JP2017207869A - Data output restriction device for solid object molding - Google Patents

Abstract

PROBLEM TO BE SOLVED: To provide a data output restriction device for solid object molding which can appropriately determine the propriety of output even when a 3D polygon model, i.e., an output object, is outputted in a form of being constituted by a plurality of components.SOLUTION: The data output restriction device comprises: a restriction model database 70 in which the frequency distribution of parameters is registered which characterize a triangle formed on the basis of three polygons in a restriction model; frequency distribution calculation means 10 for calculating parameters that characterize a triangle formed by prescribed points on three polygons selected from within an object model, and calculating the frequency distribution of parameters while weighting on the basis of the areas and normal vectors of the three polygons; frequency distribution matching means 20 for making the scale of parameters of frequency distribution of the object model matched with the scale of frequency distribution of the restriction model; and frequency distribution collation means 50 for collating the frequency distribution of the object model with the frequency distribution of the restriction model and determining whether or not to restrict outputs.SELECTED DRAWING: Figure 5

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.

JP 2015-210783 A Japanese Patent Laid-Open No. 2015-210789

  The 3D printer has an excellent feature that cannot be realized by the conventional manufacturing method that can output complex figures in a complete form including the surface pattern, but in the production of dangerous goods such as pistols, it is divided into parts. A method of outputting to a 3D printer is generally used. The reason is that there is a movable mechanism that requires precision that requires manual adjustment. In addition, if you need metal parts or electronic parts that cannot be output by a 3D printer, if you cannot output all at once due to printer output size restrictions, or if the completed model is unstable and easily collapses during modeling 3D printer output. For this reason, as in Japanese Patent Application Laid-Open No. 2004-260, partial verification of the entire model registered in the black list and the part model output by the 3D printer is required in realizing the 3D printer output restriction system.

  However, since the application of Patent Document 1 is limited to the case where polygon data having the same polygon configuration as that of the polygon model registered in the DB is included as a part, if the model shape is slightly deformed, the polygon There was a problem that the correspondence could not be taken and proper judgment could not be made. As another countermeasure, a method of registering the polygon data of the entire model and all the component model on the DB side can be considered. As a result, if the 3D printer output target model is a part model registered in the DB, it can be blocked. However, it is not possible to output a 3D printer in a form in which a plurality of parts are combined. However, it is difficult to register the DB with any combination of parts as the number of components increases.

Therefore, Patent Document 2 proposes a method of dividing a given polygon model into parts and collating them. On the DB side, feature data (also referred to as feature vectors) of the entire model and all component model are registered, and the 3D model to be output is divided into parts and collated in parts. The technique of Patent Document 2 has the following two problems.
(1) In the case of a model composed of a large number of subdivided parts, it is difficult to determine the illegality of individual parts alone (because each part is also used in an appropriate model, the appropriate model is illegal) It is easy to misjudg that there is a sex).
(2) If a modification is made to connect a composite part in an inseparable form, or a modification is made to divide a single part into a separable state, consistency with the parts registered in the DB is reduced. Cannot be judged.

  Therefore, the present invention appropriately determines whether output is appropriate even when a 3D polygon model to be output is output in a form constituted by a plurality of parts constituting an overall model registered in the DB. It is an object of the present invention to provide a three-dimensional object shaping data output regulation device that can be determined.

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 a frequency distribution of parameters characterizing a triangle formed based on three polygons in a restriction model, which is a polygon model whose output is to be restricted, is registered;
For a target model that is a polygon model to be output, parameters that characterize a triangle formed by predetermined points on three polygons selected from the target model are calculated, and the areas and normals of the three polygons are calculated. A frequency distribution calculating means for calculating a frequency distribution of the parameter while weighting based on a vector;
The frequency of creating the frequency distribution of the matched target model by performing processing for matching the scale of the parameter of the frequency distribution of the target model with the scale of the parameter of the frequency distribution of the regulatory model registered in the database Distribution matching means;
A frequency distribution matching unit that matches the frequency distribution of the matched target model with the frequency distribution of the restriction model and determines whether or not the output should be restricted;
There is provided a three-dimensional object shaping data output regulation device characterized by comprising:

  According to the first aspect of the present invention, there is provided a database in which a frequency distribution of parameters characterizing a triangle formed based on three polygons in a restriction model is registered, and an object model that is a polygon model to be output is provided. The parameter characterizing the triangle formed by the predetermined points on the three polygons selected from the target model is calculated, and the frequency distribution of the parameter is calculated while weighting based on the area and normal vector of the three polygons. Calculate and adjust the frequency distribution parameter scale of the target model to the frequency distribution parameter scale of the regulated model registered in the database, create the frequency distribution of the matched target model, and match Check whether the output should be regulated by checking against the frequency distribution of the regulated model of the target model Therefore, there is a difference in the frequency distribution calculated when the three polygons belong to the polygon model corresponding to the same part and when the three polygons belong to the polygon model corresponding to a plurality of parts. Even if the identification of individual parts is improved and the 3D polygon model to be output is output in a form composed of a plurality of parts constituting the restriction model registered in the database It is possible to appropriately determine whether or not this is appropriate.

  In the second aspect of the present invention, the parameter characterizing the triangle is a circumscribed circle radius of a triangle constituted by a predetermined point of each of the three polygons.

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

  In the third aspect of the present invention, each frequency value is corrected so as to be within a predetermined range with respect to the frequency distribution of the matched target model, and the corrected frequency value is raised to a power less than 1. An emphasis component creating means for creating an emphasis component, and a frequency distribution of the matched target model and a frequency distribution of the regulation model are multiplied by the emphasis component, respectively. Frequency distribution emphasizing means for creating an emphasis frequency distribution, and the frequency distribution collating means collates the emphasis frequency distribution of the target model with the emphasis frequency distribution of the restriction model.

  According to the third aspect of the present invention, the frequency value of the matched target model is corrected so as to be within a predetermined range, and the corrected frequency value is raised to a power of less than 1. To create the emphasis frequency distribution of the target model and the emphasis frequency distribution of the regulation model by multiplying the frequency distribution of the matched target model and the frequency distribution of the regulation model by the emphasis component, respectively. However, since the emphasis frequency distribution of the target model and the emphasis frequency distribution of the regulatory model are collated, components corresponding to the frequency distribution of the target model are generally extracted from the frequency distribution of the regulatory model, and the frequency distribution of the regulatory model is Partial verification can be performed. For this reason, even when the target model corresponds to one component of the restriction model, it is possible to determine with high accuracy whether or not the output should be restricted. In fact, when the frequency distribution of the regulatory model is multiplied by the emphasis component, not only the component corresponding to the frequency distribution of the target model but also the component corresponding to other parts not corresponding to the frequency distribution of the target model are partially emphasized. Will be. In order to suppress this emphasis, the value obtained by increasing the power of the corrected frequency is set to a real value less than 1, thereby smoothing the change in the component after the emphasis process. Accordingly, when the frequency distribution of the regulated model is multiplied by an emphasis component in which a value obtained by raising the corrected frequency value to a real value of less than 1, emphasis processing is performed on components corresponding to other parts not corresponding to the frequency distribution of the target model Can be suppressed. The value obtained by raising the corrected frequency value to a power is more preferably a real value in the range of 1/5 to 1/3.

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

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

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

  According to the fifth aspect of the present invention, an inverted random number array in which a random number array in which consecutive integers of the number of polygons N / 3 in each group are randomly replaced is created and the order from the beginning to the end of the random number array is reversed After applying the random number array to one group and the inverted random number array to the other group, and changing the order of the polygons in the group, the polygons in order from the top of each group Since the selection order of the polygons of the three groups can be set at random from the position in the three-dimensional space, the parameter that characterizes the triangle calculated by the combination of random and non-overlapping polygons can be used. Based on this, the frequency distribution can be obtained.

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

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

In the seventh aspect of the present invention,
The frequency distribution calculating means includes:
Each time the frequency distribution is calculated, the order of polygons in two groups out of the three groups is changed, and the frequency distribution is recalculated a predetermined number of times, and the calculated frequency is calculated. The average value of the distribution is a frequency distribution to be verified.

  According to the seventh aspect of the present invention, every time the frequency distribution is calculated, the process of calculating the frequency distribution again by changing the order of the polygons in two groups out of the three groups a predetermined number of times, Since the average value of the calculated frequency distribution is repeatedly set as the frequency distribution to be collated, the frequency distribution is calculated based on the new N triangles that do not overlap with the already calculated N triangles. Therefore, since the frequency distribution is updated, it is possible to obtain an accurate frequency distribution so that the frequency distribution is not biased to a peculiar form when there are too few triangular samples that are combinations of three polygons. .

In the eighth aspect of the present invention,
The frequency distribution calculating means includes:
Each time the frequency distribution is calculated, the order of polygons in two groups among the three groups is changed, and the process of calculating the frequency distribution is repeated.
When the frequency distribution immediately after the calculation is compared with the frequency distribution obtained immediately before that, and the similarity is found as a result of the comparison, the frequency distribution immediately after the calculation is the frequency distribution to be verified. It is characterized by that.

