JP2016151997A - Three-dimensional object shaping data output control apparatus - Google Patents

Abstract

PROBLEM TO BE SOLVED: To provide a three-dimensional object shaping data output control apparatus that is used in outputting a three-dimensional object on the basis of a polygon model and can accurately determine whether to control an output on the basis of a reference two-dimensional image.SOLUTION: An embodiment comprises: a frequency distribution database 50, in which a distance distribution and an angle distribution are registered that represent characteristics of each control image, which is an image whose output is to be controlled; model frequency distribution calculation means 10 to calculate, for a target model being a polygon model to be output, a distance from a reference point of the target model to each polygon constituting the target model and an angle specific to each polygon, calculate a distance distribution being a frequency distribution of the calculated distances to the individual polygons, and calculate an angle distribution being a frequency distribution of the calculated angles specific to the individual polygons; and frequency distribution checking means 30 to check the calculated distance distribution and angle distribution respectively against the distance distributions and angle distributions of the control images registered in the frequency distribution database 50 so as to determine whether to control the output.SELECTED DRAWING: Figure 10

Description

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

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

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

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

  On the other hand, a method of constructing a system that determines whether or not output is possible using a method similar to that of an anti-virus tool is conceivable, and a technique for matching polygons is used for realizing the system. However, not only the three-dimensional polygon model but also the three-dimensional representation of two-dimensional representations such as illustrations and photographs should be included as targets to be regulated by the 3D printer. is there. In order to cope with this, a technique for matching with a three-dimensional polygon model based on a two-dimensional image has been developed (see Patent Documents 1 to 3).

Japanese Patent No. 4709668 Japanese Patent No. 5278881 Japanese Patent No. 5560925

  However, in each of the above conventional techniques, an image is generated by projecting a three-dimensional polygon model in two dimensions, and a feature vector obtained from this image is used for collation. Not determined. For this reason, there is a problem that it is difficult to determine the appropriateness of output of the three-dimensional polygon model based on the two-dimensional image.

  Therefore, the present invention provides three-dimensional object modeling data that can accurately determine whether output should be regulated based on a two-dimensional image when outputting a three-dimensional object based on a polygon model. It is an object to provide an output regulating device.

In order to solve the above problems, in the first aspect of the present invention,
A device for determining whether or not to regulate when outputting a polygon model expressed as a set of polygons to a three-dimensional object modeling apparatus as three-dimensional object modeling data,
A database in which distance distributions and angle distributions representing the characteristics of restricted images, which are images whose outputs should be restricted, are registered;
For the target model that is the polygon model of the output target, the distance from the reference point of the target model to each polygon that constitutes the target model and the angle specific to each polygon are calculated, and based on the calculation result, the distance to each polygon is calculated. Model frequency distribution calculating means for calculating a distance distribution that is a frequency distribution of distances and an angle distribution that is a frequency distribution of angles unique to each polygon;
The distance distribution and angle distribution of the target model calculated by the model frequency distribution calculating means are respectively compared with the distance distribution and angle distribution of the restriction image registered in the database to determine whether or not the output should be restricted. A frequency distribution matching means;
A three-dimensional object shaping data output regulation device is provided.

  According to the first aspect of the present invention, there is provided a database in which distance distributions and angle distributions representing the features of the restriction image are registered, and for the target model, from the reference point of the target model to each polygon constituting the target model The distance and the angle specific to each polygon are calculated, the distance distribution that is the frequency distribution of the distance and the angle distribution that is the frequency distribution of the angle are calculated based on the calculation result, and the calculated target model distance distribution and angle distribution Are compared with the distance distribution and angle distribution of the restriction image registered in the database, and it is determined whether the output should be restricted, so when performing the output of the three-dimensional object based on the polygon model, It is possible to accurately determine whether or not the output should be regulated. In particular, when the three-dimensional target model is a polygon model created based on a two-dimensional image, the output of the target model can be restricted when the image is a restriction image.

  Further, in the second aspect of the present invention, the distance distribution and the angle distribution of the restriction image registered in the database are pixels that constitute an edge of the object (object) with respect to the restriction image as edge points. And the distance from the reference point of the restriction image to each edge point of the restriction image and the angle specific to each edge point are calculated, and the distance distribution that is the frequency distribution of the distance to each edge point based on the calculation result And an image frequency distribution calculating device having an image frequency distribution calculating means for calculating an angle distribution which is a frequency distribution of angles unique to each edge point. Here, the object (object) is a target area that is formed on the polygon model in the restriction image, and the other area is the background. Therefore, the object is a portion corresponding to a subject if the image is a photograph, and a portion corresponding to an illustration or a character if the image is an artificially created image. Whether an image is an object or a background is subjective, but an image registered as a restricted image is often clearly defined as the object to be copied. The background classification is relatively clear.

  According to the second aspect of the present invention, the distance distribution and the angle distribution of the restriction image registered in the database are determined by the image frequency distribution calculation device using the pixels constituting the edge of the target object as edge points with respect to the restriction image. Extract and calculate the distance from the reference point of the restricted image to each edge point and the angle specific to each edge point, and based on the calculation result, the distance distribution that is the frequency distribution of the distance to each edge point, and each edge point specific Since the angle distribution, which is the frequency distribution of the angle, is calculated, the distance distribution and the angle distribution of the target model obtained by the model frequency distribution calculating means can be matched, and the frequency distribution matching means can perform two-dimensional It is possible to determine the similarity between data of different dimensions such as the restriction image and the three-dimensional target model by collating each distance distribution and angle distribution.That is, even if a three-dimensional polygon model similar to the target model is not obtained in advance as a restriction model and registered in the database, if a two-dimensional restriction image is obtained in advance and registered in the database, the target object of the restriction image is obtained. It becomes possible to regulate the output of the three-dimensional target model created based on the base.

In the third aspect of the present invention,
A device for determining whether or not to regulate when outputting a polygon model expressed as a set of polygons to a three-dimensional object modeling apparatus as three-dimensional object modeling data,
For the restriction image, which is an image whose output should be restricted, the pixels that constitute the edge of the object are extracted as edge points, and the distance from the reference point of the restriction image to each edge of the restriction image and each edge point specific Image frequency distribution calculating means for calculating an angle of the distance, and calculating a distance distribution that is a frequency distribution of distances to each edge based on a calculation result and an angle distribution that is a frequency distribution of angles unique to each edge point;
For the target model that is the polygon model of the output target, the distance from the reference point of the target model to each polygon that constitutes the target model and the angle specific to each polygon are calculated, and based on the calculation result, the distance to each polygon is calculated. Model frequency distribution calculating means for calculating a distance distribution that is a frequency distribution of distances and an angle distribution that is a frequency distribution of angles unique to each polygon;
Whether the distance distribution and angle distribution of the restriction image calculated by the image frequency distribution calculation means and the distance distribution and angle distribution of the target model calculated by the model frequency distribution calculation means are collated, and whether the output should be restricted Frequency distribution matching means for determining whether or not
A three-dimensional object shaping data output regulation device is provided.

  According to the third aspect of the present invention, pixels constituting the edge of the object are extracted as edge points from the restriction image, and the distance from the reference point of the restriction image to each edge and the angle unique to each edge point are determined. Calculate the distance distribution that is the frequency distribution of the distance to each edge and the angle distribution that is the frequency distribution of the angle specific to each edge point based on the calculation result, and calculate the reference of the target model for the target model Calculate the distance from the point to each polygon that makes up the target model and the angle unique to each polygon, calculate the distance distribution that is the frequency distribution of the calculated distance, and calculate the angle distribution that is the frequency distribution of the calculated angle The distance distribution and angle distribution of the target model are compared with the distance distribution and angle distribution of the restriction image to determine whether the output should be restricted. Dimension when doing Possible to determine whether or not to restrict the output by performing matching between different two-dimensional regulations images become. In particular, when the three-dimensional target model is a polygon model created based on the target in the two-dimensional image, the output of the target model can be restricted when the image is a restriction image. .

  Further, in the fourth aspect of the present invention, as the edge point, a pre-defined filter matrix of 3 × 3 pixels in 8 directions is applied to the values of all the pixels of the restricted image, and each pixel is Pixels whose edge intensity, which is the maximum value of the calculated intensity, exceeds a predetermined threshold value are extracted.

  According to the fourth aspect of the present invention, as the edge point, a filter matrix of 3 × 3 pixels in 8 directions defined in advance is applied to the values of all the pixels of the restricted image, and is calculated for each pixel. Pixels whose edge intensity, which is the maximum value of the intensity, exceeds a predetermined threshold value, are extracted, so that in addition to extracting edge points that are pixels that constitute the edge of the object in the restricted image, the edge It is possible to accurately calculate the direction of.

  In the fifth aspect of the present invention, as the edge point, a predefined 3 × 3 pixel filter matrix of 16 directions is applied to the values of all the pixels of the restricted image, Pixels whose edge intensity, which is the maximum value of the calculated intensity, exceeds a predetermined threshold value are extracted.

  According to the fifth aspect of the present invention, as the edge point, a predefined 3 × 3 pixel filter matrix of 16 directions is applied to the values of all the pixels of the restricted image, and is calculated for each pixel. Pixels whose edge intensity, which is the maximum value of the intensity, exceeds a predetermined threshold value, are extracted, so that in addition to extracting edge points that are pixels that constitute the edge of the object in the restricted image, the edge It is possible to accurately calculate the direction of.

  In the sixth aspect of the present invention, the reference point of the restriction image is obtained by adding the value obtained by multiplying the edge point by the edge strength of the edge point as a weight and adding the value for all the edge points. It is given as a point specified by a value divided by the sum of intensity.

  According to the sixth aspect of the present invention, the reference point of the restriction image is obtained by dividing the value obtained by multiplying the edge point by the edge strength as a weight and adding the value for all the edge points by the sum of the edge strengths of all the edge points. Since it is given as a point specified by a value, it is possible to quickly calculate a position close to the center of gravity of the object in the restricted image as a reference point.

  In the seventh aspect of the present invention, the image frequency distribution calculating means obtains a vector from the reference point of the restriction image to each edge point as an edge individual vector, and determines the size of the edge individual vector as each edge point. The distance between each edge point and the edge point is determined by making the angle formed by the edge direction vector that gives the edge strength at the edge point corresponding to the edge individual vector and the edge individual vector with an angle unique to each edge point. A unique angle is calculated.

  According to the seventh aspect of the present invention, the vector from the reference point of the restriction image to each edge point is obtained as an edge individual vector, the magnitude of the edge individual vector is the distance to each edge point, and the edge corresponding to the edge individual vector The distance to each edge point and the edge point specific are determined by making the angle formed with the edge direction vector that gives the edge strength, which is the maximum intensity calculated by applying the filter matrix at the point, as an angle specific to each edge point. Since the angle distribution of the target model obtained by the model frequency distribution calculating means and the consistency with the angle distribution of the target model can be calculated from the restriction image, the two-dimensional restriction image and the three-dimensional restriction image can be calculated. It is possible to determine the similarity between the data of different dimensions of the target model by comparing the respective angular distributions.

  In the eighth aspect of the present invention, the image frequency distribution calculating means prepares a one-dimensional array composed of a predetermined number (MD) of elements to calculate the distance distribution, and calculates the calculated distance. The distance distribution is calculated by equally dividing the range of the maximum value into the predetermined number, assigning each distance to one of the elements based on the distance, and counting the number corresponding to the element. It is characterized by doing so.

  According to the eighth aspect of the present invention, in calculating the distance distribution of the restriction image, a one-dimensional array composed of a predetermined number of elements is prepared, and the range of the maximum value of the calculated distance is equally divided into the predetermined number In addition, each distance is assigned to one of the elements based on the distance, and the number corresponding to the element is counted, so the distance distribution can be easily calculated using the distance from the reference point to each edge point. It becomes possible to do.

  In the ninth aspect of the present invention, the image frequency distribution calculating unit prepares a one-dimensional array composed of a predetermined number (MA) of elements to calculate the angle distribution, and an angle of 0 to 180 degrees. The angle distribution is calculated by equally dividing the range into a predetermined number and assigning it to any one of the elements based on the angle unique to each edge point, and counting the value of the element. It is characterized by being.

  According to the ninth aspect of the present invention, in calculating the angular distribution of the restriction image, a one-dimensional array composed of a predetermined number of elements is prepared, and a range from 0 degrees to 180 degrees is equally divided into a predetermined number In the above, the angle distribution is calculated by assigning to one of the elements based on the angle unique to each edge point and counting the value of the element, so it is easy to use the angle unique to each edge point. The distribution can be calculated.

  In the tenth aspect of the present invention, the image frequency distribution calculating means weights the edge element corresponding to the edge individual vector with the edge strength when counting the value of the assigned element. It is characterized by.

  According to the tenth aspect of the present invention, in calculating the distance distribution or the angle distribution of the restricted image, the value of the assigned element is weighted with the edge strength of the edge point corresponding to the individual edge vector. As a result, the difference in definition image definition does not reflect much in the distance distribution or the angle distribution, and it is possible to make an accurate determination without registering a plurality of restriction images with different numbers of pixels in the database. .

  In the eleventh aspect of the present invention, the reference point of the target model is a value obtained by adding a value obtained by multiplying a polygon average coordinate, which is an average of vertex coordinates of each polygon, by using the area of the polygon as a weight, for all polygons. The point is specified as a point specified by a value obtained by dividing the total area of all the polygons.

  According to the eleventh aspect of the present invention, the reference point of the target model is obtained by adding a value obtained by multiplying a polygon average coordinate, which is an average of vertex coordinates of each polygon, by the area of the polygon as a weight, for all polygons, Since it is given as a point specified by a value divided by the total area of all the polygons, a position close to the center of gravity of the target model can be quickly calculated as a reference point.

  In the twelfth aspect of the present invention, the model frequency distribution calculating means obtains a vector from the reference point to the average coordinates of each polygon as a polygon individual vector, and determines the size of the polygon individual vector to each polygon. A distance to each polygon and an angle specific to the polygon are calculated by setting a distance and an angle formed by the polygon individual vector and a normal vector of the corresponding polygon as an angle specific to each polygon. .

  According to the twelfth aspect of the present invention, a vector from the reference point to the average coordinate of each polygon is obtained as an individual polygon vector, the magnitude of the individual polygon vector is the distance to each polygon, and the polygon method corresponding to the individual polygon vector. By making the angle made with the line vector an angle unique to each polygon, the distance to each polygon and the angle unique to the polygon are calculated. It is possible to easily calculate a frequency distribution that accurately reflects the shape characteristics of the model.

