output control device shown in FIGS. 2 and 3 will be described. First, two types of frequency distributions, a circumscribed circle distribution and an inscribed circle distribution, 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 modeling data output control device described later. The creation of the circumscribed circle distribution and the inscribed circle distribution from the regulation model may be performed by the three-dimensional object modeling data output regulation 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 regulatory model database 40.

  The output may be restricted not only to dangerous goods such as guns and swords, but also to copyrighted works such as characters. In any case, the produced polygon model is a work in which the copyright owner exists. Therefore, in order to register a polygon model, which is a literary work, in the database, the act itself requires the permission of the author. 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 it is not possible to restore the person's figure itself, and the circumscribed circle distribution and inscribed circle distribution are equivalent to fingerprints. And is not a copyrighted work. Therefore, by registering in the form of the circumscribed circle distribution and the inscribed circle distribution as in the present invention, it is possible to construct a database in which the features of the regulatory model are recorded without requiring permission from the author.

<3.2. Processing overview>
Next, the processing operation of the three-dimensional object modeling data output control device shown in FIGS. 2 and 3 will be described. FIG. 4 is a flowchart showing an outline of processing of the data output control device for three-dimensional object modeling according to one embodiment of the present invention. First, the frequency distribution calculating means 10 calculates two types of frequency distributions, a circumscribed circle distribution and an inscribed circle distribution, for a target model which is a polygon model (step S100). Next, matching processing is performed on the calculated two types of frequency distributions using the frequency distribution of the regulatory model registered in the regulatory model database 40 (step S130). Subsequently, an emphasis component creation process is performed using the two matched frequency distributions (step S150). Further, using the created emphasis component, the two types of frequency distribution created in step S130 are emphasized (step S170). Then, the emphasis frequency distribution of the emphasized target model is compared with the emphasis frequency distribution of the emphasized regulatory model (step S200).

  The manner in which two polygon models are collated by the processing shown in FIG. 4 will be described with reference to FIG. FIG. 5 is a diagram showing how the target model and the regulation model are collated. The example of FIG. 5 shows a case where the whole is registered in the database as the regulation model (1) and a part of the regulation model (1) is used as the target model (2). In this case, two types of frequency distributions (3) are obtained from the regulation model (1) and two types of frequency distributions (4) are obtained from the target model (2) by the process of step S100. At this time, the horizontal axis of the frequency distribution indicates the maximum circumscribed circle radius (described later as maximum radius a and maximum radius b in FIG. 5), and does not reflect the scale at the time of 3D printer output. The scale of the maximum circumscribed circle radius is greatly different between and. Therefore, by the process of step S130, the process of matching the frequency distribution of the target model with the frequency distribution of the regulation model using the actual maximum circumscribed circle radius is performed (5). Specifically, when each of the target model and the regulated model is output by a 3D printer, the actual dimension of the maximum circumscribed circle radius of the target model is the actual dimension α, and the actual dimension of the maximum circumscribed circle radius of the regulated model is the actual dimension β. Then, a process of reducing the frequency distribution in the horizontal direction is performed such that the horizontal axis of the frequency distribution of the target model is the actual dimension α / the actual dimension β (maximum radius b / maximum radius a). Although the frequency distribution is composed of a circumscribed circle distribution and an inscribed circle distribution, the scales of the horizontal axes of the two coincide with each other. You can reduce it. After that, an emphasis component is created by the process of step S150 (6), and after the emphasis process is performed (7) (8), a collation process is performed (9).

<3.3. Calculation of frequency distribution>
Hereinafter, the calculation processing of the frequency distribution will be specifically described.
<3.3.1. First method>
First, the frequency distribution calculation process in step S100 will be described. There are two methods for calculating the frequency distribution, the first method and the second method. First, the first method will be described. FIG. 6 is a flowchart illustrating the details of the frequency distribution calculation process according to the first technique. Here, the number N of polygons of the target model and a variable i (i = 0,..., N−1) for specifying the polygon are set, and the vertices of each polygon i are represented by (Xu (i), Yu (i), Zu ( i)). u indicates a vertex number. In the present embodiment, since the polygon is triangular, three values of u = 0, 1, and 2 are taken. Therefore, for example, Xu (i) is also described as X0 (i), X1 (i), and X2 (i). The polygon and the normal vector whose vertex has been specified are necessary for output by the 3D printer, which is a three-dimensional object forming apparatus, and the direction of the normal vector indicates the outside of the forming surface (the direction from the material to the air). Normal vectors are usually recorded for each polygon in a polygon model output to a 3D printer. The target model also has its output scale information Sout. The output scale information Sout is an actual dimension when the target model is output as a three-dimensional object, and is an actual dimension per unit length (length 1) of a three-dimensional coordinate system defining a vertex, and the unit is expressed in mm. Have been. The frequency distribution calculating means 10 first calculates the total area Ssum, which is the sum of the area S (i) of each polygon i and the area S (i) of the N polygons constituting the target model according to the following [Equation 1]. It is calculated by executing the above-mentioned processing (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 any integer from 0 to N−1. Next, an average point, which is an average of vertices, is calculated for each polygon constituting the target model (step S502). Specifically, a polygon average point (Xc (i), Yc (i), Zc (i)), which is an average point of each polygon, is calculated by executing processing according to the following [Equation 2]. .

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

  Next, the 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 the present embodiment, based on the order in the data array of the polygon model, a first group of polygons in which the remainder is 0 when divided by 3 and a second group of polygons in which the remainder is 1 when divided by 3 The polygons are divided into a group and a third group of polygons in which the remainder is 2 when divided by 3. As a result, the N polygons forming the polygon model are divided into N / 3 groups. If 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 first polygon 0 is overlapped with the last polygon N = polygon 0, and the total number of polygons is Correct so that it becomes 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 last and the last polygon N + 1 = polygon 1 It is corrected so that the total number of polygons becomes N + 2. Polygons are added redundantly so that the number of polygons in each group is the same, and even if the total number of polygons becomes N + 1 or N + 2, the number of polygons is replaced with N in the following. I will explain.

  By grouping according to 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 of each group is changed. The permutation is performed randomly. That is, it is shuffled. Any method may be used as long as the order of the polygon array can be changed. In the present embodiment, the order of the polygon array is changed by the following method. First, three types of random number arrays R1 (k), R2 (k), and R3 (k) are created. Specifically, for k = 0,..., N / 3-1, R1 (k) = k is initialized. Then, a real-valued uniform random number is generated N / 3 times in the range of 0 ≦ Rnd (k) <1, and every time it is generated, 0 ≦ Rnd (k) × N / 3 is calculated by an operation of pp = Rnd (k) × N / 3. A process of calculating an integer value of pp ≦ N / 3 and exchanging the value of R1 (k) and the value of R1 (pp) is repeated N / 3 times. As a result of the repetition, a random number array R1 (k) in which N / 3 consecutive integers are randomly replaced is obtained. Thus, for example, h = R1 (k) is given to an array G1 (h) of polygons of the first group, and G1 (R1 (k)) = 3R1 (k) (k = 0,..., As in N / 3-1), it is possible to obtain an array in which the order is changed. For the second random number array R2 (k), R2 (k) = R1 (N / 3-3-k). That is, the random number array R2 (k) is obtained by reversing the order from the top 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. Also, the random number array R3 (k) is set to k, and instead of a random number, it is set to a sequential value of k = 0,..., N / 3-1. In this way, by changing the order of the polygon arrangement, the arrangement order of the polygons in each group can be set at random from the polygons in the target model which is a three-dimensional space. 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 three polygons from being overlapped with each other when the triangles are selected at random. Although it is possible to sequentially form triangles by combining all the polygons constituting the polygon model, if the number of polygons is large, the number of combinations becomes enormous, which is not practical. Also, a method of selecting three polygons at random from all the polygons forming the polygon model without grouping to form a triangle may be considered, but the same triangle is generated with a certain probability. If the number of randomly extracted triangle samples is not sufficiently large, a biased frequency distribution may be calculated. As in the present embodiment, three polygons for forming a triangle are mutually overlapped by classifying the polygons into three groups, selecting one polygon from each group, and selecting a total of three polygons. It is possible to select efficiently without randomness.