  According to the eighth aspect of the present invention, every time the frequency distribution is calculated, the order of the polygons in two groups among the three groups is changed, and the process of calculating the frequency distribution is repeated to calculate the frequency distribution. When the frequency distribution immediately after and the frequency distribution obtained immediately before are compared, and the similarity is found as a result of the comparison, the frequency distribution immediately after the calculation is set as the frequency distribution to be verified. It is possible to obtain an optimal frequency distribution while automatically determining whether or not the frequency distribution is likely to be biased to a peculiar form when there are too few triangular samples that are combinations of two polygons.

In the ninth aspect of the present invention,
In calculating the frequency distribution, the frequency distribution calculating unit prepares a one-dimensional array composed of a predetermined number of elements, and equally divides the calculated parameter maximum value range into the predetermined number Above, each parameter is assigned to one of the predetermined number of elements (ma) based on the value of the parameter, and a weight value calculated based on the area and normal vector of the three polygons is added to the element. Thus, the frequency distribution is calculated.

  According to the ninth aspect of the present invention, in calculating the frequency distribution, a one-dimensional array composed of a predetermined number of elements is prepared, and the range of the maximum value of the calculated parameter is equally divided into the predetermined number By assigning each parameter to a predetermined number of elements based on the value of the parameter and adding the weight value calculated based on the area and normal vector of the three polygons to the element, the frequency Since the distribution is calculated, there is a difference in the frequency distribution calculated when the three polygons belong to the polygon model corresponding to the same part and when the three polygons belong to the polygon model corresponding to a plurality of parts. It is possible to improve the discriminability of individual parts in the frequency distribution.

  Further, in the tenth aspect of the present invention, the frequency distribution calculating means includes a unit vector from the average point of the triangle formed based on the three polygons to each vertex and a normal vector of the polygon including the vertex. Weighting is performed based on the area of the three polygons and the normal vector, with an average value for the three polygons obtained by multiplying the area of the polygon by the inner product value as a weight value.

  According to the tenth aspect of the present invention, in calculating the frequency distribution, unit vectors and vertices from the average point, which is a point having the average coordinates of the three vertices of the triangle formed based on the three polygons, to each vertex Weighting is performed based on the area of the three polygons and the normal vector, with the average value for the three polygons obtained by multiplying the area of the polygon by the inner product value of the normal vector of the polygon including Therefore, the difference in the resolution of polygon division does not greatly affect the frequency distribution. Instead, whether the three polygons belong to the same part in the regulatory model composed of a plurality of parts depends on the frequency distribution. When multiple polygon models that have a large effect and have different polygon divisions are judged to be the same (the output should be restricted), and the target model corresponds to a part , It becomes possible to improve the identification of individual components in the frequency distribution.

  In the eleventh aspect of the present invention, the frequency distribution calculating means may calculate the value of each element (Hd () by the sum of weight values (weight sum value SQsum) for each polygon constituting each target model. The frequency distribution is calculated by dividing md) and Ha (ma)) and giving the absolute value when the element is a negative value.

  According to the eleventh aspect of the present invention, in calculating the frequency distribution, the value of each element is divided by the weight value for each polygon constituting each target model, and when the element is a negative value, Since it is changed to a positive value, the influence of the surface area and volume of the target model is eliminated, and when the target model corresponds to a part, it is possible to obtain a frequency distribution with improved identification of individual parts It becomes.

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

  According to the twelfth aspect of the present invention, the first parameter and the second parameter which are two kinds of parameters are used as the parameters characterizing the triangle, and the frequency distribution includes the first frequency distribution corresponding to the first parameter, Since there are two types of frequency distributions of the second frequency distribution corresponding to the two parameters, the similarity between the two polygon models is determined from a plurality of viewpoints, and the determination as to whether or not the output should be regulated is more accurate. Can be done.

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

  According to the thirteenth aspect of the present invention, the radius of the circumscribed circle of the triangle is used as the first parameter, the radius of the inscribed circle of the triangle is used as the second parameter, and the frequency distribution of the radius of the circumscribed circle is used as the first frequency distribution. Since a circumscribed circle distribution is used and the inscribed circle distribution, which is the frequency distribution of the radius of the inscribed circle, is used as the second frequency distribution, the output is restricted when a solid object is output based on the polygon model. The determination of whether or not to be performed can be performed with high accuracy using a circumscribed circle distribution that sharply reflects the shape of the polygon model, and the similarity between the two polygon models is determined from a plurality of viewpoints. Therefore, it is possible to more accurately determine whether the output should be regulated.

  In the fourteenth aspect of the present invention, the frequency distribution calculating means prepares a one-dimensional array composed of a predetermined number of elements when calculating the inscribed circle distribution, and calculates the calculated circumscribed circle. A range normalized with a value of 35% to 50% of the maximum value of the radius is equally divided into the predetermined number, and the radius of each inscribed circle is determined based on the radius value of the inscribed circle. The inscribed circle distribution is calculated by assigning to a predetermined number of elements and adding a weight value calculated based on the area and normal vector of the three polygons to the element. Features.

  According to the fourteenth aspect of the present invention, in calculating the inscribed circle distribution, a one-dimensional array composed of a predetermined number of elements is prepared, and 35% to 50% of the calculated maximum value of the radius of the circumscribed circle The range normalized by the value of% is equally divided into a predetermined number, and the radius of each inscribed circle is assigned to one of the elements of the predetermined number based on the value of the radius of the inscribed circle. Since the inscribed circle distribution is calculated by adding the weight values calculated based on the area of the three polygons and the normal vector, the scale ratio between the circumscribed circle radius and the inscribed circle radius is A maintained distribution is achieved, the distribution spread becomes narrower and sharper, and the shape distinguishability becomes higher.

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

  According to the fifteenth aspect of the present invention, when collating the frequency distribution of the matched target model with the frequency distribution of the regulatory model, the peak position that gives the maximum value is calculated, and based on the difference between the calculated peak positions. Therefore, even if the frequency distribution of the restriction model represents the characteristics of an object consisting of many parts, the part represented by the frequency distribution of the matched target model It is possible to specify a feature corresponding to the above from the frequency distribution of the regulation model and perform accurate matching.

  In the sixteenth aspect of the present invention, a parameter characterizing a triangle formed by a predetermined point on three polygons selected from the restriction model is calculated for the restriction model, and the three polygons are calculated. Registration means for receiving the frequency distribution calculated by the regulatory model frequency distribution calculating device that calculates the frequency distribution of the parameter while weighting based on the area and the normal vector, and registering the received frequency distribution in the database. It is characterized by having.

  According to the sixteenth aspect of the present invention, a parameter that characterizes a triangle formed by a predetermined point on three polygons selected from within the restriction model is calculated for the restriction model, and the area and method of the three polygons are calculated. A separate regulation model frequency distribution calculation device that calculates the frequency distribution of parameters while weighting based on the line vector is prepared, and the frequency distribution of the regulation model is received from the regulation model frequency distribution calculation device and registered in the database. As a result, the database can be updated quickly from a remote location.

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

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

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

  According to the eighteenth aspect of the present invention, the frequency distribution of the target model is received via the network, and the determination result obtained by determining whether the output should be regulated is transmitted to the transmission source of the frequency distribution of the target model. Since it did in this way, the judgment whether an output should be controlled can be provided by a cloud type, and the process in 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 nineteenth aspect of the present invention,
The three-dimensional object shaping data output restriction device;
A three-dimensional object modeling apparatus that models a three-dimensional object using a polygon model whose output is permitted by the three-dimensional object modeling data output restriction device;
There is provided a three-dimensional object forming system.

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

In the twentieth aspect of the present invention,
A device that creates a frequency distribution based on the polygon model in order to determine whether or not to restrict when a polygon model expressed as a set of polygons is output to the three-dimensional object modeling device as three-dimensional object modeling data. There,
For a target model that is a polygon model to be output, parameters that characterize a triangle formed by predetermined points on three polygons selected from the target model are calculated, and the areas and normals of the three polygons are calculated. Provided is a frequency distribution calculation device that calculates a frequency distribution of the parameter while weighting based on a vector.

  According to the twentieth aspect of the present invention, a parameter that characterizes a triangle formed by predetermined points on three polygons selected from the target model is calculated for a target model that is a polygon model to be output. Since the frequency distribution of the parameter is calculated while weighting based on the area and normal vector of the three polygons, the three polygons belong to the polygon model corresponding to the same part and the three polygons There is a difference in the frequency distribution calculated between the case belonging to the polygon model corresponding to a plurality of parts, the individual parts in the frequency distribution are improved in identification, and the output 3D polygon model is registered in the database. Appropriate output propriety even when output in a form composed of multiple parts that make up the regulatory model It is possible to constant.