  In the thirteenth aspect of the present invention, the model frequency distribution calculating means prepares a one-dimensional array composed of a predetermined number (MD) of elements for calculating the distance distribution, and calculates the calculated distance. The distance distribution is calculated by equally dividing the range of the maximum value into the predetermined number, assigning each distance to one of the elements based on the distance, and counting the number corresponding to the element. It is characterized by doing so.

  According to the thirteenth aspect of the present invention, in calculating the distance distribution, a one-dimensional array composed of a predetermined number of elements is prepared, and the range of the maximum value of the calculated distance is equally divided into a predetermined number. Since each distance is assigned to one of a predetermined number of elements based on the distance, and the distance distribution is calculated by counting the number corresponding to that element, the vector from the reference point to each polygon is calculated. It is possible to easily calculate the distance distribution by using it.

  In the fourteenth aspect of the present invention, the model frequency distribution calculating means prepares a one-dimensional array composed of a predetermined number (MA) of elements to calculate the angle distribution, and the angle is 0 to 180 degrees. Is uniformly divided into the predetermined number, and is assigned to one of the elements based on the angle unique to each polygon, and the angle distribution is calculated by counting the value of the element. It is characterized by that.

  According to the fourteenth aspect of the present invention, in calculating the angle distribution, a one-dimensional array composed of a predetermined number of elements is prepared, and a range of angles from 0 degrees to 180 degrees is equally divided into a predetermined number. Since the angle distribution is calculated by assigning to a certain number of elements based on the unique angle of each polygon and counting the value of that element, it is easy to use the vector from the reference point to each polygon It is possible to calculate the angular distribution.

  In the fifteenth aspect of the present invention, the model frequency distribution calculating unit weights the area of a polygon corresponding to the polygon individual vector when counting the value of the allocated element. And

  According to the fifteenth aspect of the present invention, in calculating the distance distribution or the angle distribution, the value of the allocated element is weighted by the area of the polygon corresponding to the polygon individual vector. The difference in the resolution of the image is not reflected in the distance distribution or the angle distribution so much, and it is possible to determine that a plurality of polygon models having different polygon divisions are the same (the output should be regulated). .

In the sixteenth aspect of the present invention,
The frequency distribution matching means, when matching the distance distribution and angle distribution of the target model with the distance distribution and angle distribution of the restriction image, respectively,
The correlation coefficient between the distance distributions and the correlation coefficient between the angular distributions are calculated, and the output should be regulated when both of the calculated correlation coefficients are larger than a predetermined positive threshold value. It is characterized by making it judge.

  According to the sixteenth aspect of the present invention, when collating the distance distribution and the angle distribution of the target model with the distance distribution and the angle distribution of the restriction image, respectively, the correlation coefficient between the distance distributions and the correlation coefficient between the angle distributions are calculated. When the calculated correlation coefficient is larger than a predetermined positive threshold, it is determined that the output should be regulated. It becomes possible to carry out quickly and accurately.

  The seventeenth aspect of the present invention further includes polygon reduction means for reducing polygons so that the number of polygons included in the target model is equal to or less than a predetermined value. The model frequency distribution calculating means performs processing.

  According to the seventeenth aspect of the present invention, first, polygons are reduced so that the number of polygons included in the target model is equal to or less than a predetermined value. Therefore, it is possible to determine whether the output is appropriate at high speed while allowing a difference in the fine shape of the target model.

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

  According to the eighteenth aspect of the present invention, first, the target model is divided into a plurality of partial target models, and the distance distribution and the angle distribution are calculated for the partial target model. When collating target models that represent products to be manufactured, it is possible to determine whether the output is appropriate or not even if the component models of the target model and the target model are different from each other Become.

  In the nineteenth aspect of the present invention, the polygon is a triangle, and the target model dividing means is arranged so that a polygon and three adjacent polygons sharing a side with the polygon belong to the same partial target model. And dividing.

  According to the nineteenth aspect of the present invention, the polygon is a triangle, and the target model dividing means divides a polygon and three adjacent polygons sharing the sides with the polygon so that they belong to the same partial target model. Since it did in this way, it becomes possible to divide | segment the object model expressing the articles | goods comprised by several components into several partial object model rapidly and exactly.

  Further, in the twentieth aspect of the present invention, with respect to the restriction image, pixels constituting the edge of the object are extracted as edge points, and the distance from the reference point of the restriction image to each edge point of the restriction image An image frequency distribution that calculates an angle specific to each edge point and calculates a distance distribution that is a frequency distribution of distances to each edge point and an angle distribution that is a frequency distribution of angles specific to each edge point based on the calculation result It further comprises registration means for receiving the frequency distribution calculated by the calculation device and registering the received frequency distribution in the database.

  According to the twentieth aspect of the present invention, with respect to the restriction image, pixels constituting the edge of the object are extracted as edge points, and the distance from the reference point of the restriction image to each edge point of the restriction image and each edge point A separate image frequency distribution calculation device that calculates a unique angle and calculates a distance distribution that is a frequency distribution of distances to each edge point and an angular distribution that is a frequency distribution of angles unique to each edge point based on the calculation result Since the frequency distribution of the regulated image is received from the image frequency distribution calculating device and registered in the database, the database can be updated quickly from a remote location.

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

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

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

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

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

  According to the twenty-third aspect of the present invention, the three-dimensional object is formed by using the three-dimensional object modeling data output restriction device and the three-dimensional object shaping apparatus that forms the three-dimensional object using the target model that is permitted to be output by the three-dimensional object shaping data output restriction device. Since the modeling system is realized, the three-dimensional object modeling system can be provided in the form of a 3D printer or the like incorporating a board computer.

  According to the present invention, when a three-dimensional object is output based on a polygon model, it is possible to accurately determine whether or not the output should be restricted based on a two-dimensional image.

It is a figure for demonstrating the basic concept of this invention. 3 is a hardware configuration diagram of an image frequency distribution calculating apparatus 300. FIG. 3 is a functional block diagram illustrating a configuration of an image frequency distribution calculation apparatus 300. FIG. 5 is a flowchart showing a processing operation of the image frequency distribution calculation apparatus 300. It is a figure which shows an 8-way edge filter. It is a figure which shows a 16 direction edge filter. It is a figure which shows a 16 direction edge filter. It is a figure which shows the relationship between the distance and angle in distance distribution and angle distribution. 1 is a hardware configuration diagram of a three-dimensional object formation system including a three-dimensional object formation data output restriction device 100 according to an embodiment of the present invention. It is a functional block diagram which shows the structure of the data output control apparatus for solid object modeling which concerns on the 1st Embodiment of this invention. It is a flowchart which shows the process outline | summary of the data output control apparatus for solid object modeling which concerns on the 1st Embodiment of this invention. It is a flowchart which shows the detail of the calculation process of the model frequency distribution of step S100. It is a flowchart which shows the detail of the collation process of the frequency distribution of step S200. It is a functional block diagram which shows the structure of the data output control apparatus for solid object modeling which concerns on the 2nd Embodiment of this invention. It is a flowchart which shows the process outline | summary of the data output control apparatus for solid object modeling which concerns on the 2nd Embodiment of this invention. It is a figure which shows the distance distribution and angle distribution obtained with respect to each of two spherical models A and B. It is a figure which shows the distance distribution and angle distribution which are obtained with respect to two spherical models B and C, respectively. It is a figure which shows the basic principle of polygon deletion. It is a flowchart which shows the detail of a polygon reduction process. It is a flowchart which shows the detail of object model division processing. It is a hardware block diagram of the solid object modeling system containing the data output control apparatus for solid object modeling in a modification. It is a hardware block diagram of a cloud type solid thing modeling system. It is a functional block diagram of a cloud type solid thing modeling system. It is a figure which shows the frequency distribution calculation example with respect to the spherical body a. It is a figure which shows the frequency distribution calculation example with respect to dachi b. It is a figure which shows the frequency distribution calculation example with respect to dachi c. It is a figure which shows the frequency distribution calculation example with respect to dachi d. FIG. 28 is a diagram illustrating correlation coefficients Dd and Da between the model illustrated in FIGS. 24 to 27 and an image. It is a figure which shows the frequency distribution calculation example with respect to a medical organ model. It is a figure which shows the frequency distribution calculation example with respect to a medical organ model.

DESCRIPTION OF EXEMPLARY EMBODIMENTS Hereinafter, preferred embodiments of the invention will be described in detail with reference to the drawings.
<1. Basic concept of the present invention>
First, the basic concept of the present invention will be described. In order to form a figure based on a character motif image with a 3D printer, it is necessary to create a 3D polygon model. In recent years, however, it has only been traced over illustrations and photographs displayed on the screen. Therefore, development of tools that can automatically add a depth by a cylindrical shape or the like and produce a three-dimensional polygon model semi-automatically has become active. Along with this, illegal figures are easily produced by 3D printers based on two-dimensional images with high literary properties such as characters and illustrations, and the market for genuine figure figures molded with molds is about to be pressed. . In the present invention, a three-dimensional polygon model is created on the basis of a two-dimensional image having a high copyright property such as a character or an illustration, and when the polygon model is output without the permission of the right holder of the character or illustration, the output is output. One purpose is to regulate. However, it is difficult to create a 3D polygon model of an illegal figure in advance, and even if it can be registered in the database, the shape of the 3D polygon model created based on the character motif image is inexhaustible. Therefore, the method of collating the polygon model to be output with the polygon model registered in the database cannot perform timely output regulation. Therefore, when two-dimensional motif images of typical characters with little diversity are registered in the database and a three-dimensional polygon model is output by a 3D printer, matching is performed using the registered two-dimensional images. It is necessary to implement a function that enables

  FIG. 1 is a diagram for explaining the basic concept of the present invention. FIG. 1A shows a three-dimensional polygon model to be output, and FIG. 1B shows a two-dimensional image to be regulated registered in the database. In the present invention, two types of frequency distributions for the distance and angle shown in FIG. 1C are created from the polygon model shown in FIG. 1A, and the image shown in FIG. As shown in FIG. 1D, two frequency distributions with respect to distance and angle as shown in c) are created. If the two frequency distributions for the distance and angle shown in FIGS. 1 (c) and 1 (d) are made to be consistent with each other, these are collated and their correlation is obtained. The similarity between the polygon model shown in FIG. 1A and the image shown in FIG. 1B can be determined. When the polygon model has characteristics similar to the registered image, the output of the polygon model is restricted.

<2. First Embodiment>
<2.1. Device Configuration of Image Frequency Distribution Calculation Device>
In the first embodiment, when determining whether or not the output of a three-dimensional polygon model is appropriate, the frequency distribution created from the polygon model and the frequency distribution created from the two-dimensional image to be regulated are collated. For this reason, it is necessary to create a frequency distribution from an image in advance. First, an image frequency distribution calculation apparatus that creates a frequency distribution from such an image will be described.

  FIG. 2 is a hardware configuration diagram of the image frequency distribution calculation apparatus. The image frequency distribution calculating apparatus 300 can be realized by a general-purpose computer. As shown in FIG. 2, the CPU (Central Processing Unit) 81, a RAM (Random Access Memory) 82 that is a main memory of the computer, and a CPU 81 A large-capacity storage device 83 such as a hard disk or flash memory for storing programs and data executed by the computer, a key input I / F (interface) 84 such as a keyboard and a mouse, and an external device such as a 3D printer and a data storage medium A data input / output I / F (interface) 85 for data communication with the apparatus and a display unit 86 which is a display device such as a liquid crystal display are provided and connected to each other via a bus.

  FIG. 3 is a functional block diagram showing the configuration of the image frequency distribution calculation apparatus. In FIG. 3, reference numeral 91 denotes restriction image storage means, 92 denotes image frequency distribution calculation means, and 93 denotes image frequency distribution storage means.

  The image frequency distribution calculating unit 92 calculates the distance and direction from the reference point of the restriction image to each edge constituting the restriction image with respect to the restriction image that is the restriction target image, and the frequency distribution of the calculated distance is used. A certain distance distribution is calculated, and an angle distribution that is a frequency distribution of angles formed by the calculated direction and the edge direction of each edge point is calculated.

  The image frequency distribution calculating unit 92 is realized by the CPU 81 executing a program stored in the storage device 83. The restriction image storage unit 91 is a storage unit that stores a restriction image that is an image whose output should be restricted, and is realized by the storage device 83.

  The image frequency distribution storage means 93 stores a distance distribution and an angle distribution calculated as two types of frequency distributions for a restriction image, which is an image whose output is to be restricted, into a database. 83. The distance distribution and the angle distribution, which are two types of frequency distribution, serve as a feature vector that represents the feature of the restriction image. That is, the difference between the original restriction images can be identified by the two types of frequency distributions, but the original restriction image cannot be restored. This is similar to the fact that individual differences can be identified by fingerprints, but the figure of the person itself cannot be restored. Therefore, the two frequency distributions do not serve as works, but serve as so-called fingerprints that prove the identity of the two works. In the image frequency distribution storage means 93, not only the distance distribution and the angle distribution but also the restriction image itself that is the basis for the calculation of the distance distribution and the angle distribution is registered. Since it can be visually confirmed by a human, the regulated image itself is usually registered with a resolution not worth commercial quality after receiving permission from the copyright holder.

  Each component shown in FIG. 3 is actually realized by mounting a dedicated program on hardware such as a computer and its peripheral devices as shown in FIG. That is, the computer executes the contents of each means according to a dedicated program.

  In the storage device 83 shown in FIG. 2, a dedicated program for operating the CPU 81 and causing the computer to function as an image frequency distribution calculation device is installed. By executing this dedicated program, the CPU 81 realizes a function as the image frequency distribution calculating unit 92. The storage device 83 not only functions as the restricted image storage unit 91 and the image frequency distribution storage unit 93 but also stores various data necessary for processing as the image frequency distribution calculation device.