  Next, initialization is performed (step S504). More specifically, the circumscribed circle distribution and the inscribed circle distribution to be calculated are initialized, and the loop counter is initialized. The circumcircle distribution calculated in the present embodiment is a distribution of the radius of a circumcircle of a triangle obtained by weighting the area of a polygon. The number of elements is MD, and each element md (md = 0,..., MD− The frequency of 1) is represented as Hd (md). Hd (md) is a one-dimensional array composed of a predetermined number of MD elements. The number of elements MD can be set to any value, but in the present embodiment, MD = 360. After preparing such a one-dimensional array, the frequency distribution calculation means 10 sets the initial value to Hd (md) = 0. The inscribed circle distribution calculated in the present embodiment is a distribution of the radius of the inscribed circle of a triangle obtained by weighting the area of the polygon. The number of elements is MA, and each element ma (ma = 0,..., The frequency of MA-1) is represented as Ha (ma). Ha (ma) is a one-dimensional array composed of a predetermined number MA of elements. The number of elements MA may be the same as or different from the number of elements MD. As the number of elements MA, an arbitrary value can be set. In the present embodiment, the number of elements is the same as the number of elements MD, and MA = 360. After preparing such a one-dimensional array, the frequency distribution calculation means 10 sets the initial value to Ha (ma) = 0. The initial value LC = 0 is set for the loop counter LC.

  Next, the frequency distribution calculating means 10 creates a triangle having the average point of each polygon between the three groups as a vertex (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. A point on a polygon means a point on a plane passing through all vertices of the polygon and located in 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. More specifically, G1 (h) in which the order of the h (h = 0,..., N / 3-1) th polygon G1 (h) of the first group is randomly changed by the random number array R1 (k). R1 (k)) average point (Xc (G1 (R1 (k))), Yc (G1 (R1 (k))), Zc (G1 (R1 (k)))), corresponding order of the second group The average points (Xc (G2 (R2 (k))), Yc (G2 (R2)) of G2 (R2 (k)) randomly rearranged by the random number array R2 (k) for the polygon G2 (h) of h (K))), Zc (G2 (R2 (k)))) and G3 (G3 (h) obtained by randomly changing the order of the polygon G3 (h) in the corresponding order h of the third group by the random number array R3 (k). R3 (k)) average point (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)) in which the order is randomly changed with respect to 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 a corresponding random number array R2 (k) for the polygon G3 (h) in the corresponding order h of the third group. Average points of G3 (R2 (k)) with random permutations (Xc (G3 (R2 (k))), Yc (G3 (R2 (k))), Zc (G3 (R2 (k))) , An average point (Xc (G1 (R3 (k)) of G1 (R3 (k)) obtained by randomly changing the order of the polygon G1 (h) of the corresponding order h of the first group by the random number array R3 (k). )), Yc (G1 (R3 (k))) and 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))), and a random number array R2 (k) with respect to the polygon G1 (h) in the corresponding order h of the first group. , The average points (Xc (G1 (R2 (k))), Yc (G1 (R2 (k))), Zc (G1 (R2 (k))) of G1 (R2 (k))) The average point (Xc (G2 (R3 (k))) of G2 (R3 (k)) in which the order is randomly rearranged by the random number array R3 (k) with respect to the polygon G2 (h) of the corresponding order h of the second group. ), Yc (G2 (R3 (k))) and 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 the circumscribed circle of the triangle having the polygon average point as the vertex (step S506). Specifically, first, by executing a process according to the following [Equation 3], the area S (k) of the first triangle in the range of k = 0,..., N / 3-1 is calculated. calculate.

[Equation 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. (DD12). (DD23). (DD31)] ^1/2

  In the above [Equation 3], “•” indicates multiplication. The same applies to the following [Equation]. 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]. .

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

  Similarly, the frequency distribution calculating means 10 performs the processing according to the following [Equation 5] to obtain the area S (2) of the second triangle within the range of k = 0,..., N / 3-1. k) is calculated.

[Equation 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. (DD12). (DD23). (DD31)] ^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] to thereby obtain the area S (3) of the third triangle in the range of k = 0,..., N / 3-1. k) is calculated.

[Equation 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. (DD12). (DD23). (DD31)] ^1/2

  Then, the radius D (k) of the circumscribed circle of the third triangle is calculated in the range of k = 0,..., N / 3-1 by executing the processing according to the above [Equation 4]. In the above [Equation 3], [Equation 5], and [Equation 6], the lengths D12, D23, and D31 of the sides of the triangle are circumscribed circles only for triangles satisfying all the six conditions shown in [Equation 7] below. The target of the frequency distribution of the radius. Therefore, a triangle that does not satisfy at least one of the six conditions shown in [Equation 7] is not subjected to the frequency distribution of the circumscribed circle radius. If the six conditions shown in the following [Equation 7] are not satisfied, the radius D (k) of the circumscribed circle becomes extremely large, and it is not possible to obtain an accurate frequency distribution.

[Equation 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 the above [Equation 7], the first three of the six conditions mean that all sides take positive values other than 0, that is, form a triangle. The latter three conditions out of the six conditions mean that the created triangle does not have an extremely flat shape. If the triangle has an extremely flat shape, the radius of the circumscribed circle approaches infinity, and a precise frequency distribution cannot be obtained. When the six conditions shown in the above [Equation 7] are not satisfied, no triangle is formed (if any of the sides is 0), or the radius D (k) of the circumscribed circle becomes extremely large, and the frequency with high accuracy is obtained. This is because the distribution cannot be obtained.

  The radius of the circumscribed circle of the N / 3 first triangles, the radius of the circumscribed circle of the N / 3 second triangles, and the circumscribed circle of the N / 3 third triangles calculated by the above [Equation 4] With respect to three types of N / 3 arrays D (k) having a radius of N, an offset of N / 3 is added to the N / 3 arrays of the second triangle, and an N / 3 array of the third triangle is added. Are offset by 2N / 3, are grouped by serial number i, and the range is reset to i = 0,..., N−1, and the circumscribed circle radii D (i) ( i = 0,..., N−1). Then, the largest one of the N circumscribed circle radii D (i) of i = 0,..., N−1 is defined as the maximum circumscribed circle radius Dmax.

  Next, the frequency distribution calculation unit 10 calculates the radius of the inscribed circle of the triangle having the vertex at the polygon average point (step S507). Specifically, by executing the processing according to the following [Equation 8], the first triangle, the second triangle, and the third triangle in the range of k = 0,. The radius A (k) of the tangent circle is calculated.

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

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

  The radius A (k) of the inscribed circle of N / 3 first triangles, the radius A (k) of the inscribed circle of N / 3 second triangles, and N For three N / 3 arrays A (k) of the radius A (k) of the inscribed circle of the third triangle, N / 3 arrays for the second triangle have N / 3 .., N−1, after adding an offset by 2N / 3 to the N / 3 arrays of the third triangle. The radius A (i) (i = 0,..., N−1) of the inscribed circle of the N triangles is obtained.

  Next, the frequency distribution calculation 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, by performing the processing according to the following [Equation 9], the frequency Hd (md) of the element md is calculated for i = 0,..., N−1.

[Equation 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
When 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 polygon p classified into three groups. The above [Equation 9] indicates that when the circumscribed circle radius D (i) of a triangle is equal to or larger than the maximum circumscribed circle radius Dmax multiplied 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).

  By executing the process according to the above [Equation 9] for N triangles, a circumscribed circle distribution Hd (md) (md = 0,..., MD-1) is calculated. Since the area of the polygon is added to each element, not just the frequency, the circumscribed circle distribution Hd (md) indicates an area-weighted circumscribed circle distribution. It is also possible to simply add 1 instead of the area, but in this case, the circumscribed circle distribution does not take the area of the polygon into account, and the case where the polygon configuration is coarse and the case where the polygon configuration is fine for the same polygon model are as follows: A noticeable difference occurs in the circumcircle distribution. Therefore, in the present embodiment, the area of the polygon is added when calculating the circumscribed circle distribution. If 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, which is a frequency distribution of the radius of the inscribed circle of each triangle (step S509). Specifically, by performing the processing according to the following [Equation 10], the frequency Ha (ma) of the element ma is calculated for i = 0,..., N−1.