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

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

  According to the present invention, when outputting a three-dimensional object based on a polygon model, even when outputting in a state of being divided into component parts, it is possible to quickly determine whether or not to restrict output with a small processing load. Can be performed.

It is a figure which shows the relationship between the polygon model of 3D shape, and the selected triangle. It is a figure which shows the relationship between the 3D-shaped structural component and the selected triangle in case a mutually congruent triangle is selected. FIG. 3 is a diagram illustrating a positional relationship between a triangle and a polygon and a relationship between a normal vector of the polygon in the same polygon model as FIG. 2. 1 is a hardware configuration diagram of a three-dimensional object formation system including a three-dimensional object formation data output restriction device 100 according to an embodiment of the present invention. It is a functional block diagram which shows the structure of the data output control apparatus for solid object modeling which concerns on one Embodiment of this invention. It is a flowchart which shows the process outline | summary of the data output control apparatus for solid object shaping which concerns on one Embodiment of this invention. It is a figure which shows the mode of collation of a target model and a regulation model. It is a flowchart which shows the detail of the 1st method of the calculation process of the frequency distribution of step S100. It is a flowchart which shows the detail of the 2nd method of calculation processing of the frequency distribution of step S100. It is a figure which shows the circumscribed circle and the inscribed circle in the circumscribed circle distribution and the inscribed circle distribution obtained from the target model. It is a flowchart which shows the detail of the collation process of the frequency distribution of step S200. It is a figure which shows the relationship between frequency distribution (histogram) and a peak position. 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 collation example (no matching process) of the whole model and its components. It is a figure which shows the collation example (with matching process) of the whole model and its components.

<1. Basic concept of the present invention>
First, the basic concept of the present invention will be described. FIG. 1 is a diagram showing a relationship between a 3D-shaped polygon model and a selected triangle. In FIG. 1, for convenience of explanation, a 3D shape is shown in a two-dimensionally projected state. The upper pentagon in FIGS. 1A and 1B is a component A composed of 10 polygons (only 5 polygons are visible on the drawing, but 5 polygons are hidden on the back side). The lower hexagon in FIGS. 1A and 1B is composed of 12 polygons (only 6 polygons are visible on the drawing, but 6 polygons are hidden on the back side). The component B is shown. FIG. 1 (a) shows a case where a composite part composed of a dodecahedron part and a dodecahedron part is handled as a single unit, and FIG. 1 (b) shows each of the dodecahedron part and the dodecahedron part. The case of handling as a single unit is shown.

  FIG. 1A shows three triangles composed of points on three polygons, and FIG. 1B shows two triangles composed of points on three polygons. In FIGS. 1A and 1B, the circumscribed circle and the inscribed circle of each of these five triangles are indicated by broken lines. As will be described later, the radius of these circumscribed circles and inscribed circles is also referred to as feature data (feature vector, etc.) representing the characteristics of the three-dimensional polygon model. Used to calculate frequency distribution (histogram).

  When trying to select a triangle composed of points on three polygons as the basis of the feature data, it goes without saying that a single component as shown in FIG. 1B selects a triangle only within each component. On the other hand, in the composite part as shown in FIG. 1A, a triangle is also selected between the component parts, but a triangle is also selected in each component part. As can be seen by comparing FIG. 1 (a) and FIG. 1 (b), some of the triangles selected for the composite part as shown in FIG. 1 (a) are included in each component as shown in FIG. 1 (b). In many cases, the selected triangle is included alone. However, since triangles are selected at random for both the composite part and the component parts, all the triangles selected in each component part of FIG. 1B are not necessarily selected by the composite part of FIG. It is not included in the triangle. That is, since the feature data of the polygon model of each component part includes the components of the feature data of the polygon model of each component, if the feature data of the polygon model is registered in the database in the form of the composite part, When outputting a 3D shape using a polygon model, it is possible to verify whether or not the feature data of the polygon model for each part partially matches the feature data of the polygon model of the composite part registered in the database. , It is possible to regulate the output of those that match.

  Here, consider a case where congruent triangles are selected by points on three polygons. FIG. 2 is a diagram illustrating a relationship between a 3D-shaped component and a selected triangle when congruent triangles are selected. The polygon model shown in FIG. 2A is slightly different in shape from the polygon model shown in FIG. 1A for convenience of explanation, but the configuration of the polyhedron is the same. The polygon model shown in FIG. Similarly, the polygon model shown in FIG. 1B is slightly different in shape, but the configuration of the polyhedron is the same. Accordingly, FIG. 2 (a) shows a case where a composite part composed of a dodecahedron part and a dodecahedron part is handled as a single unit, and FIG. 2 (b) shows a dodecahedron part and a dodecahedron part. The case where each is handled as a single unit is shown.

  In FIG. 2A, three triangles composed of points on three polygons are shown. In FIG. 2B, two triangles composed of points on three polygons are shown. The lower two triangles in FIG. 2A are congruent triangles. In this case, since the congruent triangles have the same circumscribed circle radius and inscribed circle radius, the lower two triangles in FIG. 2A are different in the feature data based on the circumscribed circle radius and the inscribed circle radius. Will not be attached.

  Therefore, in the present invention, the normal vector of the polygon is taken into consideration when creating the feature data. FIG. 3 is a diagram illustrating a relationship between a positional relationship between a triangle and a polygon and a normal vector of the polygon in the same polygon model as FIG. FIG. 3A shows how a triangle composed of points on three polygons intersects the polygon. In FIG. 3A, a solid line indicates a portion where the triangle is located in front of the polygon, and a broken line indicates a portion where the triangle is located behind the polygon. Comparing the lower two triangles in FIG. 3 (a), it can be seen that although they are congruent with each other, the positional relationship with the polygon is different.

  Of the three triangles shown in FIG. 3A, the upper and lower triangles are triangles created based on the polygons in the 10-sided component and the 12-sided component, respectively. Located behind the polygon. However, among the three triangles shown in FIG. 3 (a), the middle triangle is a triangle created based on one polygon in the decahedron and two polygons in the dodecahedron, so the top vertex of the triangle is It will be located in front of the lower polygon in the decahedron.

  In FIG. 3B, the polygon normal vector that forms the basis of the triangle shown in FIG. 3A and the average point that is the point having the average coordinates of the three vertices of the triangle are transferred to each polygon. Shows vector. In FIG. 3B, Na1, Na2, Na3, Nb1, Nb2, Nb3, Nb4, and Nb5 are polygon normal vectors, and Ta1, Ta2, Ta3, Tab1, Tab2, Tab3, Tb1, Tb2, and Tb3 are , A vector from an average point which is a point having the average coordinates of the three vertices of the triangle to an average point which is a point having the average coordinates of the three vertices of each polygon. By creating feature data using these vectors, even if there are triangles that are congruent or similar to each other, the difference between whether they are within a component or across components Therefore, it is possible to accurately determine similarity between polygon models.

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

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

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

  FIG. 5 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. 5, 10 is a frequency distribution calculating means, 20 is a frequency distribution matching means, 30 is an emphasis component creating means, 40 is a frequency distribution emphasizing means, 50 is a frequency distribution matching means, 60 is a target model storage means, and 70 is a regulatory model. It is a database. The frequency distribution calculating means 10 calculates a parameter characterizing a triangle formed by predetermined points on three polygons selected from within the target model for the target model that is a polygon model to be output, and the parameter Calculate the frequency distribution. In the present embodiment, two types of parameters, a first parameter and a second parameter, are used as parameters. More specifically, the radius of a circumscribed circle of a triangle formed by predetermined points on three polygons selected from the target model is used as the first parameter, and the radius is selected from the target model as the second parameter. The radius of the inscribed circle of the triangle formed by the predetermined points on the three polygons is used. Therefore, the frequency distribution calculating means 10 has two types, a circumscribed circle distribution that is a frequency distribution of the radius of the circumscribed circle as the first frequency distribution and an inscribed circle distribution that is the frequency distribution of the radius of the inscribed circle as the second frequency distribution. The frequency distribution is calculated.