<2.2. Processing Operation of Image Frequency Distribution Calculation Device>
Next, the processing operation of the image frequency distribution calculation apparatus shown in FIGS. 2 and 3 will be described. The image frequency distribution calculating unit 92 of the image frequency distribution calculating apparatus calculates a distance distribution and an angle distribution, which are two types of frequency distributions, for a restricted image that is an image whose output is to be restricted. FIG. 4 is a flowchart showing details of the frequency distribution calculation process. Here, the restriction image is handled as a monochrome image having 256 gradations in order to easily perform a filter operation described later. Therefore, in the case of a color image, a process of converting into a 256 gradation monochrome image is performed in advance, and in the case of a binary image, it is processed as a 256 gradation monochrome image having a value of either 0 or 255. To do. Therefore, the restricted image having the number of pixels Xs in the x direction and the number of pixels Ys in the y direction is Img (x, y) = 0 to 255 (x = 0,..., Xs−1; y = 0,. , Ys-1). The restricted image has an object part and a background part, and the object part is set to gray or white Img (x, y)> 0, and the background is black Img (x, y) = Set to 0. Since the four corners of the restriction image are the background, Img (0,0) = Img (0, Ys−1) = Img (Xs−1,0) = Img (Xs−1, Ys−1) = 0. ing.

  First, the image frequency distribution calculating unit 92 performs a filter operation on each pixel of the restricted image, and calculates an edge strength and an edge direction vector of each pixel (step S510). First, an edge extraction filter that is a filter matrix used for filter calculation will be described. The filter matrix is also called a matrix filter, a spatial filter, or the like, and is generally used for converting the pixel value of the target pixel using the pixel values of the target pixel and its surrounding pixels. In this embodiment, two types of edge filters can be used. FIG. 5 is a diagram illustrating an 8-way edge filter. The 8-direction edge extraction filter shown in FIG. 5 is obtained by changing the weights of the values of 9 pixels near 8 including itself according to each direction of 8 directions. In the example of FIG. 5, eight directions from direction 0 to direction 7 are shown. The case of direction 0 will be described as an example. (0.0, −1.0) on the right side indicates a direction vector that is a unit vector for specifying direction 0. Nine numbers of 3 × 3 in the vertical direction represent weights defined by M (d, u, v) = [− 2, −1, +1]. The weight takes three values, -2, -1, and +1. The nine numbers have three values, with u and v being -1, 0, and 1, respectively, with the center being (u, v) = (0, 0). In the case of the center, M (d, 0, 0) is always −2 regardless of the value of d, and in the case of other than the center, the value of M (d, u, v) is −1 according to the direction d. And 1 are different positions. In the case of direction 0 (d = 0), as shown in the example on the right side of FIG. 5, the weights M (d, u, v) are M (0, −1, −1) = 1 to M (0,1). , 1) = − 1 are defined. At this time, the direction vectors Ex (d) and Ey (d) of the filter are Ex (0) = 0.0 and Ey (0) = − 1.0. The 8-direction edge extraction filter shown in FIG. 5 is known as a Prewitt 8-direction edge extraction filter.

  6 and 7 are diagrams showing a 16-direction edge filter. The 16-direction edge extraction filters shown in FIGS. 6 and 7 are obtained by changing the weights of the values of 9 pixels in the vicinity of 8 including itself according to each direction of 16 directions. In the examples of FIGS. 6 and 7, 16 directions from direction 0 to direction 15 are shown. The case of direction 0 will be described as an example. (0.0, −1.0) on the right side indicates a direction vector that is a unit vector for specifying direction 0. Nine numbers of 3 × 3 in the vertical direction represent weights defined by M (d, u, v) = [− 2, −1, +1]. The weight takes three values, -2, -1, and +1. The nine numbers have three values, with u and v being -1, 0, and 1, respectively, with the center being (u, v) = (0, 0). In the case of the center, M (d, 0, 0) = 1 without fail regardless of the value of d, and in the case of other than the center, −2 of the value of M (d, u, v) according to the direction d, The positions of -1 and 1 are different. The 16-direction edge extraction filter shown in FIGS. 6 and 7 changes the direction at 1/2 angle (22.5 degrees) as compared to the 8-direction edge extraction filter (45 degrees) shown in FIG. It has been changed.

  As the edge extraction filter, either the 8-direction edge extraction filter shown in FIG. 5 or the 16-direction edge extraction filter shown in FIGS. 6 and 7 may be used. The image frequency distribution calculating unit 92 executes processing according to the following [Equation 1] using any one of the edge extraction filters M (d, u, v), and determines the edge intensity and edge direction vector of each pixel. To do.

[Formula 1]
F (d, x, y) = [Σv _{= -1,1} Σu _{= -1,1} M (d, u, v) [Img (x, y) −128]] / 5... 8 directions When using an edge extraction filter F (d, x, y) = [Σv _{= -1,1} Σu _{= -1,1} M (d, u, v) [Img (x, y) −128]] / 6-22... Using 16-direction edge extraction filter EK (x, y) = F (dmax, x, y) = MAX _{d = 0, D} F (d, x, y)
Vx (x, y) = Ex (dmax)
Vy (x, y) = Ey (dmax)

In the first and second formulas of [Formula 1], the subscripts “v = −1,1” and “u = −1,1” of Σ are such that v is −1 to 1, and u is −1. In the case of taking all integers from 1 to 1, the sum is obtained. The filter operation value F (d, x, y) is obtained for each pixel by the first expression or the second expression of the above [Expression 1]. In the third equation of [Equation 1], “MAX _{d = 0, D} ” indicates the maximum value in all directions from the direction d = 0 to the direction d = D. D is 7 when an 8-direction edge extraction filter is used, and 15 when a 16-direction edge extraction filter is used. According to the third equation of the above [Equation 1], the maximum filter calculation value provides the edge intensity EK (x, y) of each pixel. Further, the maximum filter calculation value is taken, and the direction d giving the edge strength is set to dmax. The edge direction vectors Vx (x, y) and Vy (x, y) of the pixel (x, y) are obtained as directions of the direction dmax by the fourth and fifth expressions of the above [Equation 1]. The edge direction vector is a vector that specifies the direction of the edge, and indicates the direction orthogonal to the boundary line and crossing from the object in the background direction at the boundary between the object and the background in the restricted image. Inside the object of the restricted image, a boundary portion where the difference in density is remarkable indicates a direction crossing from a direction perpendicular to the boundary line and having a lighter density (close to white) to a darker density (close to black).

  Next, a reference point in the restricted image is calculated (step S520). Specifically, first, the edge strength calculated in step S510 is compared with a preset threshold value, and a pixel having an edge strength equal to or higher than the threshold value is extracted as an edge point. The threshold value can be set as appropriate, but is preferably set to “0” so that pixels having an edge strength of 0 or more are extracted.

  Assuming that the extraction order of edge points is i, when Ne edge points are extracted, the arrangement of edge points is [Xe (i), Ye (i)] (i = 0,..., Ne-1). Can be defined as Then, the edge intensity EK (i) = EK (Xe (i), Ye (i)) of the i-th edge point is set, and the image frequency distribution calculating unit 92 executes processing according to the following [Equation 2]. Thus, the reference point (Xge, Yge) of the restriction image is calculated.

[Formula 2]
EKsum = Σ _{i = 0, Ne-1} EK (i)
Xge = {Σi = _{0, Ne−1} (Xe (i) × EK (i))} / EKsum
Yge = {Σi = _{0, Ne−1} (Ye (i) × EK (i))} / EKsum

  In the above [Equation 2], EKsum is the sum of the edge intensities of all the extracted edge points. Since the reference point of the restriction image is multiplied by the edge strength of each edge point as shown in [Formula 2], it indicates a point close to the center of gravity of the object of the restriction image, and the edge strength of the edge point is expressed as follows. It is a reference point that takes into account. The object of the restriction image means a character pattern or the like excluding the background portion.

  Next, the image frequency distribution calculating unit 92 calculates a vector from the reference point to the edge point (step S530). Specifically, by executing processing according to the following [Equation 3], a vector from the reference point (Xge, Yge) of the restricted image to each edge point i [Xe (i), Ye (i)] The edge individual vectors (Dxe (i), Dye (i)) are calculated.

[Formula 3]
Dxe (i) = Xe (i) -Xge
Dye (i) = Ye (i) -Yge

  Next, the image frequency distribution calculating unit 92 calculates the maximum distance between the reference point and the edge point (step S540). Specifically, the distance from the reference point (Xge, Yge) of the restriction image to each edge point i (Xe (i), Ye (i)) is executed by executing the processing according to the following [Equation 4]. De (i) is obtained, and the maximum of Ne distances De (i) is set as the maximum distance Demax.

[Formula 4]
De (i) = [Dxe (i) ² + Dye (i) ² ] ^1/2
Demax = MAX _{i = 0, Ne-1} De (i)

  In the above [Expression 4], Max indicates that the maximum value is selected from De (i) of the subscript “i = 0, Ne−1”.

  Next, the image frequency distribution calculating unit 92 calculates the angle formed by the edge individual vector, which is a vector from the reference point to the edge point, and the edge direction vector (step S550). Specifically, by executing processing according to the following [Equation 5], the edge individual vector (Dxe (i), Dye (i)) of the edge point i and the edge direction vector (Vx) of the edge point i are executed. An angle Ae (i) formed by (i), Vy (i)) (= (Vx (x, y), Vy (x, y))) is calculated.

[Formula 5]
Ae (i) = [cos ⁻¹ [Dxe (i) · Vx (i) + Dye (i) · Vy (i)] / De (i)] · 180 / π

  In the above [Formula 5], in order to change the unit from radians to degrees, the part enclosed by [] is multiplied by 180 / π. Therefore, Ae (i) takes a value from 0 to 180. The angle formed by the vector from the reference point to the edge point and the edge direction vector is an angle unique to each edge point.

  Next, the image frequency distribution calculating unit 92 calculates the distance distribution between the reference point and the edge point (step S560). The distance distribution calculated in the present embodiment is a distribution of distance weighted with edge strength, where the number of elements is MD, and the frequency of each element md (md = 0,..., MD-1) is Hdo (md ).

  The image frequency distribution calculating unit 92 sets the initial value as Hdo (md) = 0, and calculates the frequency Hdo (md) of the element md by executing processing according to the following [Equation 6].

[Formula 6]
If Demax × md / MD ≦ De (i) <Demax × (md + 1) / MD,
Hdo (md) ← Hdo (md) + EK (i)

  In the above [Equation 6], the distance De (i) from the reference point of the restriction image to the edge point i is not less than a value obtained by multiplying the maximum distance Demax by md / MD and smaller than a value obtained by multiplying (md + 1) / MD. The edge strength EK (i) of the edge point i is added to Hdo (md).

  The distance distribution Hdo (md) (md = 0,..., MD-1) is calculated by executing the processing according to [Formula 6] for Ne edge points. Since each element is added with the edge strength EK (i), not just the frequency, the distance distribution Hdo (md) indicates the edge strength weighted distance distribution. It is also possible to simply add 1 instead of the edge strength EK (i). In this case, however, the distance distribution does not take into account the edge strength, and the case where the pixels are rough and the case where the pixels are coarse with respect to the same restricted image. Thus, a significant difference occurs in the distance distribution. Therefore, in this embodiment, the edge strengths of the edge points are added when calculating the distance distribution. Further, the unit is normalized to the edge intensity rate [%] by multiplying Hdo (md) calculated by the above [Equation 6] by 100 / EKsum and replacing it with Hdo (md).

  Next, the image frequency distribution calculating unit 92 calculates the angle distribution of the edge individual vector, which is a vector to the edge point, and the edge direction vector (step S570). The angle distribution calculated in the present embodiment is an angle distribution obtained by weighting the edge strength, where the number of elements is MA, and the frequency of each element ma (ma = 0,..., MA-1) is Hao (ma ). The element number MA may be the same as the element number MD.

  The image frequency distribution calculating unit 92 sets the initial value Hao (ma) = 0, and calculates the frequency Hao (ma) of the element ma by executing processing according to the following [Equation 7].

[Formula 7]
If 180 × ma / MA ≦ Ae (i) <180 × (ma + 1) / MA,
Hao (ma) ← Hao (ma) + EK (i)

  [Equation 7] indicates that the angle Ae (i) between the edge individual vector of the edge point i and the edge direction vector of the edge point i is not less than a value obtained by multiplying 180 by ma / MA, and (ma + 1) / This means that the edge strength EK (i) of the edge point i is added to Hao (ma) when the value is smaller than the value multiplied by MA.

  The angle distribution Hao (ma) (ma = 0,..., MA−1) is calculated by executing the processing according to the above [Equation 7] for Ne edge points. Since the edge strength EK (i) is added to each element, not just the frequency, the angle distribution Hao (ma) indicates an edge strength-weighted angular distribution. It is possible to simply add 1 instead of the edge intensity EK (i). In this case, however, the angle distribution does not take into account the edge intensity, and the case where the pixels are coarse and the case where the pixels are coarse with respect to the same restricted image. Thus, a significant difference occurs in the distance distribution. Therefore, in the present embodiment, the edge strength of the edge point is added at the time of calculating the angular distribution, and further, the unit is replaced with Hao (ma) by multiplying Hao (ma) by 100 / EKsum, thereby converting the unit into the edge strength rate [%. ] Is normalized. The two kinds of frequency distributions of the distance distribution and the angle distribution obtained by the processing shown in FIG. 4 are stored in the image frequency distribution storage means 93 in association with the identification information for specifying the restriction image.

  FIG. 8A shows the relationship between the distance and angle in the distance distribution and the angle distribution obtained from the restriction image. In FIG. 8A, the shaded area is the object of the restriction image. This portion has a pixel value that differs greatly from the background. The reference point is usually located near the center of gravity of the object. The edge point does not exist in the boundary portion between the object and the background or in the example of FIG. 8A, but is located at the boundary portion where the contrast in the object is significant. Then, the distance from the reference point of the restriction image to the edge point is calculated for each edge point. The angle unique to each edge point is an angle formed by an edge individual vector that is a vector from the reference point to each edge point and an edge direction vector of the edge point.

<2.3. Configuration of data output restriction device for three-dimensional object modeling>
Next, the three-dimensional object formation data output restriction device according to the first embodiment of the present invention will be described. FIG. 9 is a hardware configuration diagram of a three-dimensional object formation system including the three-dimensional object formation data output restriction device 100 according to the first embodiment of the present invention. The three-dimensional object modeling data output restriction device 100 according to the present embodiment can be realized by a general-purpose computer. As shown in FIG. 9, a CPU (Central Processing Unit) 1 and a RAM (a main memory of the computer) Random Access Memory) 2, a large-capacity storage device 3 such as a hard disk or flash memory for storing programs and data executed by the CPU 1, a key input I / F (interface) 4 such as a keyboard and a mouse, and 3D A data input / output I / F (interface) 5 for data communication with an external device such as a printer or a data storage medium and a display unit 6 which is a display device such as a liquid crystal display are connected to each other via a bus. ing.