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

  For the average area S of the three polygons in [Equation 10], the one calculated by the processing according to [Equation 9] is used. [Equation 10] indicates that when the radius A (i) of the inscribed circle of the triangle is equal to or larger than the maximum circumscribed circle radius Dmax multiplied 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 a triangle as an average point is added to Ha (ma). This is because, when Dmax / 2, which is half the length of the maximum circumscribed circle radius Dmax, is multiplied by ma / MA and smaller than (ma + 1) / MA, the vertex of the triangle is defined as the average point. This is the same as adding the average area S of the three polygons to Ha (ma).

  After all, by executing the processing according to the above [Equation 10], the frequency distribution calculating means 10 prepares a one-dimensional array composed of a predetermined number MA of elements, and calculates the maximum of the calculated radius of the circumscribed circle. The range normalized by 50% of the value (the 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 radius of the circumscribed circle and the radius of the inscribed circle depend on the scale of the polygon model, when 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 a distribution that is independently normalized with different maximum values for the circumscribed circle radius and the inscribed circle radius is created, a distribution that does not depend on the scale between the circumscribed circle radius and the inscribed circle radius can be obtained. While robustness against deformation is increased, shape discrimination is weakened. In the present invention, the inscribed circle radius is also created using a maximum circumscribed circle radius, which is the same reference value as the circumscribed circle radius, to create a normalized distribution, so that the scale between the circumscribed circle radius and the inscribed circle radius is set. The distribution with the ratio maintained is obtained, and the spread of the distribution is narrower and sharper than in the case where the normalization is independently performed at different maximum values for the circumscribed circle radius and the inscribed circle radius, and the shape discriminating property is improved. In practice, the radius of the inscribed circle does not take a value larger than 1/2 (50%) of the maximum value of the radius of the circumscribed circle (maximum radius of the circumscribed circle). To In the present embodiment, as shown in the above [Equation 10], normalization is performed using Dmax / 2, which is half the length of the maximum circumscribed circle radius Dmax. The range of normalization 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 concentrates on the maximum value of the element ma. The shape is not accurately reflected. If it exceeds 50%, a distribution with a large blank on the maximum value side of the element ma will be formed.

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

  After 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). If the result of the determination is that the loop counter LC is less than the specified value, processing is performed to shift the arrangement of the two groups by one (step S511). For example, when the order of the first group is fixed, in the present embodiment, a process according to the following [Equation 11] is executed, and the random number array R2 (k ), The order of the random number array R3 (k) is shifted.

[Equation 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,
Let R2 (N / 3-1) = Ro and R3 (N / 3-1) = Ro3.

  In the case where the order of the second group is fixed, and in the case where the order of the third group is fixed, the processing is performed according to [Equation 11] by changing the relationship between the groups. At the same time, a process of incrementing the loop counter LC (LC ← LC + 1) is also performed. If the frequency distribution already calculated exists, the frequency value corresponding to each element is added to each element of the frequency distribution. In step S511, after the arrangement of the two groups is shifted, the combination of the polygons of the two groups and the other group is changed from the first time, and the processing of steps S505 to S509 is executed to obtain the circumscribed circle distribution and the inner circle. Calculate the tangent distribution. The calculation process of the circumscribed circle distribution and the inscribed circle distribution in steps S505 to S509 is repeated a predetermined number of times set as a specified value. The prescribed value can be set as appropriate, but is about 10 when the total number of polygons N = 10000.

  That is, every time the circumcircle distribution and the inscribed circle distribution are calculated once, the process of changing the order of the polygons in two of the three groups and calculating the circumcircle distribution and the inscribed circle distribution again is performed. It will be done repeatedly. If the loop counter LC has reached the specified value, it is determined in step S510 that the loop counter LC is equal to or larger than the specified value, so that two types of frequency distributions are normalized (step S512).

  Specifically, the unit is normalized to the area ratio [%] by multiplying Hd (md) calculated in 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 sum Ssum of the area of the polygon and the number of loops LC. Thus, even if the polygon models have different total sums of the areas of the polygons, that is, even if the polygon models have different scales, the polygon models are converted into circumcircle distributions of the same scale, and can be determined as polygon models based on the same scale. In addition, since the division is performed by the number of loops LC, an average value for the number of loops is obtained.

  The unit is normalized to the area ratio [%] by multiplying Ha (ma) calculated by the above [Equation 10] and further multiplying 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 sum Ssum of the polygon area and the number of loops LC. As a result, even if the polygon models have different total sums of the areas of the polygons, that is, polygon models having different scales are converted into an inscribed circle distribution of the same scale, and it is possible to determine the polygon model based on the same scale. In addition, since the division is performed by the number of loops LC, an average value for the number of loops is obtained.

  After the two types of frequency distributions have been normalized, the actual maximum circumscribed circle radius is calculated (step S513). Specifically, the actual maximum circumscribed circle radius Wmax is calculated by executing the processing according to the following [Equation 12].

[Equation 12]
Wmax = Dmax · Sout

  In the above [Equation 12], Dmax is a maximum circumscribed circle radius, and Sout is output scale information. The actual maximum circumscribed circle radius Wmax is obtained by multiplying the maximum circumscribed circle radius Dmax by the output scale information Sout.

  In the present 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 the average point of the polygon as three vertices is created. The vertices of the triangle are preferably the average points of the vertices of the polygon, but may be triangles connecting predetermined points on the polygon other than the average points. As the predetermined point on the polygon, for example, any vertex constituting the polygon, the center of gravity of the polygon, a point having a value that is exactly intermediate between the maximum value and the minimum value of the vertex of the polygon for each xyz coordinate, etc. Can be used.

<3.3.2. Second method>
Next, the second method will be described. FIG. 7 is a flowchart illustrating details of the frequency distribution calculation processing according to the second technique. The same processes as those in the first method are denoted by the same reference numerals and description thereof will be omitted. Also in the second method, steps S501 to S509 are performed in the same manner as the first method. Then, as in step S512 of the first method, two types of frequency distributions are normalized (step S512). In the second method, normalization is forcibly performed after calculating two types of frequency distributions. Specifically, the unit is normalized to the area ratio [%] by multiplying Hd (md) calculated in 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] and further multiplying by 100 / Ssum and replacing it with Ha (ma). Unlike the first method, division by the loop counter LC is not performed.

  After the normalization of the circumscribed circle distribution and the inscribed circle distribution has been performed, it is next determined whether or not the loop counter LC is 1 or more (step S521). This is to determine whether or not the calculation of the frequency distribution is the first time. If the value of the loop counter LC is less than 1, it means that the calculation of the frequency distribution is the first time, so that the processing 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 the above [Equation 11], two of the arrays R1 (k), R2 (k), and R3 (k) are obtained. The sequence is out of order.

  In step S511, after the arrangement of the two groups is shifted, the combination of the polygons of the two groups and the other group is changed from the first time, and the processing of steps S505 to S509, S512, and S521 is executed, and the circumscription is performed. Normalizes the circle distribution and the inscribed circle distribution. That is, every time the circumcircle distribution and the inscribed circle distribution are calculated once, the process of changing the order of the polygons in two of the three groups and calculating the circumcircle distribution and the inscribed circle distribution again is performed. Repeat. In the second and subsequent times, the loop counter LC is determined to be 1 or more in step S521, so that the comparison with the immediately preceding circumscribed circle distribution and inscribed circle distribution is performed (step S522). First, the processing according to the following [Equation 13] is executed to calculate the Euclidean distance DD between the calculated circumcircle distribution and the immediately preceding circumcircle distribution. The Euclidean distance is a distance in the Euclidean space given by a value obtained by squaring the absolute value of the difference between the frequencies of the elements. The Euclidean distance DD is obtained as a distance between two points in the MD-dimensional Euclidean space.

[Equation 13]
DD = { _{md = 0, MD-1} [{Hd (md) -Hdp (md)} ² / MD] ^1/2

In the above [Equation 13], the suffix “md = 0, MD-1” of の is the following [...] ^1/2 of the case where md takes all integers from 0 to MD-1. Indicates that the sum is to be obtained. Hdp (md) indicates the circumcircle distribution immediately before.