  The frequency distribution matching means 20 performs processing for matching the scale of the parameter radius on the horizontal axis of the frequency distribution of the target model with the scale of the parameter radius on the horizontal axis of the frequency distribution of the regulatory model registered in the database. Since the maximum value of the horizontal axis of the frequency distribution is normalized by the maximum value of the circumscribed circle radius calculated for each model, the scale of the horizontal axis of the frequency distribution does not usually match between the target model and the regulation model. Therefore, when the frequency distribution matching unit 20 outputs each of the target model and the restriction model by the 3D printer, the actual dimension corresponding to the maximum value on the horizontal axis of the frequency distribution of the target model is the actual dimension α, and the frequency of the restriction model. When the actual dimension corresponding to the maximum value on the horizontal axis of the distribution is the actual dimension β, the frequency distribution is reduced in the horizontal direction so that the horizontal axis of the frequency distribution of the target model is reduced by the actual dimension α / actual dimension β. I do. The frequency distribution is composed of a circumscribed circle distribution and an inscribed circle distribution. Since the scales of the horizontal axes of both are the same, the horizontal axes of both frequency distributions can be reduced by the actual dimension α / actual dimension β. It ’s fine. The emphasis component creating means 30 corrects the frequency values of the matched target model so as to fall within a predetermined range, and creates an emphasis component that is raised to a predetermined real value less than 1. The frequency distribution emphasizing means 40 multiplies the frequency distribution of the matched target model and the frequency distribution of the regulation model by the emphasis component, respectively, and creates the enhancement frequency distribution of the target model and the enhancement frequency distribution of the regulation model. As the enhancement frequency distribution, two types of enhancement frequency distributions are calculated, as described above, the circumscribed circle distribution that is the frequency distribution of the radius of the circumscribed circle and the inscribed circle distribution that is the frequency distribution of the radius of the inscribed circle. It will be. The frequency distribution matching unit 50 matches the enhancement frequency distribution calculated for the target model with the enhancement frequency distribution calculated for the restriction model, and whether or not the output should be regulated, that is, whether the output is inappropriate or appropriate. Determine.

  The frequency distribution calculating means 10, the frequency distribution matching means 20, the emphasis component creating means 30, the frequency distribution emphasizing means 40, and the frequency distribution matching means 50 are realized by the CPU 1 executing a program stored in the storage device 3. The The target model storage unit 60 is a storage unit that stores a target model that 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 restriction model database 70 stores a circumscribed circle distribution and an inscribed circle distribution calculated as two types of frequency distributions for a restriction model that is a polygon model whose output is to be restricted, and is converted into a database. This is realized by the device 3. The circumscribed circle distribution and the inscribed circle distribution, which are two kinds of frequency distributions, serve as feature data expressing the features of the regulation model. That is, the difference between the original restriction models can be identified by the two types of frequency distributions, but the original restriction model cannot be restored. This is similar to the fact that individual differences can be identified by fingerprints, but the figure of the person itself cannot be restored. Therefore, the two frequency distributions do not play a role as a work, but also play a role as a so-called fingerprint that proves the identity of the two works. In the regulation model database 70, it is possible to register not only the circumscribed circle distribution and the inscribed circle distribution but also the regulation model itself that is a basis for calculating the circumscribed circle distribution and the inscribed circle distribution. The regulatory model itself is not registered due to copyright issues.

  Each component means shown in FIG. 5 is actually realized by installing a dedicated program in hardware such as a computer and its peripheral devices as shown in FIG. That is, the computer executes the contents of each means according to a dedicated program. In this specification, the computer means an apparatus having an arithmetic processing unit such as a CPU and capable of data processing, and is incorporated not only in a general-purpose computer such as a personal computer but also in a “3D printer” as a product. Board computer included.

  In the storage device 3 shown in FIG. 4, a dedicated program for operating the CPU 1 and causing the computer to function as a three-dimensional object formation data output restriction device is mounted. By executing this dedicated program, the CPU 1 realizes functions as the frequency distribution calculating means 10, the frequency distribution matching means 20, the emphasis component creating means 30, the frequency distribution emphasizing means 40, and the frequency distribution matching means 50. Become. The storage device 3 not only functions as the target model storage unit 60 and the restriction model database 70 but also stores various data necessary for processing as the three-dimensional object formation data output restriction device.

  In the hardware configuration shown in FIG. 4, the frequency distribution calculating device for calculating the frequency distribution is implemented by mounting only a dedicated program for causing the computer to function as the frequency distribution calculating means 10 in the storage device 3. Can also be realized. The frequency distribution calculation device calculates and outputs two types of frequency distributions based on the target model read from the target model storage unit 60. The output frequency distribution is separately input to an apparatus including a frequency distribution matching unit 20, an emphasis component creating unit 30, a frequency distribution emphasizing unit 40, a frequency distribution matching unit 50, and a regulation model database 70, and is used for collation and output regulation. Judgment can be made.

<3. Processing action>
<3.1. Pretreatment>
Next, the processing operation of the three-dimensional object formation data output restriction device shown in FIGS. 4 and 5 will be described. First, a circumscribed circle distribution and an inscribed circle distribution, which are two types of frequency distributions, are created for a regulated model that is a polygon model to be regulated. The creation of the circumscribed circle distribution and the inscribed circle distribution from the polygon model can be performed in the same manner as the processing in the three-dimensional object formation data output restriction device described later. Creation of the circumscribed circle distribution and the inscribed circle distribution from the restriction model may be performed by the three-dimensional object formation data output restriction device, or may be performed by executing a similar program on another computer. The created circumscribed circle distribution and inscribed circle distribution are registered in the regulation model database 70.

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

<3.2. Process Overview>
Next, the processing operation of the three-dimensional object formation data output restriction device shown in FIGS. 4 and 5 will be described. FIG. 6 is a flowchart showing a processing outline of the three-dimensional object formation data output restriction device according to the embodiment of the present invention. First, the frequency distribution calculating means 10 calculates a circumscribed circle distribution and an inscribed circle distribution that are two types of frequency distributions for the target model that is a polygon model (step S100). Next, the calculated two types of frequency distributions are matched using the frequency distribution of the restriction model registered in the restriction model database 70 (step S130). Subsequently, an emphasis component creation process is performed using the two matched frequency distributions (step S150). Further, using the created enhancement component, enhancement processing of the two frequency distributions created in step S130 is performed (step S170). Then, the enhancement frequency distribution of the target model subjected to the enhancement process is collated with the enhancement frequency distribution of the restriction model subjected to the enhancement process (step S200).

  The manner in which the two polygon models are collated by the processing shown in FIG. 6 will be described with reference to FIG. FIG. 7 is a diagram illustrating a state of matching between the target model and the restriction model. In the example of FIG. 7, the whole is registered as a restriction model (1) in the database, and a part of the restriction model (1) is used as the target model (2). In this case, two kinds of frequency distributions (3) are obtained from the restriction model (1) and two kinds of frequency distributions (4) are obtained from the target model (2) by the process of step S100. At this time, the horizontal axis of the frequency distribution indicates the maximum circumscribed circle radius (described later as maximum radius a and maximum radius b in FIG. 7) and does not reflect the scale at the time of 3D printer output. And the scale of the maximum circumscribed circle radius is greatly different. Therefore, the process of step S130 performs the process of matching the frequency distribution of the target model with the frequency distribution of the restriction model using the actual maximum circumscribed circle radius (5). Specifically, when each of the target model and the regulated model is output by the 3D printer, the actual dimension α of the maximum circumscribed circle radius of the target model is the actual dimension α, and the actual dimension of the maximum circumscribed circle radius of the regulated model is the actual dimension β. , The frequency distribution is reduced in the horizontal direction so that the horizontal axis of the frequency distribution of the target model is the actual dimension α / the actual dimension β (maximum radius b / maximum radius a). The frequency distribution is composed of a circumscribed circle distribution and an inscribed circle distribution, but the scales of the horizontal axes are the same, so that both frequency distributions have the actual dimension α / actual dimension β. What is necessary is just to reduce. After that, an emphasis component is created by the process of step S150 (6), the emphasis process is performed (7) (8), and a collation process is performed (9).

<3.3. Frequency distribution calculation processing>
The frequency distribution calculation process will be specifically described below.
<3.3.1. First Method>
First, the frequency distribution calculation process in step S100 will be described. There are two types of frequency distribution calculation processing: the first method and the second method. First, the first method will be described. FIG. 8 is a flowchart showing details of the frequency distribution calculation processing by the first method. Here, the number of polygons of the target model is N, and the variable i (i = 0,..., N−1) for specifying the polygon is used, and the vertices of each polygon i are (Xu (i), Yu (i), Zu ( i)), the normal vector of each polygon i is defined as (Xn (i), Yn (i), Zn (i)). u indicates a vertex number. In this embodiment, since the polygon is triangular, three values of u = 0, 1, and 2 are taken. Therefore, for example, Xu (i) is also expressed as X0 (i), X1 (i), and X2 (i). The polygons and normal vectors specifying the vertices are necessary for output by a 3D printer, which is a three-dimensional object forming apparatus, and the direction of the normal vectors indicates the outside of the forming surface (from the material to the air). The normal vector is normally recorded for each polygon in a polygon model for output to a 3D printer. The target model also has its output scale information Sout. The output scale information Sout is an actual dimension when the target model is output as a three-dimensional object, and is an actual dimension per unit length (length 1) of the three-dimensional coordinate system defining the vertex, expressed in mm. Has been. First, the frequency distribution calculating means 10 calculates the area Sp (i) of each polygon i constituting the target model by executing processing according to the following [Equation 1] (step S501).