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

  In FIG. 9, the three-dimensional object formation data output restriction device 100 and the 3D printer 7 are shown in a separated form, but most of the 3D printer products currently on the market include the three-dimensional object formation data output restriction device 100. Constituent elements, such as CPU1, RAM2, storage device 3, key input I / F4 (not a keyboard / mouse for general-purpose computers, but several buttons at a numeric keypad level), data input / output I / F5, display unit 6 (several lines) A small liquid crystal panel capable of displaying the above characters and a touch panel are often superimposed to serve as a key input I / F 4). Accordingly, the 3D printer 7 itself can directly receive the three-dimensional object modeling data via the external storage medium, and can be operated to model the three-dimensional object alone (particularly this is more common in a 3D printer for consumer use). ). In other words, the three-dimensional object modeling system shown in FIG. 9 is often housed in a single casing and distributed as a product called “3D printer”.

  FIG. 10 is a functional block diagram illustrating a configuration of the three-dimensional object formation data output restriction device according to the present embodiment. In FIG. 10, 10 is a model frequency distribution calculating means, 30 is a frequency distribution matching means, 40 is a target model storage means, and 50 is a frequency distribution database.

  The model frequency distribution calculation means 10 calculates the direction and distance from the reference point of the target model to each polygon constituting the target model for the target model that is a polygon model to be output, and the frequency distribution of the calculated distance A distance distribution is calculated, and an angle distribution that is a frequency distribution of angles formed by the calculated direction and a direction (normal vector) unique to each polygon is calculated. The frequency distribution collating unit 30 collates the distance distribution and angle distribution calculated for the target model with the distance distribution and angle distribution of the restriction image registered in the frequency distribution database 50, respectively, and whether or not the output should be restricted, That is, it is determined whether the output is inappropriate or appropriate.

  The model frequency distribution calculating unit 10 and the frequency distribution matching unit 30 are realized by the CPU 1 executing a program stored in the storage device 3. The target model storage unit 40 is a storage unit that stores a target model, which is a polygon model that is a determination target of whether or not to restrict output, and is realized by the storage device 3. The polygon model is data representing a predetermined three-dimensional shape in a three-dimensional space by a set of polygons, and is output as three-dimensional object modeling data to a three-dimensional object modeling apparatus such as a 3D printer. In this embodiment, STL (Standard Triangulated Language) is adopted as the data format of the polygon model.

  The frequency distribution database 50 stores a distance distribution and an angle distribution calculated as two types of frequency distributions for a restriction model that is a polygon model whose output is to be restricted and a restriction image that is an image whose output is to be restricted. The database is realized by the storage device 3. The distance distribution and the angle distribution, which are two types of frequency distribution, serve as a feature vector that represents the features of the restriction image and the restriction model. That is, the difference between the original restriction model and the restriction image can be identified by the two types of frequency distributions, but the original restriction model and the restriction image cannot be restored. This is similar to the fact that individual differences can be identified by fingerprints, but the figure of the person itself cannot be restored. Therefore, the two frequency distributions do not serve as works, but serve as so-called fingerprints that prove the identity of the two works. In the frequency distribution database 50, it is possible to register not only the distance distribution and the angle distribution but also the regulation model and the regulation image itself that are the basis of the calculation of the distance distribution and the angle distribution. Normally, the regulatory model itself is not registered due to copyright issues, but for regulated images, the database is usually registered with permission from the copyright holder, and human verification of the verification results is performed. In order to make this possible, the regulation image itself is often registered together.

  Each component shown in FIG. 10 is actually realized by mounting a dedicated program on hardware such as a computer and its peripheral devices as shown in FIG. That is, the computer executes the contents of each means according to a dedicated program. In this specification, the computer means an apparatus having an arithmetic processing unit such as a CPU and capable of data processing, and is incorporated not only in a general-purpose computer such as a personal computer but also in a “3D printer” as a product. Board computer included.

  The storage device 3 shown in FIG. 9 is mounted with a dedicated program for operating the CPU 1 and causing the computer to function as a three-dimensional object formation data output restriction device. By executing this dedicated program, the CPU 1 realizes functions as the model frequency distribution calculating means 10 and the frequency distribution matching means 30. The storage device 3 not only functions as the target model storage unit 40 and the frequency distribution database 50 but also stores various data necessary for processing as the three-dimensional object formation data output restriction device.

<2.4. Processing operation of three-dimensional object shaping data output restriction device>
<2.4.2. Pretreatment>
Next, the processing operation of the three-dimensional object formation data output restriction device shown in FIGS. 9 and 10 will be described. First, with the image frequency distribution calculating apparatus as described above, two types of frequency distributions are created from the regulated image that is an image whose output is to be regulated, and the created frequency distributions are registered in the frequency distribution database 50. The image frequency distribution calculating device does not need to be realized by a separate computer from the three-dimensional object shaping data output restriction device, and the computer for realizing the three-dimensional object shaping data output restriction device is for realizing the image frequency distribution calculating device. A program can also be incorporated.

  Furthermore, not only the frequency distribution created from the regulation image, but also a frequency distribution expressing a regulation model that is a polygon model to be regulated can be created and registered in the frequency distribution database 50. In this case, a distance distribution and an angle distribution, which are two types of frequency distribution, are created for the restriction model. The creation of the distance distribution and the angle distribution from the polygon model can be performed in the same manner as the processing in the three-dimensional object formation data output restriction device described later. Creation of the distance distribution and the angle distribution from the restriction model may be performed by the three-dimensional object formation data output restriction device, or may be performed by executing a similar program on another computer. The created distance distribution and angle distribution are registered in the frequency distribution database 50. The frequency distribution created from the restriction image that is two-dimensional data and the frequency distribution created from the restriction model that is three-dimensional data are created so as to have the same format. Thereby, at the time of the collation mentioned later, it becomes possible to collate similarly about both a regulation image and a regulation model.

  The target of output regulation may be not only dangerous materials such as guns and swords, but also copyrighted materials such as characters. In either case, the produced image or polygon model is a copyrighted work with a copyright holder. Therefore, in order to register an image or polygon model that is a copyrighted work in the database, the act itself needs the permission of the author. In many cases, it is a corporation with an explicit copyright holder for images, and it is possible to obtain permission, but for polygon models, it is difficult to obtain permission because many copyright holders such as individuals are unknown. It is. Although the difference between the original image and the polygon model can be identified by the format of the distance distribution and the angle distribution, the original image and the polygon model cannot be restored. This is similar to the fact that individual differences can be identified by fingerprints, but the figure of the person itself cannot be restored, and the form of distance distribution and angle distribution corresponds to fingerprints. Not applicable. Therefore, as in the present invention, by registering in the form of distance distribution and angle distribution, it is possible to construct a database that records the features of the restriction image and restriction model without requiring the author's permission. However, for regulated images, the regulated image itself is also registered with the resolution reduced to a level that cannot be used for commercial use with the permission of the author. It is desirable to take operation so that can be confirmed visually.

<2.4.3. Process Overview>
Next, the processing operation of the three-dimensional object formation data output restriction device shown in FIGS. 9 and 10 will be described. FIG. 11: is a flowchart which shows the process outline | summary of the data output control apparatus for solid object shaping which concerns on the 1st Embodiment of this invention. First, the model frequency distribution calculation means 10 calculates a distance distribution and an angle distribution that are two types of frequency distributions for the target model that is a polygon model (step S100). Then, the calculated frequency distribution of the target model is collated with the frequency distribution of the regulation image and regulation model registered in the frequency distribution database 50 (step S200).

<2.4.4. Frequency distribution calculation processing>
First, the frequency distribution calculation process in step S100 will be described. FIG. 12 is a flowchart showing details of the frequency distribution calculation processing. Here, the number of polygons of the target model is N, and the variable i (i = 0,..., N−1) for specifying the polygon is used, and the vertices of each polygon i are (Xu (i), Yu (i), Zu ( i)). u represents a vertex number, and in this embodiment, since the polygon is a triangle, it takes three values u = 0, 1, and 2. Therefore, for example, Xu (i) is also expressed as X0 (i), X1 (i), and X2 (i). Further, the normal vector of each polygon i is defined as (Xn (i), Yn (i), Zn (i)). The polygons and normal vectors specifying the vertices are necessary for output by a 3D printer, which is a three-dimensional object forming apparatus, and the direction of the normal vectors indicates the outside of the forming surface (from the material to the air).

  The model frequency distribution calculating means 10 first calculates the total area Ssum, which is the sum of the areas S (i) of the polygons i and the areas S (i) of the N polygons constituting the target model, by the following [Equation 8]. Calculation is performed by executing the processing according to the above (step S110).

[Formula 8]
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 8], the subscript “i = 0, N−1” of Σ indicates that the sum is obtained when i takes all integers from 0 to N−1. The same applies to the following [Equation 10]. Next, a reference point in the target model is calculated (step S120). Specifically, for each polygon constituting the polygon model, an average point that is the average of the vertices is calculated, and N points constituting the polygon model are obtained by multiplying the average point of each polygon by the area of the polygon as a weight. The average value of the polygons is calculated as a reference point of the polygon model. Specifically, first, an average point (Xc (i), Yc (i), Zc (i)) of each polygon is calculated by executing processing according to the following [Equation 9].

[Formula 9]
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

  Next, the model frequency distribution calculating means 10 uses the polygon area S (i) calculated by the processing according to the above [Equation 8] and the total area Ssum which is the sum of the areas S (i) as follows. The reference point (Xg, Yg, Zg) of the target model is calculated by executing the process according to [Formula 10].

[Formula 10]
Xg = {Σi _{= 0, N−1} (Xc (i) × S (i))} / Ssum
Yg = {Σi _{= 0, N−1} (Yc (i) × S (i))} / Ssum
Zg = {Σi _{= 0, N−1} (Zc (i) × S (i))} / Ssum

  Since the reference point of the target model is multiplied by the area of each polygon as shown in [Formula 10] above, it indicates a point close to the center of gravity of the target model, and is a reference point that takes into account the weight of the polygon. ing.

  Next, the model frequency distribution calculating means 10 calculates a vector from the reference point to the polygon average point (step S130). Specifically, by executing processing according to the following [Equation 11], the average point (Xc (i), Yc (i)) of each polygon i from the reference point (Xg, Yg, Zg) of the target model. , Zc (i)), a polygon individual vector (Dx (i), Dy (i), Dz (i)), which is a vector to be calculated.

[Formula 11]
Dx (i) = Xc (i) -Xg
Dy (i) = Yc (i) -Yg
Dz (i) = Zc (i) -Zg

  Next, the model frequency distribution calculation means 10 calculates the maximum distance between the reference point and the polygon average point (step S140). Specifically, by executing the processing according to the following [Equation 12], the average point (Xc (i), Yc (i)) of each polygon i from the reference point (Xg, Yg, Zg) of the target model. , Zc (i)), the maximum distance D (i) is determined from the N distances D (i).

[Formula 12]
D (i) = [Dx (i) ² + Dy (i) ² + Dz (i) ² ] ^1/2
Dmax = MAX _{i = 0, N-1} D (i)

  In the above [Equation 12], Max indicates that the maximum value is selected from N D (i) with the subscript “i = 0, N−1”.

  Next, the model frequency distribution calculating means 10 calculates an angle formed between a polygon individual vector, which is a vector from the reference point to the polygon average point, and a normal vector (step S150). Specifically, by executing the processing according to the following [Equation 13], the polygon individual vector (Dx (i), Dy (i), Dz (i)) of the polygon i and the normal line of the polygon i are executed. An angle A (i) formed by the vectors (Xn (i), Yn (i), Zn (i)) is calculated.

[Formula 13]
A (i) = [cos ⁻¹ [Dx (i) Xn (i) + Dy (i) Yn (i) + Dz (i) Zn (i)] / D (i)] · 180 / π

  In the above [Formula 13], the unit enclosed by [] is multiplied by 180 / π in order to change the unit from radians to degrees. Therefore, A (i) takes a value from 0 to 180. The angle formed between the individual polygon vector, which is a vector from the reference point to the polygon average point, and the normal vector is an angle unique to each polygon.

  Next, the model frequency distribution calculating means 10 calculates the distance distribution between the reference point and the polygon average point (step S160). The distance distribution calculated in the present embodiment is an area-weighted distance distribution, where the number of elements is MD, and the frequency of each element md (md = 0,..., MD-1) is Hd (md). Represent.

  The model frequency distribution calculating means 10 sets the initial value as Hd (md) = 0, and calculates the frequency Hd (md) of the element md by executing processing according to the following [Equation 14].

[Formula 14]
If Dmax × md / MD ≦ D (i) <Dmax × (md + 1) / MD,
Hd (md) ← Hd (md) + S (i)

  In the above [Expression 14], the distance D (i) from the reference point of the target model to the average point of the polygon i is not less than a value obtained by multiplying the maximum distance Dmax by md / MD, and a value obtained by multiplying (md + 1) / MD. If it is smaller, it means that the area S (i) of the polygon i is added to Hd (md).

  The distance distribution Hd (md) (md = 0,..., MD-1) is calculated by executing the processing according to the above [Equation 14] for N polygons. Since the area S (i) is added to each element, not just the frequency, the distance distribution Hd (md) indicates an area weighted distance distribution. It is possible to simply add 1 instead of the area S (i), but in this case, the distance distribution does not take into account the area of the polygon, and the case where the polygon structure is coarse and fine for the same polygon model This causes a significant difference in the distance distribution. Therefore, in the present embodiment, the area of the polygon is added when calculating the distance distribution. Further, the unit is normalized to the area ratio [%] by multiplying Hd (md) calculated by the above [Equation 14] by 100 / Ssum and replacing it with Hd (md).

  Next, the model frequency distribution calculation means 10 calculates the angle distribution between the polygon individual vector, which is a vector to the polygon average point, and the normal vector (step S170). The angle distribution calculated in the present embodiment is an area-weighted angle distribution, where the number of elements is MA, and the frequency of each element ma (ma = 0,..., MA-1) is Ha (ma). Represent. The element number MA may be the same as the element number MD.

  The model frequency distribution calculating means 10 sets the initial value Ha (ma) = 0 and executes the processing according to the following [Equation 15] to calculate the frequency Ha (ma) of the element ma.