  Subsequently, the Euclidean distance DA between the calculated inscribed circle distribution and the immediately preceding inscribed circle distribution is calculated by executing processing according to the following [Equation 14]. The Euclidean distance DA is obtained as a distance between two points in the MA-dimensional Euclidean space.

(Equation 14)
DA = { _{ma = 0, MA-1} [{Ha (ma) -Hap (ma)} ² / MA] ^1/2

In the above [Equation 14], the suffix “ma = 0, MA-1” of Σ represents the following [...] ^1/2 of the case where ma takes all integers from 0 to MA-1. Indicates that the sum is to be obtained. Hap (ma) indicates the immediately preceding inscribed circle distribution.

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

  If it is determined in step S523 that the two are in a saturated state, that is, if both are similar, the frequency distribution calculation unit 10 forms the last calculated circumcircle distribution and inscribed circle distribution into two types. It is determined to be a frequency distribution (step S524). That is, the circumcircle distribution and the inscribed circle distribution immediately after the calculation are compared with the circumcircle distribution and the inscribed circle distribution obtained immediately before the calculation, and if similarity is recognized in the comparison result, the circumcircle circle immediately after the calculation is obtained. The distribution and the inscribed circle distribution are defined as the circumscribed circle distribution and the inscribed circle distribution to be compared.

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

  After the two kinds of frequency distributions are determined, the actual size maximum circumscribed circle radius is calculated by executing the process according to the above [Equation 12], similarly to the first method (step S513).

  FIG. 8 shows the circumscribed circle and the inscribed circle in the circumscribed circle distribution and the inscribed circle distribution obtained from the target model. FIG. 8 shows a polygon model when the polygon is triangular. For 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 FIGS. 8A and 8B are the same. Then, a circumscribed circle and an inscribed circle of the triangle are obtained. FIG. 8A shows the relationship between a triangle, its circumscribed circle, and a radius, and FIG. 8B shows the relationship between a triangle, its inscribed circle, and a radius. 8A and 8B, downward arrows indicate the radius of the circumscribed circle and the radius of the inscribed circle, respectively.

In general, when a polygon model has a shape that is as close as possible to a sphere, the circumscribed circle of a triangle that connects the centers of three randomly selected polygons is a circle obtained by cutting the sphere with a plane formed by the three centers. Become. In other words, in the case of a sphere, the circumcircle radius often coincides with the radius of the sphere, and the circumcircle distribution has a form in which a peak appears at one place corresponding to the radius of the sphere. However, as the density of the polygon decreases and approaches the polyhedron, the circumcircle distribution increases in the direction of the shorter circumscribed circle radius, and as the polygon model deviates from the sphere, the circumcircle distribution becomes a complex form. Therefore, the circumcircle distribution using the circumcircle radius is extremely effective for identifying a 3D shape. On the other hand, the radius of the inscribed circle has various values even when the polygon model is a sphere, and since the inscribed circle distribution has a widely spread form, the robustness to the modification of the 3D shape is smaller than that of the circumscribed circle distribution. On the other hand, the discrimination of the shape becomes weaker. 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. Then, the inscribed circle distribution is locally biased in the direction of the radius 0, and the discriminability of the inscribed circle distribution is improved.

<3.4. Frequency Distribution Matching Process>
Next, the frequency distribution matching process in step S130 will be described. First, the frequency distribution matching means 13 determines the actual maximum size of the frequency distribution registered in the record re (re = 0,..., RE-1) from among the RE records stored in the regulation model database 40. Extract the circumcircle radius. The actual maximum circumscribed circle radius of the record re is represented by Wmaxo (re). Then, the frequency distribution matching unit 13 performs a process according to the following [Equation 15] to perform a matching process on the circumscribed circle distribution Hd (md) calculated from the target model.

(Equation 15)
MD ′ = MD · Wmax / Wmaxo (re)
MD1 = md · Wmax / Wmaxo (re)
MD2 = (md + 1) · Wmax / Wmaxo (re) −1
When md ≦ MD ′, Hd ′ (md) = Σ _{md = MD1, MD2} Hd (md)
Hd '(md) = 0 when MD'<md<MD

  As a result of executing the processing according to the above [Equation 15], the circumscribed circle distribution Hd ′ (md) subjected to the matching processing is obtained. Subsequently, the frequency distribution matching unit 13 performs a process according to the following [Equation 16] to perform a matching process on the inscribed circle distribution Ha (ma) calculated from the target model.

(Equation 16)
MA ′ = MA · Wmax / Wmaxo (re)
MA1 = maWmax / Wmaxo (re)
MA2 = (ma + 1) · Wmax / Wmaxo (re) −1
If md ≦ MA ′, Ha ′ (ma) = Σ _{md = MA1, MA2} Ha (ma)
If MA ′ <ma <MA, Ha ′ (ma) = 0

  As a result of executing the processing according to the above [Equation 16], the inscribed circle distribution Ha ′ (ma) subjected to the matching processing is obtained.

<3.5. Creation processing of emphasis component>
Next, the process of creating an emphasized component in step S150 will be described. First, the emphasis component creating means 15 performs an emphasis component creation process from the frequency distribution of the target model subjected to the matching process. Specifically, the emphasis component creating unit 15 corrects the matched circumscribed circle distribution Hd ′ (md) so as to fall within a predetermined range by executing a process according to the following [Equation 17]. To create an emphasis component.

(Equation 17)
Hmaxd = MAX _{md = 0, MD-1} Hd '(md)
Nd (md) = Hd ′ (md) / Hmaxd

As a result of executing the processing according to the above [Equation 17], a circumscribed circle distribution Nd (md) corrected to fall within a predetermined range is obtained. This is used as an emphasis component. In the first expression, MAX _{md = 0, MD-1} Hd '(md) indicates the maximum value of Hd' (md). Therefore, Hd '(md) may take 0 as the minimum value, and the emphasis component Nd (md) falls within a range of 0 or more and 1 or less as a predetermined range. Subsequently, the emphasis component creating means 15 corrects the matched inscribed circle distribution Ha ′ (ma) so as to fall within a predetermined range by executing processing according to the following [Equation 18]. , Create an emphasis component.

(Equation 18)
Hmaxa = MAX _{ma = 0, MA-1} Ha '(ma)
Na (ma) = Ha '(ma) / Hmaxa

As a result of executing the processing according to the above [Equation 18], an inscribed circle distribution Na (ma) corrected to fall within a predetermined range is obtained. This is used as an emphasis component. In the first expression, MAX _{ma = 0, MA-1} Ha '(ma) indicates the maximum value of Ha' (ma). Therefore, Ha ′ (ma) may take 0 as a minimum value, and the emphasized component Na (ma) falls within a range of 0 or more and 1 or less as a predetermined range.

<3.6. Emphasis processing of frequency distribution>
Next, the frequency distribution emphasizing process in step S170 will be described. First, the frequency distribution emphasizing means 17 performs emphasis processing on the frequency distribution of the matched target model and the frequency distribution stored in the regulation model database 40. Specifically, the frequency distribution emphasizing means 17 is first registered in the record re (re = 0,..., RE-1) from among the RE records stored in the regulation model database 40. As a frequency distribution, a circumscribed circle distribution Hdo (re, md) (md = 0,..., MD-1) and an inscribed circle distribution Hao (re, ma) (ma = 0,..., MA-1) Is extracted. Then, by executing the processing according to the following [Equation 19], the circumscribed circle distribution Hd '(md) of the matched target model and the circumscribed circle distribution Hdo of each record re stored in the regulation model database 40 are obtained. Enhancement processing is performed on (re, md) so that all values in the range of md = 0,..., MD-1 are updated.