[Formula 1]
Vx = [Y1 (i) -Y0 (i)] [Z2 (i) -Z0 (i)]-[Z1 (i) -Z0 (i)] [Y2 (i) -Y0 (i)]
Vy = [Z1 (i) -Z0 (i)] [X2 (i) -X0 (i)]-[X1 (i) -X0 (i)] [Z2 (i) -Z0 (i)]
Vz = [X1 (i) -X0 (i)] [Y2 (i) -Y0 (i)]-[Y1 (i) -Y0 (i)] [X2 (i) -X0 (i)]
Sp (i) = [Vx (i) ² + Vy (i) ² + Vz (i) ² ] ^1/2

  Next, an average point that is a point having the average coordinates of the vertices is calculated for each polygon constituting the target model (step S502). Specifically, a polygon average point (Xc (i), Yc (i), Zc (i)) that is an average point of each polygon is calculated by executing processing according to the following [Equation 2]. .

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

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

  Here, an average point of the triangle and a unit vector to each vertex of the triangle will be described. An average point of a triangle formed by polygon average points of three polygons i1, i2, i3 (i1, i2, i3 are different integers where i takes a value from 0 to N-1) (Xo (i1, i2 , I3), Yo (i1, i2, i3), Zo (i1, i2, i3)). That is, the average points of the triangles (Xo (i1, i2, i3), Yo (i1, i2, i3), Zo (i1, i2, i3)) are defined by the following [Equation 3].

[Formula 3]
Xo (i1, i2, i3) = {Xc (i1) + Xc (i2) + Xc (i3)} / 3
Yo (i1, i2, i3) = {Yc (i1) + Yc (i2) + Yc (i3)} / 3
Zo (i1, i2, i3) = {Zc (i1) + Zc (i2) + Zc (i3)} / 3

  The unit vector (Ex (i2, i3, i1) from the average point of the triangle to the polygon average point (Xc (i1), Yc (i1), Zc (i1)) of the polygon i1 is obtained by the following [Equation 4]. , Ey (i2, i3, i1), Ez (i2, i3, i1)).

[Formula 4]
E (i2, i3, i1) = [{Xc (i1) −Xo (i1, i2, i3)} ² + {Yc (i1) −Yo (i1, i2, i3)} ² + {Zc (i1) − Zo (i1, i2, i3)} ² ] ^1/2
Ex (i2, i3, i1) = {Xc (i1) -Xo (i1, i2, i3)} / E (i2, i3, i1)
Ey (i2, i3, i1) = {Yc (i1) −Yo (i1, i2, i3)} / E (i2, i3, i1)
Ez (i2, i3, i1) = {Zc (i1) −Zo (i1, i2, i3)} / E (i2, i3, i1)

  The unit vector to the average point of the triangle and each vertex of the triangle is used for calculation of the frequency distribution in step S508, which will be described later, by executing the processing according to [Formula 3] and [Formula 4].

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

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

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

  Next, initial setting is performed (step S504). Specifically, the circumscribed circle distribution to be calculated, the inscribed circle distribution are initialized, the loop counter is initialized, and the weight sum value is initialized. The circumscribed circle distribution calculated in the present embodiment is a distribution of the radius of a triangular circumscribed circle obtained by weighting the area of the polygon. The number of elements is MD, and each element md (md = 0,..., MD− The frequency of 1) is expressed as Hd (md). Hd (md) is a one-dimensional array composed of a predetermined number of MD elements. An arbitrary value can be set as the number of elements MD, but in this embodiment, MD = 360. After preparing such a one-dimensional array, the frequency distribution calculating means 10 sets the initial value as Hd (md) = 0. The inscribed circle distribution calculated in the present embodiment is a distribution of the radius of a triangular inscribed circle obtained by weighting the area of a polygon. The number of elements is MA, and each element ma (ma = 0,..., The frequency of MA-1) is expressed as Ha (ma). Ha (ma) is a one-dimensional array composed of a predetermined number of elements. The element number MA may be the same as or different from the element number MD. Although an arbitrary value can be set as the element number MA, in the present embodiment, it is the same as the element number MD, and MA = 360. After preparing such a one-dimensional array, the frequency distribution calculating means 10 sets the initial value as Ha (ma) = 0. For the loop counter LC, an initial value LC = 0 is set. Further, the weight sum value SQsum = 0, which is the sum value of the weight values SQ, is set.

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

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

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

  Next, the frequency distribution calculating means 10 calculates the radius of a circumscribed circle of a triangle having the polygon average point as a vertex (step S506). Specifically, first, by executing the processing according to the following [Formula 5], the area S (k) of the first triangle is set in the range of k = 0,..., N / 3-1. calculate.

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

  In the above [Formula 5], “·” indicates multiplication. The same applies to the following [Formula]. Then, by executing the processing according to the following [Equation 6], the radius D (k) of the circumscribed circle of the first triangle is calculated in the range of k = 0,..., N / 3-1. .

[Formula 6]
D (k) = D12 · D23 · D31 / (4 · S (k))

  Similarly, the frequency distribution calculating means 10 executes a process according to the following [Equation 7], whereby the area S (2) of the second triangle in the range of k = 0,..., N / 3-1. k) is calculated.

[Formula 7]
D12 = [[Xc (G3 (R2 (k))) − Xc (G2 (R1 (k)))] ² + [Yc (G3 (R2 (k))) − Yc (G2 (R1 (k))) ] ² ] ^1/2
D23 = [[Xe (G1 (R3 (k)))-Xc (G3 (R2 (k)))] ² + [Yc (G1 (R3 (k)))-Yc (G3 (R2 (k))) ] ² ] ^1/2
D31 = [[Xc (G2 (R1 (k))) − Xc (G1 (R3 (k)))] ² + [Yc (G2 (R1 (k))) − Yc (G1 (R3 (k))) ] ² ] ^1/2
D = (D12 + D23 + D31) / 2
S (k) = [D · (D−D12) · (D−D23) · (D−D31)] ^1/2

  Then, the radius D (k) of the circumscribed circle of the second triangle is calculated in the range of k = 0,..., N / 3-1 by executing the processing according to the above [Equation 6]. Similarly, the frequency distribution calculating means 10 executes the process according to the following [Equation 8], whereby the area S (3) of the third triangle in the range of k = 0,..., N / 3-1. k) is calculated.

[Formula 8]
D12 = [[Xc (G1 (R2 (k)))-Xc (G3 (R1 (k)))] ² + [Yc (G1 (R2 (k)))-Yc (G3 (R1 (k))) ] ² ] ^1/2
D23 = [[Xc (G2 (R3 (k))) − Xc (G1 (R2 (k)))] ² + [Yc (G2 (R3 (k))) − Yc (G1 (R2 (k))) ] ² ] ^1/2
D31 = [[Xc (G3 (R1 (k))) − Xc (G2 (R3 (k)))] ² + [Yc (G3 (R1 (k))) − Yc (G2 (R3 (k))) ] ² ] ^1/2
D = (D12 + D23 + D31) / 2
S (k) = [D · (D−D12) · (D−D23) · (D−D31)] ^1/2

  Then, by executing the processing according to the above [Equation 6], the radius D (k) of the circumscribed circle of the third triangle is calculated in the range of k = 0,..., N / 3-1. In the above [Equation 5], [Equation 7] and [Equation 8], only the triangle in which the lengths D12, D23 and D31 of each side of the triangle satisfy all the six conditions shown in the following [Equation 9] The target of the frequency distribution of the radius. Therefore, a triangle that does not satisfy even one of the six conditions shown in [Formula 9] is not subject to the frequency distribution of the circumscribed circle radius. This is because when the six conditions shown in [Equation 9] below are not satisfied, the radius D (k) of the circumscribed circle becomes extremely large, and a highly accurate frequency distribution cannot be obtained.

[Formula 9]
D12> 0
D23> 0
D31> 0
| D12-D23-D31 | / D12> 0.1
| D23-D31-D12 | / D23> 0.1
| D31-D12-D23 | / D31> 0.1

  As shown in the above [Equation 9], the first three conditions out of the six conditions mean that all sides have positive values other than 0, that is, form a triangle. The latter half of the six conditions means that the triangle to be created does not have an extremely flat shape. If the triangle becomes extremely flat, the radius of the circumscribed circle will approach infinity, and a highly accurate frequency distribution will not be obtained. When the six conditions shown in [Equation 9] are not satisfied, a triangle is not formed (when either side is 0), or the radius D (k) of the circumscribed circle becomes extremely large, and the frequency with high accuracy This is because the distribution cannot be obtained.

  The radius of the circumscribed circle of N / 3 first triangles, the radius of the circumscribed circle of N / 3 second triangles, and the circumscribed circle of N / 3 third triangles calculated by the above [Equation 6] The N / 3 array D (k) of the second triangle is offset by N / 3 for the N / 3 array D of the second triangle, and the N / 3 array of the third triangle is added to the N / 3 array of the third triangle. Is added with an offset of 2N / 3, and is grouped with a serial number i, the range is reset to i = 0,..., N−1, and the circumscribed circle radius D (i) ( i = 0,..., N-1). And the largest one among the N circumscribed circle radii D (i) of i = 0,..., N−1 is set as the maximum circumscribed circle radius Dmax.