[Formula 15]
If 180 × ma / MA ≦ A (i) <180 × (ma + 1) / MA,
Ha (ma) ← Ha (ma) + S (i)

  In the above [Equation 15], the angle A (i) between the polygon individual vector of the polygon i and the normal vector of the polygon i is equal to or larger than the value obtained by multiplying 180 by ma / MA, and (ma + 1) / MA is When the value is smaller than the multiplied value, it means that the area S (i) of the polygon i is added to Ha (ma).

  The angle distribution Ha (ma) (ma = 0,..., MA−1) is calculated by executing the processing according to the above [Equation 15] for N polygons. Since each element is added with an area S (i), not just a frequency, the angle distribution Ha (ma) indicates an area-weighted angular distribution. It is also possible to simply add 1 instead of the area S (i), but in this case, the angle distribution does not take into account the polygon area, and the case where the polygon structure is coarse and fine for the same polygon model This causes a significant difference in the distance distribution. Therefore, in this embodiment, the area of the polygon is added at the time of calculating the angle distribution, and the unit is normalized to the area ratio [%] by multiplying Ha (ma) by 100 / Ssum and replacing it with Ha (ma). It has become.

  FIG. 8B shows the relationship between the distance and angle in the distance distribution and angle distribution obtained from the target model. FIG. 8B shows a cross section of the polygon model. In FIG. 8B, the surrounding line segments indicate polygons. The reference point is usually located near the center of gravity of the polygon model. The average point having the average coordinates of the vertices of one polygon and the distance to the reference point are calculated for each polygon. The angle unique to each polygon is an angle formed by a polygon individual vector that is a vector from the reference point to each polygon and a normal vector of the polygon.

<2.4.5. Frequency distribution matching process>
Next, the frequency distribution matching process in step S200 will be described. FIG. 13 is a flowchart showing the frequency distribution matching process. First, the frequency distribution matching unit 30 extracts the frequency distribution registered in the record re (re = 0,..., RE−1) from the RE records stored in the frequency distribution database 50. (Step S210). As the frequency distribution, a distance distribution Hdo (re, md) (md = 0,..., MD-1) and an angle distribution Hao (re, ma) (ma = 0,..., MA-1) are extracted. .

  Next, the frequency distribution matching unit 30 calculates a correlation coefficient between each element between the distance distribution calculated from the target model and the distance distribution of the restriction model extracted from the frequency distribution database 50 (step S220). The correlation coefficient is an index indicating the strength of the relationship between two array elements. Here, it is used to determine the strength of similarity between the target model and the restriction image (or restriction model). First, by executing the processing according to the following [Equation 16], the average value Ad of the distance distribution Hd (md) calculated from the target model, the distance distribution Hdo ( The average value Ado (re) of re, md) is calculated.

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

  Subsequently, by executing the processing according to the following [Equation 17], the standard deviation Sd of the distance distribution Hd (md) calculated from the target model, and the distance distribution Hdo of the restriction image extracted from the frequency distribution database 50. A standard deviation Sdo (re) of (re, md) is calculated.

[Formula 17]
Sd = [Σ _{md = 0, MD-1} (Hd (md) −Ad) ² ] ^1/2
Sdo (re) = [Σ _{md = 0, MD-1} (Hdo (re, md) −Ado (re)) ² ] ^1/2

  Strictly speaking, the standard deviation needs to be further divided by a constant MD within {} in the above [Equation 17]. However, since the purpose here is to calculate the correlation coefficient, it is necessary to divide by the constant MD in the term for calculating the covariance of the numerator in [Equation 18] to be described later. The operation of dividing by the constant MD is canceled (the denominator is divided twice by the square root of the constant MD).

  Next, by using the calculated average value and standard deviation, a process according to the following [Equation 18] is executed to correlate the distance distribution of the target model and the distance distribution of the restriction model corresponding to the record re. The number Dd (re) is calculated.

[Formula 18]
Dd (re) = [Σ _{md = 0, MD-1} (Hd (md) −Ad) × (Hdo (re, md) −Ado (re))] / (Sdo (re) × Sd)

  In the above [Equation 18], the subscript “md = 0, MD-1” of Σ indicates that the sum is obtained when md takes all integers from 0 to MD-1. The correlation coefficient Dd (re) is obtained by dividing the covariance of the frequency distributions Hd (md) and Hdo (re, md) by their standard deviations. In general, the processing load is high when dividing the square root of the denominator twice as in the calculation of [Formula 18]. For this reason, in the field of image processing, what is required only by the product-sum operation of numerators is called “correlation coefficient”, and Dd (re) shown in the above [Equation 18] is called “normalized correlation coefficient”. There are also customs.

  Next, the frequency distribution matching unit 30 compares the correlation coefficient Dd (re) of the distance distribution calculated in step S220 with a determination threshold value (step S230). When the correlation coefficient Dd (re) is greater than or equal to the determination threshold value, it is determined as conforming, and when the correlation coefficient Dd (re) is smaller than the determination threshold value, it is determined as non-conforming. The determination threshold value can be arbitrarily set as long as it is a positive value, but here, when the correlation coefficient value taking a range of +5.0 (−1 to +1) is multiplied by 100 and expressed in% dimension ).

  If it is determined in step S230 that the frequency distribution is matched, the frequency distribution matching unit 30 compares the angle distribution calculated from the target model and the angular distribution of the restriction image extracted from the frequency distribution database 50 with each other. The number of relationships is calculated (step S240). First, by executing the processing according to the following [Equation 19], the average value Aa of the angular distribution Ha (ma) calculated from the target model, and the angular distribution Hao ( The average value Aao (re) of re, ma) is calculated.

[Formula 19]
Aa = Σ _{ma = 0, MA-1} Ha (ma) / MA
Aao (re) = Σ _{ma = 0, MA-1} Hao (re, ma) / MA

  Subsequently, by executing processing according to the following [Equation 20], the standard deviation Sa of the angular distribution Ha (ma) calculated from the target model, and the angular distribution Hao of the restriction image extracted from the frequency distribution database 50 are obtained. A standard deviation Sao (re) of (re, ma) is calculated.

[Formula 20]
Sa = [ _{Σma = 0, MA-1} (Ha (ma) −Aa) ² ] ^1/2
Sao (re) = [Σma _{= 0, MA-1} (Hao (re, ma) −Aao (re)) ² ] ^1/2

  Strictly speaking, the standard deviation needs to be further divided by a constant MA within {} in the above [Equation 20]. However, since the purpose here is to calculate the correlation coefficient, it is necessary to divide by the constant MA in the term for calculating the covariance of the numerator in [Equation 21], which will be described later. The operation of dividing by the constant MA is canceled (the denominator is divided by the square root of the constant MA twice).

  Next, by using the calculated average value and standard deviation, a process according to the following [Equation 21] is executed to thereby correlate the angular distribution of the target model and the angular distribution of the restriction image corresponding to the record re. The number Da (re) is calculated.

[Formula 21]
Da (re) = [Σma _{= 0, MA-1} (Ha (ma) −Aa) × (Hao (re, ma) −Aao (re))] / (Sao (re) × Sa)

  In the above [Equation 21], the subscript “ma = 0, MA−1” of Σ indicates that the sum is obtained when ma takes all integers from 0 to MA−1. The correlation coefficient Da (re) is obtained by dividing the covariance of the frequency distributions Ha (ma) and Hao (re, ma) by the respective standard deviations. In general, the processing load is high when the square root of the denominator is divided twice as in the above equation [21]. For this reason, in the field of image processing, what is required only by the product-sum operation of the numerators is called “correlation coefficient”, and Da (re) shown in [Equation 21] is called “normalized correlation coefficient” There are also customs.

  Next, the frequency distribution matching unit 30 compares the correlation coefficient Da (re) of the angular distribution calculated in step S240 with the determination threshold value (step S250). If the correlation coefficient Da (re) is greater than or equal to the determination threshold value, it is determined as conforming, and if the correlation coefficient Da (re) is smaller than the determination threshold value, it is determined as non-conforming. The determination threshold value can be arbitrarily set as long as it is a positive value, but here, when the correlation coefficient value taking a range of +5.0 (−1 to +1) is multiplied by 100 and expressed in% dimension ).

  If it is determined to be suitable in step S250, the frequency distribution matching unit 30 determines that the output should be regulated (output inappropriate) because the target model and the regulated image of the record re are matched. Do.

  When it is determined as non-conforming in either step S230 or S250, the frequency distribution matching unit 30 finishes matching the target model with the restriction image of the record re, and proceeds to matching with the restriction image of the next record re + 1. . If the process according to the flowchart of FIG. 13 is executed for all records in the frequency distribution database 50 and there is at least one applicable restriction image, the output should be restricted to the target model (output inappropriate) ) ", The collation process in step S200 is terminated.

<2.4.5.1. Verification by Euclidean distance>
As described above, the correlation coefficient is preferably used for the collation, but the Euclidean distance can also be used. In this case, the processes in steps S220 to S250 are as follows. The frequency distribution matching unit 30 calculates the Euclidean distance between the elements based on the distance distribution calculated from the target model and the distance distribution of the restriction image extracted from the frequency distribution database 50 (step S220). Specifically, the Euclidean distance Dd2 (re) between the distance distribution of the target model and the distance distribution of the restriction image corresponding to the record re is calculated by executing processing according to the following [Equation 22]. The Euclidean distance is a distance in the Euclidean space given as a value obtained by squaring the absolute value of the difference between the frequencies of each element. The Euclidean distance Dd2 (re) is obtained as a distance between two points in the MD / K-dimensional Euclidean space.

[Formula 22]
Dd2 (re) = Σi _{= 0, MD / K-1} [{Σk _{= 0, K-1} Hdo (re, i × K + k) / K−Σ _{k = 0, K−1} Hd (i × K + k) / K} ² × K / MD] ^1/2

  In the above [Expression 22], the subscript “i = 0, MD / K−1” of Σ indicates that the sum is obtained when i takes all integers from 0 to MD / K−1. The subscript “k = 0, K−1” indicates that the sum is obtained when md takes all integers from 0 to MD-1. In the above [Equation 22], K is an allowable range of the positional deviation of distance. K can be arbitrarily set. For example, when MD = 360, K = 10. k is an integer that takes values of k = 0,..., K−1. The allowable range K is used to eliminate the influence when the reference point fluctuates due to a slight change in the shape of the target model (the fineness of the polygon configuration changes or the shape decoration changes slightly). Only if you want to). By providing the permissible range K, even if there is a slight variation in the distance distribution of the target model, it is possible to accurately collate with the restriction image.

  Next, the frequency distribution matching unit 30 compares the Euclidean distance Dd2 (re) of the distance distribution calculated in step S220 with the determination threshold (step S230). The determination by comparison with the determination threshold is opposite to the case of the correlation coefficient. That is, when the Euclidean distance Dd2 (re) is smaller than the determination threshold value, it is determined as conforming, and when the Euclidean distance Dd2 (re) is greater than or equal to the determination threshold value, it is determined as nonconforming. The determination threshold can be arbitrarily set, but can be set to 0.5, for example.

  If it is determined in step S230 that the frequency distribution is matched, the frequency distribution matching unit 30 calculates the mutual Euclidean distance between the angular distribution calculated from the target model and the angular distribution of the restriction image extracted from the frequency distribution database 50. Is calculated (step S240). Specifically, the Euclidean distance Da2 (re) between the angular distribution of the target model and the angular distribution of the restriction image corresponding to the record re is calculated by executing processing according to the following [Equation 23]. The Euclidean distance Da2 (re) is obtained as a distance between two points in the MA dimensional Euclidean space.

[Formula 23]
Da2 (re) = Σma _{= 0, MA-1} [{Hao (re, ma) −Ha (ma)} ² / MA] ^1/2

In the above [Equation 23], the subscript “ma = 0, MA-1” of Σ is the following [...] ^{1/2 in} the case where ma takes all integers from 0 to MA-1. The sum is calculated.

  Next, the frequency distribution matching unit 30 compares the Euclidean distance Da2 (re) of the angular distribution calculated in step S240 with the determination threshold (step S250). The determination by comparison with the determination threshold is opposite to the case of the correlation coefficient. That is, when the Euclidean distance Da2 (re) is smaller than the determination threshold value, it is determined as conforming, and when the Euclidean distance Da2 (re) is greater than or equal to the determination threshold value, it is determined as nonconforming. The determination threshold can be arbitrarily set, but can be set to 0.5, for example.

<3. Second Embodiment>
Next, a second embodiment will be described. In the first embodiment, the frequency distribution of the restriction image is created in advance by the image frequency distribution calculation device, and is registered in the frequency distribution database of the three-dimensional object formation data output restriction device. In the second embodiment, the restriction image itself is registered in the restriction image database, and the two types of frequency distributions are created on the spot from the restriction image in real time, and the process is performed up to collation.

<3.1. Device configuration>
FIG. 14 is a functional block diagram illustrating a configuration of the three-dimensional object formation data output restriction device according to the second embodiment of the present invention. In FIG. 14, 10 is a model frequency distribution calculating unit, 30 is a frequency distribution matching unit, 40 is a target model storage unit, 51 is a regulated image database, and 92 is an image frequency distribution calculating unit. 14, components having functions equivalent to those in FIGS. 3 and 10 are denoted by the same reference numerals, and detailed description thereof is omitted.

  The model frequency distribution calculation means 10 performs a process of calculating a distance distribution that is a distance frequency distribution and an angle distribution that is an angle frequency distribution on the target model, as shown in FIG. The image frequency distribution calculation unit 92 performs a process of calculating a distance distribution that is a distance frequency distribution and an angle distribution that is an angle frequency distribution on the restriction image, as in the case illustrated in FIG. 3. Whether the frequency distribution matching means 30 should collate the distance distribution and angle distribution calculated for the target model with the distance distribution and angle distribution calculated for the restriction image, respectively, and regulate the output, as shown in FIG. Whether or not the output is appropriate or not is determined.

  The model frequency distribution calculating means 10, the image frequency distribution calculating means 92, and the frequency distribution matching means 30 are realized by the CPU 1 shown in FIG. 9 executing a program stored in the storage device 3. The target model storage unit 40 is a storage unit that stores a target model, similar to that illustrated in FIG. 10, and is realized by the storage device 3.

  The restriction image database 51 is a storage unit that stores restriction images, and is realized by the storage device 3.

  Each component shown in FIG. 14 is actually realized by installing a dedicated program on hardware such as a computer and its peripheral devices as shown in FIG. That is, the computer executes the contents of each means according to a dedicated program.