[Equation 19]
Hd ″ (md) = Hd ′ (md) · Nd (md)
Hdo '(re, md) = Hdo (re, md) Nd (md)

  In the above [Equation 19], the circumscribed circle distribution Hd ′ (md) of the matched target model and the circumscribed circle distribution Hdo (re, md) of each record re stored in the regulation model database 40 are calculated as follows: .., MD-1 are multiplied by the emphasis component Nd (md). The emphasis component Nd (md) plays a role as a so-called window function that extracts only a specific section of data, and the above [Equation 19] means that a process of multiplying the window function is performed. As a result of executing the processing according to [Equation 19], the circumscribed circle distribution Hd ″ (md) of the emphasized target model and the circumscribed circle distribution Hdo ′ (re, md) of each emphasized regulated model are obtained. Obtained as an emphasis frequency distribution. Subsequently, the frequency distribution emphasizing means 17 executes the processing according to the following [Equation 20] to store the inscribed circle distribution Ha ′ (ma) of the matched target model in the regulation model database 40. Enhancement processing is performed on the inscribed circle distribution Hao (re, ma) of each record re to update all values in the range of ma = 0,..., MA-1.

[Equation 20]
Ha ″ (ma) = Ha ′ (ma) · Na (ma)
Hao '(re, ma) = Hao (re, ma) · Na (ma)

  In the above [Equation 20], the normalized inscribed circle distribution Ha ′ (ma) of the target model and the inscribed circle distribution Hao (re, ma) of each record re stored in the regulatory model database 40 are respectively given. On the other hand, a process of multiplying all the values in the range of ma = 0,..., MA-1 by the emphasis component Na (ma) is performed. The emphasis component Na (ma) plays a role as a so-called window function that extracts only a specific section of the data, and the above [Equation 20] performs a process of multiplying the window function as in the above [Equation 19]. Will be. As a result of executing the processing in accordance with [Equation 20], the inscribed circle distribution Ha ″ (ma) of the emphasized target model and the inscribed circle distribution Hao ′ (re, ma) of each emphasized regulated model are obtained. ) Is obtained as an emphasis frequency distribution.

<3.7. Frequency Distribution Matching Process>
Next, the frequency distribution collation processing in step S200 will be described. FIG. 9 is a flowchart showing the frequency distribution collation processing. First, the frequency distribution matching unit 20 acquires a frequency distribution (emphasized frequency distribution) extracted from the regulation model database 40 and subjected to enhancement processing (step S610). The circumscribed circle distribution Hdo ′ (re, md) (md = 0,..., MD-1) and the inscribed circle distribution Hao ′ (re, ma) (ma = 0,.・, MA-1). In the frequency distribution collation processing in step S200, the emphasis frequency distributions Hd ″ (md) and Hdo ′ (re, md) calculated in the above [Equation 19] and [Equation 20] for simplicity of explanation. , Ha ″ (ma), and Hao ′ (re, ma) are treated as Hd (md), Hdo (re, md), Ha (ma), and Hao (re, ma), respectively.

  Next, the frequency distribution matching unit 20 calculates a peak position difference between the circumscribed circle distribution of the emphasized target model and the circumscribed circle distribution of the regulated model extracted from the regulation model database 40 and enhanced (step S1). S620). The peak position correlation indicates a correlation between peak positions, which are positions having the highest frequency in the frequency distribution. The frequency distribution matching unit 20 performs the processing according to the following [Equation 21], and thereby, the circumscribed circle distribution Hd (md) of the emphasized target model and the circumscribed circle distribution Hdo (Hdo ( The peak position correlation Pd (re) of (re, md) is calculated.

[Equation 21]
Pd (re) = {MD / 4- | mdM-mdoM (re) |} 400 / MD

  In the above [Equation 21], mdM is the value of md when Hd (md) is maximum, and mdoM (re) is the value of md when Hdo (re, md) is maximum, that is, the peak position. is there. | MdM−mdoM (re) | is a peak position difference indicating a difference between peak positions. When mdM and mdoM (re) match, the peak position difference | mdM−mdoM (re) | = 0 and the peak position correlation Pd (re) = 100, and when the mdM and mdoM (re) are farthest apart, theoretically , Peak position difference | mdM−mdoM (re) | = MD−1 and the peak position correlation is approximately Pd (re) −300, so that the peak position correlation Pd (re) is theoretically −300 <Pd ( re) takes a value of ≦ 100. However, empirically, since the peak position difference | mdM−mdoM (re) | rarely exceeds MD / 2, the peak position correlation Pd (re) is usually -100 <Pd (re) ≦ 100. Take a value. The peak position correlation Pd (re) is a value that increases as the peak position difference | mdM-mdoM (re) | decreases, and is determined based on the peak position difference.

  Next, the frequency distribution matching unit 20 compares the peak position correlation Pd (re) of the circumscribed circle distribution calculated in step S620 with the determination threshold (step S630). If the peak position correlation Pd (re) is greater than or equal to the determination threshold, it is determined to be suitable, and if the peak position correlation Pd (re) is smaller than the determination threshold, it is determined to be non-conforming. The determination threshold can be arbitrarily set as long as it is a positive value, but is +50.0 here.

  If it is determined in step S630 that there is a match, the frequency distribution matching unit 20 determines the inscribed circle distribution of the emphasized target model and the inscribed circle of the regulated model extracted from the regulated model database 40 and emphasized. A peak position correlation is calculated between the distributions (step S640). The frequency distribution matching unit 20 executes the processing according to the following [Equation 22] to thereby obtain the inscribed circle distribution Ha (ma) of the emphasized target model and the inscribed circle distribution Ha of the emphasized regulated model. The peak position correlation Pa (re) of Hao (re, ma) is calculated.

(Equation 22)
Pa (re) = {MA / 4- | maM-maoM (re) |} .400 / MA

  In the above [Equation 22], maM is the value of ma when Ha (ma) is maximum, and maoM (re) is the value of ma when Hao (re, ma) is maximum, that is, the peak position. is there. | MaM−maoM (re) | is a peak position difference indicating a difference between peak positions. When maM and maoM (re) match, the peak position difference | maM-maoM (re) | = 0 and the peak position correlation Pa (re) = 100, and when maM and maoM (re) are farthest apart, theoretically , Peak position difference | maM−maoM (re) | = MA−1 and the peak position correlation is approximately Pa (re) −300, so that the peak position correlation Pa (re) is theoretically −300 <Pa ( re) takes a value of ≦ 100. However, empirically, since the peak position difference | maM−maoM (re) | rarely exceeds MA / 2, the peak position correlation Pa (re) is usually -100 <Pa (re) ≦ 100. Take a value. The peak position correlation Pa (re) is a value that increases as the peak position difference | mdaM−maoM (re) | decreases, and is determined based on the peak position difference.

  Next, the frequency distribution matching unit 20 compares the peak position correlation Pa (re) of the inscribed circle distribution calculated in step S640 with the determination threshold (step S650). If the peak position correlation Pa (re) is greater than or equal to the determination threshold, it is determined to be suitable, and if the peak position correlation Pa (re) is smaller than the determination threshold, it is determined to be non-compliant. The determination threshold can be arbitrarily set as long as it is a positive value, but is +50.0 here. If it is determined in step S650 that the target model is compatible, 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 match. Do. FIG. 10 is a diagram illustrating a relationship between a frequency distribution (histogram) and a peak position. In FIG. 10, the left side shows the regulation model, and the right side shows the target model. The upper part shows a circumscribed circle distribution, and the lower part shows an inscribed circle distribution.

  If it is determined in any of Steps S630 and S650 that there is no match, the frequency distribution matching unit 20 finishes matching the target model with the regulation model of the record re, and shifts to matching with the regulation model of the next record re + 1. . The process according to the flowchart of FIG. 9 is executed for all records in the regulation model database 40, and if there is at least one conforming regulation model, the target model is set to “output should be regulated (output unsuitable). ) ", The collation processing in step S200 is terminated.

<3.7.1. Frequency Distribution Matching Process>
In the above embodiment, the peak position correlations of the frequency distribution are collated, but the collation may be performed using another index. Here, a case where a correlation coefficient is used instead of the peak position correlation will be described as an example.

  In this case, 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 distribution of the regulation model extracted from the regulation 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 degree of similarity between the target model and the regulatory model. First, by executing processing according to the following [Equation 23], the average value Ad of the circumcircle distribution Hd (md) of the target model and the average value Ado (of the circumcircle distribution Hdo (re, md) of the regulated model are obtained. re) is calculated.