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

[Formula 10]
A (k) = 2 · S (k) / (D12 + D23 + D31)

  In the above [Expression 10], D12, D23, D31 and S (k) are the first triangle, the second triangle, the third triangle calculated by the processing according to the above [Expression 5], [Expression 7], and [Expression 8]. The length and area of the three sides of the triangle. Note that a triangle that does not satisfy even one of the six conditions shown in [Equation 9] is not subject to the frequency distribution of the inscribed circle radius.

  The radius A (k) of the inscribed circle of N / 3 first triangles calculated by the above [Equation 10], the radius A (k) of the inscribed circle of N / 3 second triangles, and N / 3 of N / 3 arrays A (k) of inscribed circle radius A (k) of 3rd 3 triangles, N / 3 for N / 3 arrays of 2nd triangle Add an offset only, add an offset of 2N / 3 to the N / 3 array for the third triangle, and reset the range of serial number i to i = 0, ..., N-1 The radius A (i) (i = 0,..., N−1) of N inscribed circles of N triangles is obtained.

  Next, the frequency distribution calculating means 10 calculates a circumscribed circle distribution that is a frequency distribution of the radius of the circumscribed circle of the triangle (step S508). Specifically, the frequency Hd (md) of the element md is calculated for i = 0,..., N−1 by executing processing according to the following [Equation 11].

[Formula 11]
If Dmax · md / MD ≦ D (i) <Dmax · (md + 1) / MD,
Hd (md) ← Hd (md) + SQ, SQsum ← SQsum + SQ
In case of 0 ≦ i ≦ N / 3-1 SQ = [Sp (G1 (R1 (i))) · {Ex (G2 (R2 (i)), G3 (R3 (i)), G1 (R1 (i)] )). Xn (G1 (R1 (i))) + Ey (G2 (R2 (i)), G3 (R3 (i)), G1 (R1 (i))). Yn (G1 (R1 (i))) + Ez (G2 (R2 (i)), G3 (R3 (i)), G1 (R1 (i))). Zn (G1 (R1 (i)))} + Sp (G2 (R2 (i))). { Ex (G3 (R3 (i)), G1 (R1 (i)), G2 (R2 (i))). Xn (G2 (R2 (i))) + Ey (G3 (R3 (i)), G1 (R1) (I)), G2 (R2 (i))) Yn (G2 (R2 (i))) + Ez (G3 (R3 (i)), G1 (R1 (i)), G2 (R2 (i))) Zn (G2 (R2 (i)) )} + Sp (G3 (R3 (i))). {Ex (G1 (R1 (i)), G2 (R2 (i)), G3 (R3 (i))). Xn (G3 (R3 (i)) ) + Ey (G1 (R1 (i)), G2 (R2 (i)), G3 (R3 (i))). Yn (G3 (R3 (i))) + Ez (G1 (R1 (i)), G2 ( R2 (i)), G3 (R3 (i))). Zn (G3 (R3 (i)))}] / 3
When N / 3 ≦ i ≦ 2N / 3-1 SQ = [Sp (G2 (R2 (i))) · {Ex (G3 (R3 (i)), G1 (R1 (i)), G2 (R2 ( i))). Xn (G2 (R2 (i))) + Ey (G3 (R3 (i)), G1 (R1 (i)), G2 (R2 (i))). Yn (G2 (R2 (i) )) + Ez (G3 (R3 (i)), G1 (R1 (i)), G2 (R2 (i))). Zn (G2 (R2 (i)))} + Sp (G3 (R3 (i))) · {Ex (G1 (R1 (i)), G2 (R2 (i)), G3 (R3 (i))) · Xn (G3 (R3 (i))) + Ey (G1 (R1 (i))), G2 (R2 (i)), G3 (R3 (i))) Yn (G3 (R3 (i))) + Ez (G1 (R1 (i)), G2 (R2 (i)), G3 (R3 (i)) )) Zn (G3 (R3 ( i)))} + Sp (G1 (R1 (i))). {Ex (G2 (R2 (i)), G3 (R3 (i)), G1 (R1 (i))). Xn (G1 (R1 ( i))) + Ey (G2 (R2 (i)), G3 (R3 (i)), G1 (R1 (i))). Yn (G1 (R1 (i))) + Ez (G2 (R2 (i)) , G3 (R3 (i)), G1 (R1 (i))). Zn (G1 (R1 (i)))}] / 3
In the case of 2N / 3 ≦ i ≦ N−1 SQ = [Sp (G3 (R3 (i))) · {Ex (G1 (R1 (i)), G2 (R2 (i)), G3 (R3 (i) )). Xn (G3 (R3 (i))) + Ey (G1 (R1 (i)), G2 (R2 (i)), G3 (R3 (i))). Yn (G3 (R3 (i))) + Ez (G1 (R1 (i)), G2 (R2 (i)), G3 (R3 (i))). Zn (G3 (R3 (i)))} + Sp (G1 (R1 (i))). { Ex (G2 (R2 (i)), G3 (R3 (i)), G1 (R1 (i))). Xn (G1 (R1 (i))) + Ey (G2 (R2 (i)), G3 (R3 (I)), G1 (R1 (i))) Yn (G1 (R1 (i))) + Ez (G2 (R2 (i)), G3 (R3 (i)), G1 (R1 (i))) Zn (G1 (R1 (i) ))} + Sp (G2 (R2 (i))). {Ex (G3 (R3 (i)), G1 (R1 (i)), G2 (R2 (i))). Xn (G2 (R2 (i)) )) + Ey (G3 (R3 (i)), G1 (R1 (i)), G2 (R2 (i))). Yn (G2 (R2 (i))) + Ez (G3 (R3 (i)), G1 (R1 (i)), G2 (R2 (i))). Zn (G2 (R2 (i)))}] / 3

  In the above [Expression 11], SQ is the inner product value of the vectors (Ex (), Ey (), Ez ()) from the average point of the triangle to each vertex and the normal vector Xn () of each polygon. It is a weight value multiplied by the area Sp (). The vectors (Ex (), Ey (), Ez ()) from the average point of the triangle to each vertex are calculated from the average point of the triangle calculated using the above [Equation 3] and [Equation 4] to each vertex. Calculated using unit vectors. [Equation 11] indicates that the circumscribed circle radius D (i) of the triangle is equal to or greater than the value obtained by multiplying the maximum circumscribed circle radius Dmax by md / MD and smaller than the value obtained by multiplying (md + 1) / MD. This means that a weight value SQ based on three polygons having vertices as polygon average points is added to Hd (md). Similarly, the weight value SQ is added to the weight sum value SQsum. The weight value SQ becomes a small value when the direction from the average point of the triangle to each vertex and the normal vector direction of each polygon are different, and may take a negative value when the direction is further reversed. If it does so, the value added to frequency Hd (md) will become small or will be subtracted. Such a phenomenon is likely to occur when the three polygons constituting the triangle belong to the polygon models of different parts, and as a result, the three polygons constituting the triangle belong to the polygon model of the same part with the frequency Hd ( It tends to be counted in md). That is, the features that cross a plurality of parts are suppressed in the frequency distribution, and the characteristics of the individual parts are likely to remain.

  The circumscribed circle distribution Hd (md) (md = 0,..., MD-1) is calculated by executing the processing according to the above [Equation 11] for N triangles. Each element is not just a frequency, but a weight value obtained by multiplying the area of the polygon by the inner product of the vector from the average point of the triangle to each vertex and the normal vector of each polygon. Hd (md) represents a circumscribed circle distribution weighted by a weight value. When only 1 is added or when the area of the polygon is simply added, there is a significant difference in the circumscribed circle distribution between the case where the polygon configuration is coarse and the case where the polygon is coarse for the same polygon model. Therefore, in this embodiment, the weight value SQ is added when the circumscribed circle distribution is calculated.

  Next, the frequency distribution calculation means 10 calculates an inscribed circle distribution that is a frequency distribution of the radius of the inscribed circle of each triangle (step S509). Specifically, the frequency Ha (ma) of the element ma is calculated for i = 0,..., N−1 by executing processing according to the following [Equation 12].

[Formula 12]
If Dmax · ma / 2MA ≦ A (i) <Dmax · (ma + 1) / 2MA,
Ha (ma) ← Ha (ma) + SQ

  In the above [Equation 12], the weight value SQ obtained by multiplying the inner product value of the vector from the average point of the triangle to each vertex and the normal vector of each polygon by the area of the polygon is also processed according to [Equation 11]. Use the one calculated by. [Expression 12] is obtained when the radius A (i) of the inscribed circle of the triangle is equal to or larger than the value obtained by multiplying the maximum circumscribed circle radius Dmax by ma / 2MA and smaller than the value obtained by multiplying (ma + 1) / 2MA. This means that the weight value SQ using three polygons having the triangle vertices as average points is added to Ha (ma). This is because the vertex of the triangle is taken as the average point when the value is equal to or greater than the value obtained by multiplying Dmax / 2, which is half the maximum circumscribed circle radius Dmax, by ma / MA and less than (ma + 1) / MA. This is the same as adding the weight value SQ using the three polygons to Ha (ma).