  The storage device 3 shown in FIG. 9 is mounted with a dedicated program for operating the CPU 1 and causing the computer to function as a three-dimensional object formation data output restriction device. By executing this dedicated program, the CPU 1 realizes functions as the model frequency distribution calculating unit 10, the image frequency distribution calculating unit 92, and the frequency distribution matching unit 30. The storage device 3 not only functions as the target model storage unit 40 and the restriction image database 51 but also stores various data necessary for processing as the three-dimensional object formation data output restriction device.

<3.2. Processing action>
Next, the processing operation of the three-dimensional object formation data output restriction device shown in FIG. 14 will be described. FIG. 15 is a flowchart showing an outline of processing of the three-dimensional object formation data output restriction device shown in FIG. 14. First, the model frequency distribution calculating means 10 calculates a distance distribution and an angle distribution, which are two types of frequency distributions, for the target model (step S100). Next, the image frequency distribution calculation unit 92 calculates a distance distribution and an angle distribution, which are two types of frequency distribution, for the restricted image (step S300). Then, the calculated frequency distribution of the target model is collated with the frequency distribution of the restriction image (step S200). The calculation processing of the image frequency distribution in step S300 and the matching processing of the frequency distribution in step S200 are incompatible when it is determined in the matching processing that the output should not be output (unsuitable for output) or in matching with all the regulated images. When it is determined that the output is appropriate, the process ends. The processing in step S100 and step S200 is the same as that shown in FIG. The processing in step S300 is the same as the frequency distribution calculation processing shown in FIG.

<4. Usefulness of distance distribution and angular distribution>
Here, in the first and second embodiments described above, for the convenience of explanation, the usefulness of performing the collation using the two types of frequency distributions of the distance distribution and the angle distribution of the three types of three-dimensional polygon models. As will be described later, the same description holds true for two-dimensional images each represented by a two-dimensional illustration (the spherical models in FIGS. 16 and 17 are regarded as circular models). FIG. 16 is a diagram showing the distance distribution and the angular distribution obtained for each of the two spherical models A and B. 16A shows the sphere model A, and FIGS. 16B and 16C show the distance distribution and the angle distribution of the sphere model A, respectively. 16D shows the sphere model B, and FIGS. 16E and 16F show the distance distribution and angle distribution of the sphere model B, respectively.

  The sphere model A shown in FIG. 16A is a sphere model in which there is no cavity inside and the contents are clogged. When there is no cavity inside, all the normal vectors of all the polygons face outward as indicated by an arrow pointing upward in FIG. A sphere model B shown in FIG. 16D is a sphere model having a cavity inside. When there is a cavity inside, as shown in FIG. 16D, there are both normal vectors of polygons that face outward (toward the upper right in the figure) and those that face inward (toward the lower left in the figure). Since both the sphere model A and the sphere model B are spheres, the reference point corresponding to the center of gravity is located at the center of the sphere. In FIGS. 16A and 16D, the arrows extending in the left-right direction indicate the distance from the reference point to the polygon.

  In the sphere model A, since polygons are distributed equidistant from the reference point, the distance distribution of the sphere model A becomes higher with only a certain distance protruding as shown in FIG. In the sphere model B, since the outer peripheral portion has a thickness, the distance from the reference point is slightly different between the outer polygon and the inner polygon. Therefore, as shown in FIG. 16 (e), the distance distribution of the sphere model B increases with only two points protruding from the inner polygon and the outer polygon. However, if there is no significant difference between the distance to the inner polygon and the distance to the outer polygon, the two peaks in FIG. 16 (e) are integrated and added at the same position to match the distance distribution of the sphere model A. Distribution.

  Further, in the sphere model A, the normal vector is in a direction from the reference point (in this case, the center of the sphere) to the outside in order to make a state in which there is no cavity and the contents are clogged. Therefore, the angular distribution of the angle formed by the vector from the reference point to the polygon and the normal vector of the polygon increases as shown in FIG. In the sphere model B, the normal vector of the outer polygon faces outward from the reference point (the center of the sphere), but in order to form a cavity, the normal vector of the inner polygon faces the reference point. Yes. For this reason, the angular distribution becomes high by protruding only at two locations near 0 ° and near 180 ° as shown in FIG.

  As shown in FIGS. 16 (b) and 16 (e), the distance distribution is a distribution that protrudes by a predetermined distance and becomes high, and there is no significant difference. Therefore, when collation of the sphere model A and the sphere model B is performed using only the distance distribution, there is a high possibility that the sphere model A and the sphere model B are determined to be compatible (similar). However, as shown in FIGS. 16C and 16F, in the angular distribution, the distribution around 180 ° is greatly different. For this reason, it can determine with both differing by performing collation of spherical model A and spherical model B using angle distribution.

  The usefulness of performing collation using two types of frequency distributions, distance distribution and angle distribution, will be described using a second example. FIG. 17 is a diagram showing the distance distribution and the angular distribution obtained for each of the two spherical models B and C. FIG. 17A shows the sphere model B, and FIGS. 17B and 17C show the distance distribution and the angle distribution of the sphere model B, respectively. 17A to 17C are the same as FIGS. 16D to 16F. FIG. 17D shows the sphere model C, and FIGS. 17E and 17F show the distance distribution and angle distribution of the sphere model C, respectively.

  A sphere model C shown in FIG. 17D is a sphere model having a thicker outer peripheral portion and a smaller volume of the cavity than the sphere model B. Since there is a cavity inside, the normal vector of the polygon faces both the outside and the inside as shown in FIG. Since both the sphere model B and the sphere model C are spheres, the reference point corresponding to the center of gravity is located at the center of the sphere. In FIG. 17D, large and small arrows extending in the left-right direction indicate the distances from the reference point to the outer polygon and the inner polygon, respectively.

  In the sphere model B, the normal vector of the outer polygon faces outward from the reference point (the center of the sphere), but in order to form a cavity, the normal vector of the inner polygon faces the reference point. Yes. Therefore, as shown in FIG. 17 (c), the angular distribution becomes high by protruding only at two locations near 0 ° and 180 °. Even in the sphere model C, the normal vector of the outer polygon faces outward from the reference point (the center of the sphere), but the normal vector of the inner polygon faces the reference point in order to form a cavity. Yes. For this reason, the angular distribution becomes high by protruding only at two locations near 0 ° and 180 ° as shown in FIG. Since the outer surface area is larger and the total area of the polygon is larger, the distribution near 0 ° is larger than the distribution near 180 °.

  In the sphere model B, since a thin layer exists on the outer periphery, the distance from the reference point is slightly different between the outer polygon and the inner polygon. For this reason, as shown in FIG. 17B, the distance distribution of the sphere model B increases with only two points protruding from the inner polygon and the outer polygon. However, since there is no significant difference between the distance to the inner polygon and the distance to the outer polygon, the two protruding portions are close to each other. In the sphere model C, since a thick layer exists on the outer periphery, the distance from the reference point is greatly different between the outer polygon and the inner polygon. For this reason, as shown in FIG. 17E, the distance distribution of the spherical model C becomes high with only two points protruding from the distance to the inner polygon and the distance to the outer polygon. However, since there is a large difference between the distance to the inner polygon and the distance to the outer polygon, the two protruding portions are separated.

As shown in FIGS. 17 (c) and 17 (f), the angular distributions are distributions in which only two portions near 0 ° and 180 ° protrude and become high, and there is no significant difference. Therefore, when collation between the sphere model B and the sphere model C is performed using only the angular distribution, there is a high possibility that the sphere model B and the sphere model C are determined to be compatible (similar). However, as shown in FIGS. 17B and 17E, in the distance distribution, the distribution of the distance to the inner polygon is greatly different. For this reason, by comparing the spherical model B and the spherical model C using the distance distribution, it can be determined that the two are different. As in the two examples shown in FIGS. 16 and 17, it is possible to more reliably determine whether or not the two polygon models are significantly different by using two types of frequency distributions, an angle distribution and a distance distribution. Is possible.
Although the above has been described using a three-dimensional polygon model as an example, the same description holds for a two-dimensional image such as a restriction image. As shown two-dimensionally in FIGS. 16 (a), 16 (d) and 17 (d), FIG. 16 (a) is a circular image with a solid interior, and FIG. 16 (d) is bordered. The circular image, FIG. 17 (d), is assumed to be a circular image with a thick border, and these are regarded as restricted images. 16 (b), (c), FIGS. 16 (e), (f), and FIGS. 17 (e), (f), the results are similar to those shown in FIGS. The angular distribution of the model and the circular model is not exactly the same). When the sphere model A, sphere model B, and sphere model C are opaque, the difference between the three sphere models cannot be confirmed in the two-dimensional projection image. Results in similar results. However, the frequency distribution converted from each of the circular illustration image with a filled interior, the circular illustration image with a border, and the circular illustration image with a thick outline is converted from the sphere model A, the sphere model B, and the sphere model C. Three types of sphere models can be identified by matching with the frequency distribution and matching with the three types of circular illustration images.

<Data output to 5.3D printer>
In step S200, when the frequency distribution matching unit 30 determines that “the 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. On the other hand, if the frequency distribution matching unit 30 determines that “the output should be regulated (output inappropriate)”, the target model is not output to the 3D printer 7 which is a three-dimensional object forming apparatus. If it takes a long time to determine whether output is appropriate or not, whether the target model is output to the 3D printer 7 and whether the output is appropriate or inappropriate in parallel with the output process (three-dimensional object modeling process) of the 3D printer 7. If the output is unsuitable, an output stop command may be output to the 3D printer 7. At this time, from the user's point of view, it is only possible to confirm that the three-dimensional object shaping process in the 3D printer is started by issuing a single command to output the target model. I do not notice that the execution of the process for is started.

<6. Polygon reduction processing>
Prior to the frequency distribution calculation processing in step S100 shown in FIG. 11, polygon reduction processing for reducing the number of polygons of the target model may be performed. By reducing polygons with low importance in advance, it is possible to determine whether or not the output is appropriate at high speed while allowing a difference in the fine shape of the polygon model. The polygon reduction process prior to the frequency distribution calculation process will be described below. Polygon reduction processing is performed by polygon reduction means realized by the CPU 1 shown in FIG. 9 executing a program stored in the storage device 3.

  FIG. 18 is a diagram showing the basic principle of polygon deletion by the polygon reduction means. The polygon set is actually a polygon group having a three-dimensional value in space, but the example of FIG. 18 shows a polygon group having a two-dimensional value for convenience of explanation. In the example of FIG. 18A, a polygon group composed of 18 polygons is shown before deletion.

  FIG. 19 is a flowchart showing details of polygon reduction processing by the polygon reduction means. First, the polygon reduction means calculates the area and perimeter of each polygon for all polygons constituting the target model (step S11). Next, the polygon reduction means selects a polygon having a minimum value obtained by multiplying the calculated area and circumference as a polygon to be deleted (step S12). In the example of FIG. 18, as shown in FIG. 18B, a polygon EJK composed of three vertices E, J, and K is selected as a deletion target polygon.

  Subsequently, the polygon reduction means selects the shortest one of the three sides of the deletion target polygon as the deletion target side, and calculates the midpoint of the selected deletion target side (step S13). In the example of FIG. 18, as shown in FIG. 18C, the side JK of the deletion target polygon EJK is selected as the deletion target side. Further, the midpoint O of the side JK is calculated.

  Next, the polygon reduction means deletes the deletion target polygon (step S14). As a result, the number of polygons is reduced by one. Next, the polygon reduction means sequentially selects the deleted vertex shared polygon that shared the vertex with the deletion target side of the deletion target polygon (step S15). When a deleted vertex shared polygon is selected that shares two vertices with the deletion target side of the polygon to be deleted (that is, the side is shared), the polygon reduction means deletes the deleted vertex shared polygon (step S16). . In the example of FIG. 18, a polygon JKN composed of three vertices J, K, and N is deleted. This further reduces the number of polygons by one.

  When a deleted vertex shared polygon that shares one vertex with the deletion target side of the deletion target polygon is selected, the polygon reduction means corrects the shared vertex to the midpoint calculated in step S13, and calculates the area and the circumference. The length is recalculated (step S17). In the example of FIG. 18, as can be seen by comparing FIG. 18C and FIG. 18D, polygon EHJ → polygon EHO, polygon EFK → polygon EFO, polygon HJM → polygon HOM, polygon JMN → polygon OMN, and polygon FKN. → Changed to polygon FON. As a result, in the example of FIG. 18, 18 polygons at the time of FIG. 18 (a) are reduced to 16 at the time of FIG. 18 (d).

  When the processing of step S15 to step S17 is completed, the polygon reduction means determines whether the total number of polygons after the reduction is equal to or less than the set target value (step S18). As the target value, an arbitrary value can be set in advance. If the total number of polygons is not less than or equal to the target value as a result of the determination, the process returns to step S12, and the polygon reduction means selects the polygon with the smallest value obtained by multiplying the area and circumference from the remaining polygons. Are selected as polygons to be deleted. If the result of determination in step S18 is that the total number of polygons is less than or equal to the target value, the polygon reduction process is terminated. By executing the processing according to the flowchart of FIG. 19, the number of polygons is reduced so as to be equal to or less than the set target value.

<7. Target model division processing>
Prior to the frequency distribution calculation process shown in FIG. 11, a target model dividing process for dividing the target model may be performed. By dividing the target model in advance, it is possible to appropriately determine whether or not the output is appropriate even when the restriction model and the target model have different component configurations. The target model division process prior to the frequency distribution calculation process will be described below. The target model dividing process is performed by a target model dividing unit realized by the CPU 1 shown in FIG. 1 executing a program stored in the storage device 3.

  FIG. 20 is a flowchart showing details of the target model dividing process by the target model dividing means. The target model dividing unit first performs initial setting (step S21). Specifically, the number of polygons P constituting the polygon set of the read target model, variables p (p = 0,..., P−1) for specifying polygons, search status S (p), group attribute G ( p), a variable g for specifying a group and a variable s indicating a search status are set. The search status S (p) takes an integer value of 0 or more. S (p) = 0 is a state in which the polygon p is not classified into groups, and S (p)> 0 is a state in which the polygon p is classified into the group attribute G (p) in step S22 executed for the S (p) th time. Indicates. Further, in step S21, all p of p = 0,..., P−1 are initialized as S (p) = G (p) = 0, g = 1, and s = 1.