(Equation 23)
Ad = Σ _{md = 0, MD-1} Hd (md) / MD
Ado (re) = Σ _{md = 0, MD-1} Hdo (re, md) / MD

  Subsequently, the standard deviation Sd of the circumscribed circle distribution Hd (md) of the target model and the standard deviation Sdo of the circumscribed circle distribution Hdo (re, md) of the regulated model are obtained by executing processing according to the following [Equation 24]. (Re) is calculated.

(Equation 24)
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 in ｛｝ in [Equation 24]. 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 numerator of the numerator in [Equation 25] described later, and the numerator and the denominator overlap. 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 to perform processing according to the following [Equation 25], the circumcircle distribution of the target model and the circumcircle distribution of the regulatory model corresponding to the record re are obtained. A correlation coefficient Cd (re) is calculated.

(Equation 25)
Cd (re) = [Σ _{md = 0, MD-1} (Hd (md) −Ad) · (Hdo (re, md) −Ado (re))] / (Sdo (re) · Sd)

  In the above [Equation 25], 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 Cd (re) is obtained by dividing the covariance of the frequency distributions Hd (md) and Hdo (re, md) by their respective standard deviations. Performing the division of the square root of the denominator twice as in the calculation of [Equation 25] above generally requires a high processing load. For this reason, in the field of image processing, one that requires only the numerator product-sum operation is referred to as “correlation coefficient”, and Cd (re) shown in the above [Equation 25] is referred to as “normalized correlation coefficient”. There are also customs.

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

  If it is determined in step S630 that there is a match, the frequency distribution matching unit 20 calculates a correlation coefficient between each element in the inscribed circle distribution of the target model and the inscribed circle distribution of the regulation model (step S640). ). First, by executing the processing according to the following [Equation 26], the average value Aa of the inscribed circle distribution Ha (ma) of the target model and the average value of the inscribed circle distribution Hao (re, ma) of the regulated model are obtained. Aao (re) is calculated.

(Equation 26)
Aa = Σ _{ma = 0, MA-1} Ha (ma) / MA
Aao (re) = Σ _{ma = 0, MA-1} Hao (re, ma) / MA

  Subsequently, the standard deviation Sa of the inscribed circle distribution Ha (ma) of the target model and the standard deviation Sa of the inscribed circle distribution Hao (re, ma) of the regulated model are obtained by executing the processing according to the following [Equation 27]. The deviation Sao (re) is calculated.

(Equation 27)
Sa = [Σ _{ma = 0, MA-1} (Ha (ma) −Aa) ² ] ^1/2
Sao (re) = [Σ _{ma = 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 in the above [Equation 27]. However, here, since the purpose is to calculate the correlation coefficient, it is necessary to divide by the constant MA also in the term for calculating the numerator of the numerator in [Equation 28] to be described later. To cancel the operation of dividing by the constant MA (the denominator is divided by the square root of the constant MA twice).

  Next, using the calculated average value and standard deviation, a process according to the following [Equation 28] is executed, whereby the inscribed circle distribution of the target model and the inscribed circle of the regulatory model corresponding to the record re are obtained. The correlation coefficient Ca (re) of the distribution is calculated.

(Equation 28)
Ca (re) = [Σ _{ma = 0, MA-1} (Ha (ma) −Aa) · (Hao (re, ma) −Aao (re))] / (Sao (re) · Sa)

  In the above [Equation 28], the suffix “ma = 0, MA-1” of Σ indicates that the sum is obtained when ma takes all the integers from 0 to MA-1. The correlation coefficient Ca (re) is obtained by dividing the covariance of the frequency distributions Ha (ma) and Hao (re, ma) by their respective standard deviations. Performing the division of the square root of the denominator twice as in the calculation of [Equation 28] above generally requires a high processing load. For this reason, in the field of image processing, what only needs to be performed by the numerator product-sum operation is called “correlation coefficient”, and Ca (re) shown in the above [Equation 28] is called “normalized correlation coefficient”. There are also customs.

  Next, the frequency distribution matching unit 20 compares the correlation coefficient Ca (re) of the inscribed circle distribution calculated in step S640 with the determination threshold (step S650). If the correlation coefficient Ca (re) is equal to or greater than the determination threshold, it is determined to be suitable. If the correlation coefficient Ca (re) is smaller than the determination threshold, it is determined to be non-compliant. The determination threshold can be set arbitrarily as long as it is a positive value. In this case, the correlation coefficient value in the range of +5.0 (−1 to +1) is multiplied by 100 and expressed in% dimension. ). If it is determined in step S650 that the target model is compatible, 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 match. Do.

  As described above, even if another index such as a correlation coefficient is used, it is possible to accurately determine whether the output is appropriate. However, by using the peak position correlation, it is possible to determine output suitability with higher accuracy.

<4.3 Data output to 3D printer>
In step S200, when the frequency distribution matching unit 20 determines that "output should not be restricted (output is appropriate)", the target model is output to the 3D printer 7 which is a three-dimensional object forming apparatus. That is, the target model, which is a polygon model permitted to be output by the three-dimensional object modeling data output control device, is output to the three-dimensional object forming device that models the three-dimensional object. On the other hand, when the frequency distribution matching unit 20 determines that “output should be regulated (output is inappropriate)”, the target model is not output to the 3D printer 7 which is a three-dimensional object forming apparatus. If it takes time to determine whether the output is appropriate or inappropriate, the target model is output to the 3D printer 7, and the output is determined to be appropriate or inappropriate in parallel with the output process (three-dimensional object forming process) of the 3D printer 7. May be determined, and an output stop instruction may be output to the 3D printer 7 when the output is inappropriate. At this time, from the user's point of view, it is only possible to confirm that the three-dimensional object forming process in the 3D printer is started by executing a single command of outputting the target model, and to determine in parallel whether the output is appropriate or inappropriate. It is not noticed that the execution of the process for is started.

<5. About frequency distribution calculation and registration of regulatory model>
As a modified example, the circumscribed circle distribution and the inscribed circle distribution calculated for the regulation model are converted to the three-dimensional object modeling data at a place different from the three-dimensional object modeling data output control device including the frequency distribution matching means 20. The information may be transmitted to the output regulating device via a network and registered.

  FIG. 11 is a hardware configuration diagram of a three-dimensional object forming system including a three-dimensional object forming data output control device according to a modification. In the three-dimensional object modeling system shown in FIG. 11, the three-dimensional object modeling data output restricting device 101 has the same configuration as the three-dimensional object modeling data output restricting device 100 shown in FIG. 2 for communicating with a public network such as the Internet. The configuration includes a network communication unit 8. The regulation model frequency distribution calculation device 102 can be realized by a general-purpose computer, and as shown in FIG. 11, a CPU (Central Processing Unit) 1a, a RAM (Random Access Memory) 2a which is a main memory of the computer, A large-capacity storage device 3a such as a hard disk and a 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 as 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 modeling data output regulation device 101 and the network communication unit 8a of the regulation model frequency distribution calculation device 102 communicate with each other, and the three-dimensional object modeling data output regulation device 101 is transmitted from the regulation model frequency distribution calculation device 102. It is possible to transmit the two types of frequency distributions calculated for the regulation model.

  In FIG. 11, the three-dimensional object modeling data output restricting device 101 and the 3D printer 7 are shown in a separated form. However, most of the 3D printer products currently on the market are provided with the three-dimensional object modeling data output restricting device 101. Constituent elements: CPU1, RAM2, storage device 3, key input I / F4 (not a keyboard / mouse for general-purpose computer, but several buttons of numeric keypad level), data input / output I / F5, display unit 6 (several lines) Are often used as key input I / Fs 4 by superimposing a touch panel and a network communication unit 8 (wireless LAN function). Therefore, it is possible for the 3D printer itself to directly receive the data for forming a three-dimensional object via an external storage medium or the Internet and to form a three-dimensional object by itself (especially for a consumer 3D printer, this form is more suitable). Many). That is, the data output restricting device 101 for three-dimensional object modeling and the 3D printer 7 shown in FIG. 11 are often housed in one housing and distributed as a product called “3D printer”.