  Eventually, by executing the processing according to the above [Equation 12], the frequency distribution calculating means 10 prepares a one-dimensional array composed of elements of a predetermined number MA, and calculates the maximum radius of the circumscribed circle calculated. A range normalized by a value of 50% of the value (maximum circumscribed circle radius Dmax) is equally divided into a predetermined number MA, and the radius A (i) of each inscribed circle is determined based on the value. The inscribed circle distribution is calculated by assigning to any element ma of the number MA and counting the number corresponding to the element ma.

Since the circumscribed circle radius and the inscribed circle radius depend on the scale of the polygon model, if the distribution is created by normalizing the horizontal axis to the range of the maximum value, a distribution independent of the scale can be obtained. At this time, if you create an independently normalized distribution with different maximum values for the circumscribed circle radius and the inscribed circle radius, you can create a distribution that does not depend on the scale between the circumscribed circle radius and the inscribed circle radius. On the other hand, the robustness against deformation becomes strong, but the shape discrimination becomes weak. In the present invention, since the distribution in which the inscribed circle radius is normalized using the maximum circumscribed circle radius, which is the same reference value as the inscribed circle radius, is created, a scale between the inscribed circle radius and the inscribed circle radius is obtained. A distribution in which the ratio is maintained is obtained, and the spread of the distribution becomes narrower and sharper and the shape discriminability becomes higher than in the case of independently normalizing with maximum values different between the circumscribed circle radius and the inscribed circle radius. Actually, since the inscribed circle radius does not take a value that is 1/2 (50%) or more of the maximum value of the circumscribed circle radius (maximum circumscribed circle radius), normalization should be performed within the range of 1/2 of the maximum value. To. In the present embodiment, as shown in the above [Equation 12], normalization is performed with Dmax / 2, which is ½ of the maximum circumscribed circle radius Dmax. The normalization range can be set as appropriate, but is preferably 35% to 50% of the maximum value of the circumscribed circle radius, that is, 7/20 to 10/20 (1/2). If it is less than 35%, the ratio of the inscribed circle radius exceeding the maximum value of the element ma (scale) (35% of the maximum value of the circumscribed circle radius) increases and the distribution is concentrated on the maximum value of the element ma. Does not accurately reflect the shape. Further, when it exceeds 50%, a distribution in which the blank on the maximum value side of the element ma is large is formed.

  The inscribed circle distribution Ha (ma) (ma = 0,..., MA-1) is calculated by executing the processing according to the above [Equation 12] for N triangles. Each element is not just a frequency, but a weight value obtained by multiplying the inner product of the vector from the average point of the triangle to each vertex and the normal vector of each polygon by the area of the polygon. The distribution Ha (ma) indicates a weighted weighted inscribed circle distribution. When only 1 is added or when the area of the polygon is simply added, there is a significant difference in the inscribed circle distribution between the case where the polygon configuration is coarse and the case where the polygon is coarse for the same polygon model. Therefore, in the present embodiment, the weight value SQ is added when calculating the inscribed circle distribution.

  If the circumscribed circle distribution and the inscribed circle distribution are calculated, it is next determined whether or not the loop counter LC is equal to or greater than a specified value (step S510). As a result of the determination, if the loop counter LC is less than the specified value, a process of shifting the arrangement of the two groups by 1 is performed (step S511). For example, when the order of the first group is fixed, in the present embodiment, the random number array R2 (k used for the two groups, the second group and the third group, is executed by executing the processing according to the following [Formula 13]. ), The order of the random number array R3 (k) is shifted.

[Formula 13]
Ro = R2 (0), Ro3 = R3 (0),
For k = 0, ..., N / 3-2,
R2 (k) ← R2 (k + 1), R3 (k) ← R3 (k + 1) are repeated,
It is assumed that R2 (N / 3-1) = Ro and R3 (N / 3-1) = Ro3.

  When the order of the second group is fixed or when the order of the third group is fixed, the relationship between the groups is changed and the process according to [Equation 13] is executed. In addition, a process of incrementing the loop counter LC (LC ← LC + 1) is also performed. When there is a frequency distribution that has already been calculated, the frequency value corresponding to each element is added to each element of the frequency distribution. In step S511, after shifting the arrangement of the two groups, the combination of the polygons of the two groups and the other group is changed from the first, and the processing of steps S505 to S509 is executed, and the circumscribed circle distribution and the inner Calculate the tangent circle distribution. The circumscribed circle distribution and the inscribed circle distribution calculation process in steps S505 to S509 is repeated a predetermined number of times set as the specified value. The specified value can be set as appropriate, but is about 10 when the total number of polygons N = 10000.

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

  Specifically, by multiplying Hd (md) calculated by the above [Equation 11] by 100 / {SQsum × LC} and replacing it with Hd (md), the unit is normalized to the rate [%]. Yes. That is, the value Hd (md) of each element is divided by the multiplication value of the weight sum value SQsum, which is the sum value of the weight values SQ, and the loop count LC. Since the weight value SQ is obtained by using the area of the polygon, even if the polygon model has a different total area area, that is, a polygon model with a different scale, it is converted to a circumscribed circle distribution of the same scale, Judgment is possible as a model. In addition, since it is divided by the loop number LC, an average value for the loop number is obtained. Since Hd (md) has a frequency distribution, it needs to be a value of 0 or more. However, since the weight value SQ calculated by [Equation 11] can take a negative value, the obtained Hd (md) may be a negative value. In this case, an absolute value (that is, − 1), and Hd (md) is changed to a value of 0 or more.

  Further, the unit is normalized to the rate [%] by multiplying Ha (ma) calculated by the above [Equation 12] by 100 / {SQsum × LC} and replacing it with Ha (ma). That is, the value Ha (ma) of each element is divided by the multiplication value of the weight sum value SQsum, which is the sum value of the weight values SQ, and the loop count LC. Since the weight value SQ is obtained by using the area of the polygon, even polygon models having different total area values of polygons, that is, different scales, are converted into inscribed circle distributions of the same scale, Judgment is possible as a polygon model. In addition, since it is divided by the loop number LC, an average value for the loop number is obtained. Since Ha (ma) has a frequency distribution, it needs to be a value of 0 or more. However, since the weight value SQ calculated by [Equation 11] can take a negative value, Ha (ma) obtained by [Equation 12] may be a negative value. (Ie, multiply by -1), and change Ha (ma) to a value of 0 or more.

  After normalization of the two kinds of frequency distributions, next, the actual size maximum circumscribed circle radius is calculated (step S513). Specifically, the actual size maximum circumscribed circle radius Wmax is calculated by executing processing according to the following [Equation 14].

[Formula 14]
Wmax = Dmax · Sout

  In the above [Equation 14], Dmax is the maximum circumscribed circle radius, and Sout is output scale information. The actual maximum circumscribed circle radius Wmax is obtained by multiplying the maximum circumscribed circle radius Dmax by the output scale information Sout.

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

<3.3.2. Second method>
Next, the second method will be described. FIG. 9 is a flowchart showing details of the frequency distribution calculation processing by the second method. The same processes as those in the first method are denoted by the same reference numerals and description thereof is omitted. Also in the second method, steps S501 to S509 are performed in the same manner as in the first method. Then, as in step S512 of the first method, normalization of the two types of frequency distribution is performed (step S512). In the second method, normalization is forcibly performed after the calculation of the two types of frequency distributions. Specifically, the unit is normalized to the rate [%] by multiplying Hd (md) calculated by the above [Equation 11] by 100 / SQsum and replacing it with Hd (md). Further, the unit is normalized to the rate [%] by multiplying Ha (ma) calculated by the above [Equation 12] by 100 / SQsum and replacing it with Ha (ma). Unlike the first method, division by the loop counter LC is not performed.

  If normalization of the circumscribed circle distribution and the inscribed circle distribution is performed, it is next determined whether or not the loop counter LC is 1 or more (step S521). This determines whether or not the frequency distribution is calculated for the first time. When the loop counter LC is less than 1, it means that the frequency distribution is calculated for the first time, so that the process of shifting the arrangement of the two groups by 1 is performed (step S511). Specifically, similarly to step S511 of the first method, by executing the process according to [Formula 13], two of the arrays R1 (k), R2 (k), and R3 (k) The order of the array is shifted.