  Next, one polygon p satisfying S (p) = G (p) = 0 is extracted from all the polygons (step S22). Since S (p) = G (p) = 0 in the initial state, polygons with p = 0 are always extracted at the first time. In step S22, when there is no polygon p satisfying S (p) = G (p) = 0, that is, when S (p)> 0 for all p = 0,..., P−1. , The target model dividing process is terminated assuming that the target model can be divided into g-1 groups and the target model can be divided into g-1 partial target models. In step S22, since there is no state where S (p) <0, S (p) is either “0” or a positive value. When a polygon p satisfying S (p) = G (p) = 0 can be extracted, the search status S (p) = s and the group attribute G (p) = g corresponding to the extracted polygon p are set. Further, an update counter c = 0, which is a variable for counting updates, is set.

  In step S22, one polygon p satisfying S (p) = G (p) = 0 is extracted, and the search status S (p) = s and group attribute G (p) = g corresponding to the extracted polygon p are set. If set, one polygon q satisfying S (q) = s is extracted from all polygons (step S23). q is a variable for specifying a polygon, and takes an integer value in q = 0,... When there are a plurality of polygons q with S (q) = s, for example, the one having the smallest value of q is extracted.

  When one polygon q satisfying S (q) = s is extracted in step S23, three polygons r1, r2, and r3 that share sides with the extracted polygon q are searched from all the polygons. For the polygon r satisfying S (r) = G (r) = 0 among the three searched neighboring polygons r1, r2, r3, the search status S (r) = s + 1 and the group attribute G (r), respectively. = G, and the value of c is updated to c ← c + 1 (step S24). r is a variable for specifying one of the polygons r1, r2, and r3, and takes any integer value in r = 0,..., P−1, similarly to the polygons p and q. “Sharing sides of polygon q and adjacent polygon r” means that two vertices of polygon q and two vertices of polygon r are the same. That is, any one side of the adjacent polygon r is the same as any one side of the polygon q.

  In the present embodiment, since the polygon is a triangle, there are always three adjacent polygons that share one side with respect to the three sides constituting the polygon. If this condition is not satisfied when the screen is displayed with computer graphics, there is no problem even if this condition is not satisfied. However, if the output is performed with a 3D printer, it becomes impossible to physically form a modeled object. If not, an error warning is issued and the printer output is generally not executed.) Among the three extracted adjacent polygons, for the polygon r satisfying S (r) = G (r) = 0, search status S (r) = s + 1, group attribute G (r) = g, c ← Set to c + 1.

  Then, returning to step S23, if there is another polygon q with S (q) = s from all the polygons, one more is extracted, and step S24 is executed in the same manner. That is, by repeating the processing of step S23 and step S24, the processing of step S24 is executed for all the polygons for which S (q) = s in the target model. In step S23, when there is no polygon q satisfying S (q) = s, that is, when three adjacent polygons are searched for all the polygons q satisfying S (q) = s, the variable s indicating the search status is set to 1. Increment (s ← s + 1) (step S25).

  Next, it is determined whether or not the update counter c = 0 (step S26). As a result of the determination, if c = 0 is not satisfied, at least one polygon r satisfying S (r) = G (r) = 0 exists among the three polygons adjacent to the polygon q in step S24. Therefore, there is no other polygon p with an undefined group attribute to be included in the same group g, or the process returns to step S22, and S (p) = G (p) = 0 from all the polygons. Further search is made for the polygon p that becomes, and if it exists, one is extracted, and the search status S (p) = s, group attribute G (p) = g, and update counter c = 0 corresponding to the extracted polygon p are set. To do. Then, as described above, the process goes to the processing loop of step S23 and step S24, and the process of searching for three adjacent polygons for all the polygons q where S (q) = s is repeated.

  In this way, the processes in steps S22 to S25 are repeatedly executed, and when the update counter c = 0 as a result of the determination in step S26, that is, all three polygons adjacent to the polygon q in step S24 are detected. If there is no undefined polygon r that is already set to the same group g and does not have any group attribute satisfying S (r) = G (r) = 0 to be included in the group g, it is considered that the classification to the group g is completed. In order to shift to the next group g + 1, the variable g for specifying the group is incremented by 1 (g ← g + 1) (step S27). Then, the process returns to step S22, and classification into a new group g is similarly performed. If, in step S22, no polygon p having an undefined group attribute with S (p) = G (p) = 0 is found, the group attribute of all P polygons is set to any value. Therefore, it can be considered that the classification process has been completed. At this time, since the group g updated in step S27 is not set as the group attribute of any polygon, all P polygons are classified into g-1 groups.

  By executing the processing according to the flowchart of FIG. 20, all P polygons are classified into groups for each polygon set that is connected by sharing two vertices. This classified group becomes a partial target model. In the example of FIG. 20, each polygon of the target model belongs to one of g−1 partial target models. As a result, the target model is divided into g-1 partial target models.

  The polygon reduction process by the polygon reduction unit and the target model division process by the target model division unit, which are performed prior to the frequency distribution calculation process, may be performed in either order. If possible, it is more efficient to perform the target model division process after performing the polygon reduction process. Further, only one of the polygon reduction process and the target model division process may be performed.

<8. Calculation and registration of regulatory image and regulatory model frequency distribution>
As a modified example, the distance distribution and the angle distribution calculated with respect to the restriction image and the restriction model at the place different from the three-dimensional object formation data output restriction device including the frequency distribution matching unit 30 are used as the three-dimensional object formation data. You may make it transmit and register to an output control apparatus via a network.

  FIG. 21 is a hardware configuration diagram of a three-dimensional object formation system including a three-dimensional object formation data output restriction device according to a modification. In the three-dimensional object modeling data output system shown in FIG. 21, the three-dimensional object modeling data output restriction device 101 communicates with the public network such as the Internet in the configuration of the three-dimensional object shaping data output restriction device 100 shown in FIG. 9. The network communication unit 8 is provided. The image frequency distribution calculation device 301 can be realized by a general-purpose computer. As shown in FIG. 21, a CPU (Central Processing Unit) 1a, a RAM (Random Access Memory) 2a that is a main memory of the computer, and a CPU 1a A large-capacity storage device 3a such as a hard disk or flash memory for storing programs and data executed by the computer, a key input I / F (interface) 4a such as a keyboard and a mouse, and an external device such as a data storage medium and data A data input / output I / F (interface) 5a for communication, a display unit 6a, which is a display device such as a liquid crystal display, and a network communication unit 8a for communicating with a public network such as the Internet, are connected via a bus. Connected. The network communication unit 8 of the three-dimensional object formation data output restriction device 101 and the network communication unit 8a of the image frequency distribution calculation device 301 communicate with each other, and the image frequency distribution calculation device 301 restricts to the three-dimensional object formation data output restriction device 101. It is possible to transmit two types of frequency distribution calculated for an image.

  In FIG. 21, the three-dimensional object modeling data output restriction device 101 and the 3D printer 7 are shown in a separated form, but most of the currently available 3D printer products include the three-dimensional object shaping data output restriction device 101. Constituent elements, such as CPU1, RAM2, storage device 3, key input I / F4 (not a keyboard / mouse for general-purpose computers, but several buttons at a numeric keypad level), data input / output I / F5, display unit 6 (several lines) A small liquid crystal panel capable of displaying the above characters, a touch panel is often superimposed to serve also as a key input I / F 4), and a network communication unit 8 (wireless LAN function) is provided in a small but overlapping manner. Therefore, the 3D printer 7 itself can directly receive the data for modeling a three-dimensional object via an external storage medium or the Internet, and can be operated to model a three-dimensional object alone (particularly in the case of a consumer 3D printer, this form Many). That is, the three-dimensional object formation data output restriction device 101 and the 3D printer 7 shown in FIG. 21 are often housed in one housing and distributed as a product called “3D printer”.

  In the image frequency distribution calculation device 301, the CPU 1a executes a program stored in the storage device 3a, thereby realizing an image frequency distribution calculation unit and a frequency distribution transmission unit. The image frequency distribution calculating means realized by the image frequency distribution calculating apparatus 301 has the same function as that of the image frequency distribution calculating means 92 realized by the three-dimensional object shaping data output restricting apparatus 100. A distance distribution and an angle distribution are calculated as the seed frequency distribution. The frequency distribution transmission unit transmits the distance distribution and the angle distribution, which are two types of frequency distributions calculated for the restriction image, to the three-dimensional object formation data output restriction device 101. In the three-dimensional object shaping data output restriction device 101, when the network communication unit 8 receives the frequency distribution from the image frequency distribution calculation device 301, the CPU 1 executes a predetermined program to function as a frequency distribution registration unit, and the received frequency The distribution is registered in the frequency distribution database 50 realized by the storage device 3.

<9. Cloud-type 3D object modeling system>
The present invention can also be applied to a cloud-type three-dimensional object modeling system. FIG. 22 is a hardware configuration diagram of a cloud-type three-dimensional object modeling system. In the three-dimensional object modeling system shown in FIG. 22, the three-dimensional object modeling data output restriction device is realized by the output control terminal 201 and the processing server 202. In the three-dimensional object formation system shown in FIG. 22, the output control terminal 201 has a hardware configuration equivalent to the computer shown as the three-dimensional object formation data output restriction device 101 in FIG. That is, the output control terminal 201 includes a CPU 11, a RAM 12, a storage device 13, a key input I / F 14, a data input / output I / F 15, a display unit 16, and a network communication unit 18, which are connected to each other via a bus. .

  The processing server 202 can be realized by incorporating a dedicated program into a general-purpose computer. As shown in FIG. 22, 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. Yes. The network communication unit 18 of the output control terminal 201 and the network communication unit 18a of the processing server 202 communicate with each other, and send output suitability data from the processing server 202 to the output control terminal 201 based on the judgment of output suitability. It is possible. In FIG. 22, the output control terminal 201 and the 3D printer 7 are shown in a separated form, but as in the example of FIG. 21, 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 provided in an overlapping manner.

  FIG. 23 is a functional block diagram of a cloud-type three-dimensional object modeling system. The processing server 202 constituting the cloud-type three-dimensional object modeling system includes a target model receiving unit 60 and an output suitability data transmitting unit 70 in addition to each unit of the three-dimensional object modeling data output restriction device shown in FIG. It has a configuration. The target model storage means 41 stores not the target model itself but two kinds of frequency distributions calculated for the target model. Further, the model frequency distribution calculating means 10 has a configuration provided in the output control terminal 201 instead of the processing server 202.

  Components having the same functions as those of the three-dimensional object formation data output restriction device shown in FIG. The target model receiving means 60 is means for receiving the frequency distribution of the target model transmitted from the output control terminal 201 and registering it in the target model storage means 41. The CPU 11a executes a predetermined program and the network communication section This is realized by controlling 18a. The output adequacy data transmission means 70 is means for transmitting the output adequacy data to the output control terminal 201 based on “whether or not the output should be regulated” determined by the frequency distribution matching means 30, and the CPU 11a This is realized by executing a predetermined program and controlling the network communication unit 18a.

  The processing server 202 is connected to a network such as the Internet and is accessible from a number of output control terminals. The “cloud type” of the cloud-type three-dimensional object modeling system is to determine whether or not to regulate on the remote computer via the network from the output side, not the output side that outputs the three-dimensional object by the 3D printer. Means. In the conventional server type computer, when the access of many users is concentrated, the responsiveness becomes slow and it often causes trouble for the user. In the cloud type, the physical configuration of the computer is dynamically changed by the virtualization technology. This makes it possible to always maintain a stable response. Generally, the processing server 202 is physically realized by a plurality of computers.

  The processing operation of the cloud-type three-dimensional object modeling system shown in FIGS. 22 and 23 will be described. In the output control terminal 201, when the user specifies a target model to be output via the key input I / F 14, the CPU 11 extracts the specified target model stored in the storage device 13 and specifies the target model. A model ID which is identification information to be assigned is assigned. Then, the CPU 11 performs processing on the target model to which the model ID is assigned according to the flowchart shown in FIG. 12, and generates a frequency distribution composed of a distance distribution and an angle distribution. . Further, the CPU 11 transmits the generated frequency distribution to an address such as a URL set in advance in the storage device 13 via the network communication unit 18. By using a method of sending a frequency distribution with a significantly smaller amount of information than polygon data, not only the transmission time is greatly shortened, but the polygon data that is a copyrighted work is not sent to the server side, and the server side Because no duplicates remain, copyright infringement can be avoided. In parallel, the CPU 11 transmits the target model to which the model ID is assigned to the data processing unit 7a of the 3D printer 7 via the data input / output I / F 15. The target model received from the output control terminal 201 is held in the printer queue in the data processing unit 7a, and enters a standby state as an output job. At this time, it is possible to execute only the process of converting the polygon format data, which is the pre-processing in the 3D printer output, into the data in the stack format, and to enter the standby state when the data is converted into the stack format data. . Since this data processing load is also reasonably high, if the output suitability determination is completed during this time, it is possible to prevent the user from feeling an extra waiting time.

  In the processing server 202, when the target model receiving unit 60 receives the frequency distribution of the target model transmitted from the output control terminal 201, the target model storage unit 41 stores the frequency distribution in the target model storage unit 41. Here, according to the flowchart shown in FIG. 11, the frequency distribution matching means 30 performs processing, and determines whether the target model corresponding to the received frequency distribution is suitable for output. Output propriety data as a result of output propriety is passed from the frequency distribution matching unit 30 to the output propriety data transmitting unit 70. Then, the output suitability data transmission means 70 transmits the output suitability data with the model ID added to the output control terminal 201 that is the transmission source (access source) of the frequency distribution.

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

<10. Frequency distribution calculation example>
24 and 25 show frequency distribution calculation examples by the model frequency distribution calculation process (step S100) by the three-dimensional object formation data output restriction device according to the embodiment. 24 and 25, the polygon model is shown on the left side and the frequency distribution is shown on the right side. In the frequency distribution, the upper part shows a distance distribution (Distance), and the lower part shows an angular distribution (Angle).

  FIG. 24A is a diagram showing a polygon model (1224 polygon) of a sphere a (actually a polyhedron). For a spherical polygon model as shown in FIG. 24 (a), as shown in FIG. 24 (b), only a specific distance protrudes and a high distance distribution and only a vicinity of 0 ° protrudes and a high angular distribution exists. can get. From the obtained distance distribution and angle distribution, it can be seen that the polygon model in FIG. 24A is for outputting a sphere filled with the contents. FIG. 25A is a diagram showing a polygon model (15944 polygons) of Takuma b. For a polygon model with many irregularities as shown in FIG. 25 (a), as shown in FIG. 25 (b), a distance distribution and an angle distribution with more variations than a polyhedron close to a sphere are obtained.