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

<6. Cloud-based 3D object modeling system>
The present invention can also be applied to a cloud type three-dimensional object forming system. FIG. 12 is a hardware configuration diagram of a cloud type three-dimensional object forming system. In the three-dimensional object modeling system illustrated in FIG. 12, the output control terminal 201 and the processing server 202 implement a three-dimensional object modeling data output control device. In the three-dimensional object forming system shown in FIG. 12, the output control terminal 201 has the same hardware configuration as the computer shown as the three-dimensional object forming data output control device 101 in FIG. That is, the output control terminal 201 includes the CPU 11, the 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, and is 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. 12, 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 can transmit output propriety data from the processing server 202 to the output control terminal 201 based on determination of output propriety. It is possible. In FIG. 12, the output control terminal 201 and the 3D printer 7 are shown in a separated form. However, as in the example of FIG. 11, the components of the output control terminal 201 such as the CPU 11, the 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 redundantly provided.

  FIG. 13 is a functional block diagram of a cloud type three-dimensional object forming system. The processing server 202 constituting the cloud type three-dimensional object forming system includes a target model receiving unit 50 and an output propriety data transmitting unit 60 in addition to the units of the three-dimensional object forming data output control device shown in FIG. It has a configuration. The target model storage means 31 stores not the target model itself but two types of frequency distributions calculated for the target model. Further, the frequency distribution calculation means 10 has a configuration in which the output control terminal 201 is provided instead of the processing server 202.

  Components having the same functions as those of the three-dimensional object modeling data output control device shown in FIG. 3 are denoted by the same reference numerals, and description thereof is omitted. The target model receiving unit 50 is a unit that receives the frequency distribution of the target model transmitted from the output control terminal 201 and registers it in the target model storage unit 31. The CPU 11a executes a predetermined program and a network communication unit. 18a is controlled. The output propriety data transmitting unit 60 is a unit that transmits output propriety data to the output control terminal 201 based on “whether or not output should be regulated” determined by the frequency distribution matching unit 20. 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 or not the output should be regulated not by the output side that outputs the three-dimensional object by the 3D printer but by a remote computer via a network from the output side. Means to do. In conventional server-type computers, if many users' access is concentrated, responsiveness becomes slow and users are often inconvenienced. In the cloud type, the physical configuration of computers is dynamically changed by virtualization technology This makes it possible to maintain stable responsiveness at all times. Generally, the processing server 202 is physically realized by a plurality of computers.

  The processing operation of the cloud type three-dimensional object forming system shown in FIGS. 12 and 13 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. Model ID, which is identification information to be assigned. Then, in the CPU 11, the frequency distribution calculating means 10 performs processing on the target model to which the model ID has been given according to the flowchart shown in FIG. 6 or FIG. 7, and is configured with a circumscribed circle distribution and an inscribed circle distribution. Generate a frequency distribution. Further, the CPU 11 transmits the generated frequency distribution via the network communication unit 18 to an address such as a URL set in the storage device 13 in advance. By adopting a method of transmitting a frequency distribution whose information amount is significantly smaller than that of polygon data, not only transmission time is significantly reduced, but also polygon data as 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 has been 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 the printer queues as an output job. At this time, a method may be adopted in which only the process of converting the polygon format data, which is the pre-processing in the 3D printer output, to the stacked format data is executed, and a standby state is set when the data is converted to the stacked format data. . Since this data processing load is relatively high, if the output propriety determination is completed during this time, it is possible to prevent the user from feeling 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, the frequency distribution is stored in the target model storage unit 31. Here, according to the flowchart shown in FIG. 4, the frequency distribution matching unit 13, the emphasis component creating unit 15, the frequency distribution emphasizing unit 17, and the frequency distribution matching unit 20 perform processing and output the target model corresponding to the received frequency distribution. Judge the suitability. Output propriety data as a result of output propriety is passed from the frequency distribution matching means 20 to the output propriety data transmitting means 60. Then, the output propriety data transmitting means 60 transmits the output propriety data added with the model ID to the output control terminal 201 which 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 transmits 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 with the model ID assigned to the received output suitability data. Then, when the output propriety data is “proper (proper output)”, 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 propriety data is “No (output improper)”, the data processing unit 7a discards the output job. That is, it is deleted from the printer queue.

<7. Verification example>
14 to 25 show examples of collation of the entire model and its parts. The entire model corresponds to the aforementioned regulatory model, and the parts of the model correspond to the aforementioned target model. 14, FIG. 18, FIG. 18, FIG. 20, FIG. 22, and FIG. 24 show the case where the matching processing of the frequency distribution of the target model on the part side is not performed, and FIG. FIGS. 23 and 25 show the result of the matching process of the frequency distribution of the target model. In FIGS. 14 to 25, for each of the six cases, cases in which the matching process is not performed are referred to as cases 1A, 2A, 3A, 4A, 5A, and 6A, and cases in which the matching process is performed are cases 1B, 2B, and 3B. 4B, 5B, and 6B. In the figure, the values of Corr (D, A) and Peak (D, A) described in the middle part of two places are before the enhancement processing (described in the middle of the middle part) and after the enhancement processing is performed (middle part). At the right end of the table) shows the matching result between the frequency distributions shown up and down, Corr (D, A) shows the correlation coefficients Cd (re) and Ca (re), respectively, and Peak (D, A) shows , And peak position correlations Pd (re) and Pa (re), respectively. The value of D indicates the result of collation of the inscribed circle distribution, and the value of A indicates the result of collation of the inscribed circle distribution. Before performing the matching process, the result of matching with a different part that should be determined to be non-conforming is shown, and by performing the matching process, there may be little difference in the correlation coefficient or conversely, the correlation may be low. When the peak position correlation is used, the difference between before and after the matching process is increased and becomes a positive side. This is because the regulated model that has undergone the matching process contains components of other component models that are not included in the target model.If there are many models of other component components, it is better to perform the matching process. The correlation coefficient decreases. On the other hand, the peak position correlation is relatively unaffected by the components of other component models. In addition, it can be seen that the difference in peak position correlation between before and after the matching process is further increased by performing the enhancement process as to whether or not to perform the enhancement process, so that highly accurate collation can be performed. This is because although the regulated model that has undergone the matching process includes components of other component models that are not included in the target model, by performing the enhancement process, components of other component models that are not included in the target model are included. Is suppressed.

  The three-dimensional object modeling data output regulation device 100 not only determines the conformity / non-conformity, but also calculates the peak position correlation, the correlation coefficient, and the like calculated by the frequency distribution matching unit 20 in steps S620 and S640 shown in FIG. The display unit 6 can also output. By visually confirming the peak position correlation and 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 the non-conformity. .

<8. Modifications>
The preferred embodiment of the present invention has been described above, but the present invention is not limited to the above embodiment, and various modifications are possible. For example, in the above embodiment, the polygon to be processed has a triangular shape, but may be a polygonal shape of a quadrangle or more.

  In the above embodiment, the polygon average point having the average coordinates of each vertex of the polygon is used as one point on the polygon for forming the triangle. However, if the point is on a 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 exactly halfway between the maximum value and the minimum value of the vertex of the polygon for each xyz coordinate, or the like can be used.

  Further, in the above embodiment, the emphasis component in which the value of each frequency is corrected to fall within a predetermined range with respect to the frequency distribution of the matched target model is created, and the original frequency distribution is multiplied by the emphasis component. Then, the emphasis frequency distribution of the target model and the emphasis frequency distribution of the regulation model are collated, but without such emphasis processing, the frequency distribution of the matched target model and the regulation model extracted from the database are compared. The frequency distribution may be collated.