  FIG. 26 and FIG. 27 show frequency distribution calculation examples by the calculation processing (FIG. 4) of the image frequency distribution by the three-dimensional object shaping data output restriction device and the image frequency distribution calculation device according to the above embodiment. 26 and 27, an image is shown on the left side and a frequency distribution is shown on the right side. In the frequency distribution, the upper part shows a distance distribution (Distance), and the lower part shows an angular distribution (Angle).

  FIG. 26A is a diagram showing a two-dimensional shading image of Tadashi c. The two-dimensional shading image of Daruma c is a two-dimensional image obtained by projecting the polygon model of Daruma b. For a two-dimensional shading image as shown in FIG. 26A, as shown in FIG. 26B, a distance distribution with a relatively long distance and an angle distribution with a relatively small angle are obtained. FIG. 27 (a) is a diagram showing a two-dimensional illustration image of Tadama d. The two-dimensional illustration image of Daruma d is an original image based on the production of the polygon model of Daruma b, and is created completely different from the two-dimensional shading image of Daruma c. For a two-dimensional illustration image as shown in FIG. 27 (a), as shown in FIG. 27 (b), as shown in FIG. 26 (b), a relatively short distance distribution and a relatively large angle are high. Distribution is obtained.

  FIG. 28 is a diagram showing a matching result by the frequency distribution matching unit 30 between the polygon models shown in FIGS. 24, 25, 26, and 27 or between the polygon model and the image. As comparison results, the calculated correlation coefficient Dd of the distance distribution (denoted as “distance correlation Dd” in the figure) and the correlation coefficient Da of the angular distribution (denoted as “angular correlation Da” in the figure) are shown. As shown in FIG. 28, it can be seen that the three-dimensional sphere a has a low correlation with the brushes b, c, and d regardless of whether it is three-dimensional or two-dimensional. On the other hand, the three-dimensional drums b have a high correlation with the two-dimensional drums c and d. For this reason, if the images of Datsuma c and d are registered in the database, it is determined that the output should be restricted (output inappropriate) when attempting to output the 3D polygon model of Datsuma b as the target model. Will be.

  FIG. 29 and FIG. 30 show an example of the frequency distribution calculated for the medical organ model by the frequency distribution calculation processing (step S100) by the three-dimensional object modeling data output restriction device according to the present embodiment. In both FIG. 29 and FIG. 30, the polygon model is shown on the left side and the frequency distribution is shown on the right side. In the frequency distribution, the upper part shows a distance distribution (Distance), and the lower part shows an angular distribution (Angle).

  When the polygon model shown in FIG. 29A and the two-dimensional shading image shown in FIG. 30A are collated by the frequency distribution collating means 30, the correlation coefficient Dd of the distance distribution is 32.93, The correlation coefficient Da of the angle distribution is 57.35. Therefore, in this case as well, it is determined that the output should be regulated.

<11. Modified example>
The preferred embodiments of the present invention have been described above. However, the present invention is not limited to the above embodiments, and various modifications can be made. For example, in the above-described embodiment, the image to be processed is a raster format expressed by pixels of monochrome 256 gradations, but may be a vector format illustration image, in which case an edge line is provided. The processing of edge point extraction by calculation can be simplified. Further, although the polygon to be processed is a triangle, it may be a quadrilateral or more.

  In the above embodiment, the frequency distribution of the restriction image and the frequency distribution of the restriction model are registered in the frequency distribution database and collated with the frequency distribution created from the target model. However, the restriction image is stored in the frequency distribution database. Although it is recommended to register itself, it is not necessary to register if permission is not obtained, and it is recommended not to register the regulatory model itself, but it may be registered if permission is obtained. The frequency distribution of the regulatory model does not necessarily need to be registered and verified.

1, 1a, 11, 11a, 81... CPU (Central Processing Unit)
2, 2a, 12, 12a, 82 ... RAM (Random Access Memory)
3, 3a, 13, 13a, 83 ... storage device 4, 4a, 14, 14a, 84 ... key input I / F
5, 5a, 15, 15a, 85 ... Data input / output I / F
6, 6a, 16, 16a, 86 ... display unit 7 ... 3D printer (three-dimensional object modeling apparatus)
7a: Data processing unit 7b: Output unit 8, 8a, 18, 18a ... Network communication unit 10 ... Model frequency distribution calculating means 30 ... Frequency distribution collating means 40, 41 ... Target Model storage means 50 ... Frequency distribution database 51 ... Restriction image database 60 ... Target model reception means 70 ... Output adequacy data transmission means 91 ... Restriction image storage means 92 ... Image frequency distribution calculation Means 93 ... Image frequency distribution storage means 100, 101 ... Three-dimensional object shaping data output restriction device 201 ... Output control terminal 202 ... Processing server 300, 301 ... Image frequency distribution calculation device

Claims (24)

A device for determining whether or not to regulate when outputting a polygon model expressed as a set of polygons to a three-dimensional object modeling apparatus as three-dimensional object modeling data,
A database in which distance distributions and angle distributions representing the characteristics of restricted images, which are images whose outputs should be restricted, are registered;
For the target model that is the polygon model of the output target, the distance from the reference point of the target model to each polygon that constitutes the target model and the angle specific to each polygon are calculated, and based on the calculation result, the distance to each polygon is calculated. Model frequency distribution calculating means for calculating a distance distribution that is a frequency distribution of distances and an angle distribution that is a frequency distribution of angles unique to each polygon;
The distance distribution and angle distribution of the target model calculated by the model frequency distribution calculating means are respectively compared with the distance distribution and angle distribution of the restriction image registered in the database to determine whether or not the output should be restricted. A frequency distribution matching means;
A data output restriction device for three-dimensional object modeling, comprising: The distance distribution and the angle distribution of the restriction image registered in the database are as follows:
For the restriction image, the pixels constituting the edge of the object are extracted as edge points, the distance from the reference point of the restriction image to each edge point of the restriction image and the angle specific to each edge point are calculated, By an image frequency distribution calculating device having an image frequency distribution calculating means for calculating a distance distribution that is a frequency distribution of distances to each edge point and an angle distribution that is a frequency distribution of angles unique to each edge point based on the calculation result The three-dimensional object shaping data output regulating device according to claim 1, wherein the three-dimensional object shaping data output regulating device is obtained. A device for determining whether or not to regulate when outputting a polygon model expressed as a set of polygons to a three-dimensional object modeling apparatus as three-dimensional object modeling data,
For the restriction image, which is an image whose output should be restricted, the pixels that constitute the edge of the object are extracted as edge points, and the distance from the reference point of the restriction image to each edge of the restriction image and each edge point specific Image frequency distribution calculating means for calculating an angle of the distance, and calculating a distance distribution that is a frequency distribution of distances to each edge based on a calculation result and an angle distribution that is a frequency distribution of angles unique to each edge point;
For the target model that is the polygon model of the output target, the distance from the reference point of the target model to each polygon that constitutes the target model and the angle specific to each polygon are calculated, and based on the calculation result, the distance to each polygon is calculated. Model frequency distribution calculating means for calculating a distance distribution that is a frequency distribution of distances and an angle distribution that is a frequency distribution of angles unique to each polygon;
Whether the distance distribution and angle distribution of the restriction image calculated by the image frequency distribution calculation means and the distance distribution and angle distribution of the target model calculated by the model frequency distribution calculation means are collated, and whether the output should be restricted Frequency distribution matching means for determining whether or not
A data output restriction device for three-dimensional object modeling, comprising:   An edge that is the maximum value of intensity calculated for each pixel by applying a pre-defined filter matrix of 3 × 3 pixels in 8 directions to the values of all the pixels of the restricted image as the edge point 4. The three-dimensional object formation data output restriction device according to claim 2, wherein pixels whose intensity exceeds a predetermined threshold are extracted.   An edge that is the maximum value of the intensity calculated for each pixel by applying a predefined 3 × 3 pixel filter matrix of 16 directions to the values of all the pixels of the restricted image as the edge point. 4. The three-dimensional object formation data output restriction device according to claim 2, wherein pixels whose intensity exceeds a predetermined threshold are extracted.   The reference point of the restricted image is specified by a value obtained by multiplying the value obtained by multiplying the edge point by the edge strength of the edge point as a weight and adding the value for all the edge points by the sum of the edge strengths of all the edge points. 6. The three-dimensional object formation data output restriction device according to claim 4, wherein the three-dimensional object formation data output restriction device is provided as a point.   The image frequency distribution calculating unit obtains a vector from the reference point of the restriction image to each edge point as an edge individual vector, and the magnitude of the edge individual vector corresponds to the distance to each edge point and the edge individual vector. A distance to each edge point and an angle specific to the edge point are calculated by setting an angle formed with an edge direction vector that gives the edge strength at the edge point to be an angle specific to each edge point, The data output restriction device for three-dimensional object formation according to any one of claims 4 to 6.   In calculating the distance distribution, the image frequency distribution calculating unit prepares a one-dimensional array including a predetermined number of elements, and equally divides the range of the calculated maximum distance into the predetermined number. The distance distribution is calculated by assigning each distance to one of the elements based on the distance and counting the number corresponding to the element. The three-dimensional object modeling data output restriction device according to claim 7.   The image frequency distribution calculating means prepares a one-dimensional array composed of a predetermined number of elements to calculate the angular distribution, and after equally dividing a range of angles from 0 degrees to 180 degrees into the predetermined number, 9. The angle distribution is calculated by assigning to any of the elements based on an angle unique to each edge point and counting the value of the element. The data output regulation apparatus for solid object modeling as described in any one of these.   9. The image frequency distribution calculating means, when counting the value of the assigned element, weights with an edge strength of an edge point corresponding to the edge individual vector. The data output regulation apparatus for three-dimensional object modeling of 9.   The reference point of the target model is obtained by dividing the value obtained by multiplying the polygon average coordinates, which is the average of the vertex coordinates of each polygon, with the area of the polygon as a weight, and adding the value for all the polygons by the sum of the areas of all the polygons. The three-dimensional object formation data output restriction device according to any one of claims 1 to 10, wherein the three-dimensional object formation data output restriction device is given as a point specified by a value.   The model frequency distribution calculating means obtains a vector from the reference point to the average coordinates of each polygon as a polygon individual vector, and determines the size of the polygon individual vector as a distance to each polygon, and a polygon corresponding to the polygon individual vector. 12. The distance to each polygon and the angle unique to the polygon are calculated by setting the angle formed by the normal vector of the polygon to an angle unique to each polygon. 12. The data output restriction apparatus for three-dimensional object modeling according to one item.   In calculating the distance distribution, the model frequency distribution calculating unit prepares a one-dimensional array including a predetermined number of elements, and equally divides the range of the calculated maximum distance into the predetermined number. The distance distribution is calculated by assigning each distance to one of the elements based on the distance, and counting the number corresponding to the element. The three-dimensional object modeling data output restriction device according to claim 12.   In calculating the angle distribution, the model frequency distribution calculating unit prepares a one-dimensional array composed of a predetermined number of elements, and after equally dividing a range of angles from 0 degrees to 180 degrees into the predetermined number, 14. The angle distribution is calculated by assigning to any one of the elements based on an angle unique to each polygon and counting the value of the element. The data output restriction device for three-dimensional object modeling according to any one of the above.   15. The model frequency distribution calculating means weights the area of a polygon corresponding to the polygon individual vector when counting the value of the allocated element. The data output regulation apparatus for three-dimensional object modeling of description. The frequency distribution matching means, when matching the distance distribution and angle distribution of the target model with the distance distribution and angle distribution of the restriction image, respectively,
The correlation coefficient between the distance distributions and the correlation coefficient between the angular distributions are calculated, and the output should be regulated when both of the calculated correlation coefficients are larger than a predetermined positive threshold value. The data output restriction device for three-dimensional object formation according to any one of claims 1 to 15, wherein the determination is performed. Polygon reduction means for reducing polygons so that the number of polygons included in the target model is a predetermined value or less;
The three-dimensional object modeling data output restriction device according to any one of claims 1 to 16, wherein the model frequency distribution calculating unit performs processing on the target model from which the polygons are reduced. . A target model dividing means for dividing the target model, which is a polygon model to be output, into a plurality of partial target models;
The three-dimensional object formation data output restriction device according to any one of claims 1 to 17, wherein the model frequency distribution calculating unit performs processing on the partial target model.   The polygon is a triangle, and the target model dividing means divides the polygon so that three adjacent polygons sharing a side with the polygon belong to the same partial target model. 18. A three-dimensional object shaping data output regulation device according to 18.   For the restriction image, the pixels constituting the edge of the object are extracted as edge points, the distance from the reference point of the restriction image to each edge point of the restriction image and the angle specific to each edge point are calculated, A distance distribution that is a frequency distribution of distances to each edge point based on a calculation result and a frequency distribution calculated by an image frequency distribution calculation device that calculates an angle distribution that is a frequency distribution of angles unique to each edge point are received. The three-dimensional object formation data output restriction device according to any one of claims 1 to 19, further comprising a registration unit that registers the received frequency distribution in the database. Means for outputting the target model to a connected three-dimensional object shaping apparatus;
When it is determined that the output should be regulated by the frequency distribution matching unit that is executed in parallel with the three-dimensional object modeling process by the three-dimensional object modeling apparatus, the three-dimensional object modeling apparatus includes the target model. Means for outputting an output stop command;
The data output regulation device for three-dimensional object modeling according to any one of claims 1 to 20, further comprising: The output control terminal and the processing server are connected via a network,
The output control terminal has the model frequency distribution calculating means,
The processing server
The database;
Receiving means for receiving a distance distribution and an angle distribution of the target model from the output control terminal via a network;
The frequency distribution matching means;
Output suitability data transmission means for transmitting data based on whether the output should be regulated or not, determined by the frequency distribution matching means, to the output control terminal;
The three-dimensional object formation data output restriction device according to any one of claims 1 to 21, wherein the three-dimensional object formation data output restriction device. A data output restriction device for three-dimensional object formation according to any one of claims 1 to 22,
A three-dimensional object modeling apparatus that models a three-dimensional object using a target model whose output is permitted by the three-dimensional object modeling data output restriction apparatus;
A three-dimensional object forming system characterized by comprising:   A program for causing a computer to function as the three-dimensional object formation data output restriction device according to any one of claims 1 to 22.