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 input / output I / F
6, 6a, 16, 16a Display unit 7 3D printer (solid object forming device)
7a Data processing unit 7b Output unit 8, 8a, 18, 18a Network communication unit 10 Frequency distribution calculating means (frequency distribution calculating device)
13: Frequency distribution matching means 15: Emphasis component creation means 17: Frequency distribution emphasis means 20: Frequency distribution matching means 30, 31, Target model storage means 40: Regulatory model database 50 ··· Target model receiving means 60 ··· Output propriety data transmitting means 100 and 101 ··· Three-dimensional object modeling data output regulation device 102 ··· Regulated model frequency distribution calculation device 201 ··· Output control terminal 202 · ..Processing servers

Claims (18)

When outputting a polygon model represented as a set of polygons to a three-dimensional object forming apparatus as three-dimensional object forming data, the device determines whether or not to be restricted,
A database in which a frequency distribution of parameters characterizing a triangle formed based on three polygons in the regulation model which is a polygon model whose output is to be regulated is registered;
For a target model, which is an output target polygon model, a parameter that characterizes a triangle formed by predetermined points on three polygons selected from the target model is calculated, and a frequency distribution of the parameter is calculated. Distribution calculation means;
A process of matching the scale of the parameter of the frequency distribution of the target model with the scale of the parameter of the frequency distribution of the regulatory model registered in the database, and generating a frequency distribution of the matched target model. Distribution matching means;
The frequency distribution of the matched object model, against the frequency distribution of the regulating model, have a, and determining the frequency distribution verification means whether to regulate the output,
As a parameter characterizing the triangle, two kinds of parameters, a first parameter and a second parameter, are used, and the frequency distribution is a first frequency distribution corresponding to a first parameter and a second frequency corresponding to a second parameter. Two kinds of frequency distributions of the distribution, the radius of the circumscribed circle of the triangle is used as the first parameter, the radius of the inscribed circle of the triangle is used as the second parameter, and the circumscribed circle is used as the first frequency distribution. A circumscribed circle distribution that is a frequency distribution of the radius of the inscribed circle, and an inscribed circle distribution that is a frequency distribution of the radius of the inscribed circle is used as the second frequency distribution . Enhancement component creation means for creating an enhancement component in which the value of each frequency is corrected to fall within a predetermined range with respect to the frequency distribution of the matched target model,
A frequency distribution emphasis unit that multiplies the frequency distribution of the matched target model and the frequency distribution of the regulation model by the emphasis component to create an emphasis frequency distribution of the target model and an emphasis frequency distribution of the regulation model, In addition,
2. The three-dimensional object modeling data output restricting device according to claim 1, wherein the frequency distribution matching unit compares the enhancement frequency distribution of the target model with the enhancement frequency distribution of the regulation model. 3.   The frequency distribution calculation means classifies the polygons in the target model into three groups of a first group, a second group, and a third group with respect to the target model, and selects one polygon from each group. The three-dimensional object modeling data output restricting device according to claim 1 or 2, wherein the three polygons are selected.   When the number of polygons in each group is set to N / 3, the frequency distribution calculating means creates a random number array in which N / 3 consecutive integers are randomly replaced, and moves from the top to the end of the random number array. Of the three groups, the random number array is applied to one of the three groups, and the inverted random number array is applied to the other group to change the order of the polygons in the group. 4. The three-dimensional object modeling data output restriction device according to claim 3, wherein the polygons are selected sequentially from the top of each group after the replacement. The frequency distribution calculating means,
The random number array is applied to the polygons of the first group, and the inverted random number array is applied to the polygons of the second group to change the order, and the polygons are selected without changing the order of the polygons in the third group. Form N / 3 triangles,
The random number array is applied to the polygons of the second group, and the inverted random number array is applied to the polygons of the third group to change the order, and the polygons are selected without changing the order of the polygons of the first group. Form N / 3 triangles,
The random number array is applied to the polygons of the third group, and the inverted random number array is applied to the polygons of the first group to change the order, and the polygons are selected without changing the order of the polygons of the second group. The three-dimensional object modeling data output restricting device according to claim 4, wherein N / 3 triangles are formed. The frequency distribution calculating means,
Each time the frequency distribution is calculated once, the process of re-calculating the frequency distribution by changing the order of the polygons in two of the three groups is repeated a predetermined number of times. The three-dimensional object modeling data output restriction device according to claim 4 or 5, wherein the average value of the distribution is a frequency distribution to be collated. The frequency distribution calculating means,
Each time the frequency distribution is calculated once, the order of the polygons in two of the three groups is changed, and the process of calculating the frequency distribution again is repeated.
The frequency distribution immediately after calculation and the frequency distribution obtained immediately before are compared, and when similarity is recognized in the comparison result, the frequency distribution immediately after calculation is set as the frequency distribution to be compared. The three-dimensional object modeling data output restricting device according to claim 4 or 5, wherein:   In calculating the frequency distribution, the frequency distribution calculation unit prepares a one-dimensional array including a predetermined number of elements, and equally divides the range of the maximum value of the calculated parameter into the predetermined number. The frequency distribution is calculated by assigning each parameter to one of the predetermined number of elements based on the value of the parameter, and counting the number corresponding to the element. The three-dimensional object modeling data output control device according to 7.   9. The three-dimensional object modeling data output according to claim 8, wherein the frequency distribution calculating means weights the average of the areas of the selected three polygons when counting the number corresponding to the element. Regulatory devices.   10. The three-dimensional object modeling data output control device according to claim 9, wherein the frequency distribution calculating means divides the value of each of the elements by a total value of areas of polygons constituting each of the target models. In calculating the inscribed circle distribution, the frequency distribution calculation means prepares a one-dimensional array composed of a predetermined number of elements, and 35% to 50% of the calculated maximum value of the radius of the circumscribed circle. After equally dividing the range normalized by the value into the predetermined number, the radius of each inscribed circle is assigned to any of the predetermined number of elements based on the value of the radius of the inscribed circle, The three-dimensional object modeling data output according to any one of claims 1 to 10 , wherein the inscribed circle distribution is calculated by counting a number corresponding to the element. Regulatory devices. The frequency distribution matching unit calculates a peak position that gives a maximum value when comparing the frequency distribution of the matched target model with the frequency distribution of the regulated model, and based on a difference between the calculated peak positions. The three-dimensional object modeling data output restriction device according to any one of claims 1 to 11 , wherein it is determined whether the output should be restricted by the output device. For the regulation model, a regulation model frequency distribution calculation device that calculates a parameter characterizing a triangle formed by predetermined points on three polygons selected from the regulation model and calculates a frequency distribution of the parameter. receiving a calculated frequency distribution, three-dimensional object modeling data output according to the frequency distribution received from claim 1, characterized by further comprising a registering means for registering in the database in any one of claims 12 Regulatory devices. Means for outputting the target model to a connected three-dimensional object forming 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 performed by the three-dimensional object modeling device, the three-dimensional object modeling device includes, Means for outputting an output stop instruction;
The three-dimensional object modeling data output control device according to any one of claims 1 to 13 , further comprising: An output control terminal and a processing server connected via a network,
The output control terminal includes the frequency distribution calculation unit,
The processing server,
Said database;
Receiving means for receiving the frequency distribution of the target model from the output control terminal via a network,
Said frequency distribution matching means,
Output suitability data transmitting means for transmitting data based on whether or not output should be regulated, determined by the frequency distribution matching means, to the output control terminal,
The three-dimensional object modeling data output control device according to any one of claims 1 to 14 , comprising: A three-dimensional object modeling data output restriction device according to any one of claims 1 to 15 ,
A three-dimensional object modeling device that models a three-dimensional object using a polygon model permitted to be output by the three-dimensional object modeling data output control device,
A three-dimensional object forming system, comprising: When outputting a polygon model represented as a set of polygons to a three-dimensional object forming apparatus as three-dimensional object forming data, a device that creates a frequency distribution based on the polygon model in order to determine whether or not the restriction is required. So,
For a target model which is an output target polygon model, a parameter characterizing a triangle formed by predetermined points on three polygons selected from the target model is calculated, and a frequency distribution of the parameter is calculated .
As a parameter characterizing the triangle, two kinds of parameters, a first parameter and a second parameter, are used, and the frequency distribution is a first frequency distribution corresponding to a first parameter and a second frequency corresponding to a second parameter. Two kinds of frequency distributions of the distribution, the radius of the circumscribed circle of the triangle is used as the first parameter, the radius of the inscribed circle of the triangle is used as the second parameter, and the circumscribed circle is used as the first frequency distribution. radius of power and the circumscribed circle distribution using a distribution, wherein the radius of the frequency distribution frequency distribution calculating apparatus Ru with inscribed circle distribution is of the inscribed circle as the second frequency distribution of. A program for causing a computer to function as the three-dimensional object modeling data output control device according to any one of claims 1 to 15 .