  In step S511, after shifting the arrangement of the two groups, the combination of the polygons of the two groups and the other group is changed from the first time, and the processes of steps S505 to S509, S512, and S521 are executed. Normalize circular distribution and inscribed circle distribution. That is, every time the circumscribed circle distribution and the inscribed circle distribution are calculated once, the process of calculating the circumscribed circle distribution and the inscribed circle distribution again by changing the order of the polygons in two of the three groups is performed. Repeat. In the second and subsequent times, in step S521, since the loop counter LC is determined to be 1 or more, the immediately preceding circumscribed circle distribution and inscribed circle distribution are compared (step S522). First, the processing according to the following [Equation 15] is executed to calculate the Euclidean distance DD between the calculated circumscribed circle distribution and the immediately preceding circumscribed circle distribution. The Euclidean distance is a distance in the Euclidean space given as a value obtained by squaring the absolute value of the difference between the frequencies of each element. The Euclidean distance DD is obtained as a distance between two points in the MD dimensional Euclidean space.

[Formula 15]
DD = Σ _{md = 0, MD-1} [{Hd (md) −Hdp (md)} ² / MD] ^1/2

In the above [Formula 15], the subscript “md = 0, MD-1” of Σ is the following [...] ^1/2 when md takes all integers from 0 to MD-1. The sum is calculated. Hdp (md) represents the circumscribed circle distribution just before.

  Subsequently, the Euclidean distance DA between the calculated inscribed circle distribution and the immediately preceding inscribed circle distribution is calculated by executing processing according to the following [Equation 16]. The Euclidean distance DA is obtained as a distance between two points in the MA dimensional Euclidean space.

[Formula 16]
DA = Σ _{ma = 0, MA-1} [{Ha (ma) −Hap (ma)} ² / MA] ^1/2

In the above [Expression 16], the subscript “ma = 0, MA-1” of Σ is the following [...] ^1/2 when ma takes all integers from 0 to MA-1. The sum is calculated. Hap (ma) indicates the immediately preceding inscribed circle distribution.

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

  When it is determined in step S523 that the state is saturated, that is, when similarity is recognized between the two, the frequency distribution calculating unit 10 officially converts the circumscribed circle distribution and the inscribed circle distribution calculated last into two types. The frequency distribution is determined (step S524). That is, the circumscribed circle distribution and the inscribed circle distribution immediately after the calculation are compared with the circumscribed circle distribution and the inscribed circle distribution obtained immediately before the circumscribed circle immediately after the calculation. The distribution and the inscribed circle distribution are set as a circumscribed circle distribution and an inscribed circle distribution to be verified. At this time, since Hd (md) has a frequency distribution, it needs to be a value of 0 or more. Therefore, when the obtained Hd (md) is a negative value, the absolute value is taken (that is, multiplied by -1), and Hd (md) is changed to a value of 0 or more. Further, since Ha (ma) has a frequency distribution, it needs to be a value of 0 or more. Therefore, when the obtained Ha (ma) is a negative value, the absolute value is taken (that is, multiplied by -1), and Ha (ma) is changed to a value of 0 or more.

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

  When the two types of frequency distributions are determined, the actual size maximum circumscribed circle radius is calculated by executing the processing according to [Formula 14] as in the first method (step S513).

  The circumscribed circle and the inscribed circle in the circumscribed circle distribution and the inscribed circle distribution obtained from the target model are shown in FIG. FIG. 10 shows a polygon model when the polygon is triangular. For the three polygons selected from the polygon model, a triangle is created by connecting the average points having the average coordinates of the vertices of each polygon. The polygon model and the triangle in FIG. 10A and FIG. 10B are the same. Then, a circumscribed circle and an inscribed circle of a triangle are obtained. FIG. 10A shows the relationship between the triangle and its circumscribed circle and the radius, and FIG. 10B shows the relationship between the triangle and its inscribed circle and the radius. 10A and 10B, downward arrows indicate the radius of the circumscribed circle and the radius of the inscribed circle, respectively.

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

<3.4. Frequency distribution matching processing>
Next, the frequency distribution matching process in step S130 will be described. First, the frequency distribution matching unit 20 calculates the maximum actual size of the frequency distribution registered in the record re (re = 0,..., RE−1) from the RE records stored in the restriction model database 70. Extract the circumscribed circle radius. The actual size maximum circumscribed circle radius of the record re is expressed by Wmaxo (re). Then, the frequency distribution matching unit 20 executes processing according to the following [Equation 17] to match the circumscribed circle distribution Hd (md) calculated from the target model.

[Formula 17]
MD ′ = MD · Wmax / Wmaxo (re)
MD1 = md · Wmax / Wmaxo (re)
MD2 = (md + 1) · Wmax / Wmaxo (re) −1
When md ≦ MD ′, Hd ′ (md) = Σ _{md = MD1, MD2} Hd (md)
When MD ′ <md <MD, Hd ′ (md) = 0

  As a result of executing the process according to the above [Equation 17], a circumscribed circle distribution Hd ′ (md) subjected to the matching process is obtained. Subsequently, the frequency distribution matching unit 20 executes processing according to the following [Equation 18], thereby matching the inscribed circle distribution Ha (ma) calculated from the target model.

[Formula 18]
MA ′ = MA · Wmax / Wmaxo (re)
MA1 = ma · Wmax / Wmaxo (re)
MA2 = (ma + 1) · Wmax / Wmaxo (re) −1
When ma ≦ MA ′, Ha ′ (ma) = Σ _{ma = MA1, MA2} Ha (ma)
If MA ′ <ma <MA, Ha ′ (ma) = 0

  As a result of executing the process according to the above [Equation 18], the inscribed circle distribution Ha ′ (ma) subjected to the matching process is obtained.

<3.5. Emphasis component creation process>
Next, the emphasis component creation process in step S150 will be described. First, the emphasis component creation means 30 performs an emphasis component creation process from the frequency distribution of the target model subjected to the matching process. Specifically, the emphasis component creation means 30 performs processing according to the following [Equation 19] to correct the circumscribed circle distribution Hd ′ (md) subjected to the alignment processing so as to be within a predetermined range. To create an emphasis component.

[Formula 19]
Hmaxd = MAX _{md = 0, MD-1} Hd ′ (md)
Nd (md) = {Hd ′ (md) / Hmaxd} ^1/4

As a result of executing the processing according to the above [Equation 19], a circumscribed circle distribution Nd (md) corrected to fall within a predetermined range is obtained. This is an emphasis component. In the first equation, MAX _{md = 0, MD-1} Hd ′ (md) indicates the maximum value of Hd ′ (md). Therefore, since Hd ′ (md) may take 0 as a minimum value, the emphasis component Nd (md) falls within a range of 0 to 1 as a predetermined range. As described above, after normalization so as to fall within the range of 0 or more and 1 or less, {Hd ′ (md) / Hmaxd} is multiplied by the ¼ power, so that an accurate emphasis component can be obtained. Here, the reason why a real value is raised to the emphasis component is as follows. When the restriction model is a polygon model representing an article composed of multiple parts, the frequency distribution of the restriction model includes not only the component corresponding to the frequency distribution of the target model but also the frequency distribution of the target model. Many components corresponding to the part model are also included. Therefore, if the frequency distribution of the restriction model is multiplied by the emphasis component as it is, components corresponding to other component models that do not correspond to the frequency distribution of the target model are also emphasized. Therefore, when the emphasis component changes gently and the frequency distribution of the regulatory model is multiplied by the emphasis component, the emphasis processing for components corresponding to other component models that do not correspond to the frequency distribution of the target model included in the frequency distribution of the regulatory model Try to suppress. In this embodiment, ¼ is used as a value to be raised to {Hd ′ (md) / Hmaxd} as shown in [Equation 19], but it may be a real value less than 1. If possible, it is preferable to use a real value in the range of 1/5 to 1/3. If it is smaller than 1/5, the change in the component after the enhancement process becomes too gentle, and the component corresponding to the frequency distribution of the target model is not enhanced. On the other hand, if the ratio is larger than 1/3, the change in the component after the enhancement processing is too intense, and components corresponding to other component models that do not correspond to the frequency distribution of the target model are also emphasized. Subsequently, the emphasis component creation means 30 performs processing according to the following [Equation 20] to correct the inscribed circle distribution Ha ′ (ma) subjected to matching processing so as to be within a predetermined range. Create an emphasis component.

[Formula 20]
Hmaxa = MAX _{ma = 0, MA-1} Ha ′ (ma)
Na (ma) = {Ha ′ (ma) / Hmaxa} ^1/4

As a result of executing the processing according to the above [Equation 20], an inscribed circle distribution Na (ma) corrected to fall within a predetermined range is obtained. This is an emphasis component. In the first equation, MAX _{ma = 0, MA-1} Ha ′ (ma) indicates the maximum value of Ha ′ (ma). Therefore, since Ha ′ (ma) may take 0 as a minimum value, the emphasis component Na (ma) falls within a range of 0 to 1 as a predetermined range. As described above, after normalization is performed so that it falls within the range of 0 or more and 1 or less, {Ha ′ (ma) / Hmaxa} is subjected to the ¼ power, so that an accurate emphasis component can be obtained. In this embodiment, ¼ is used as a value to be raised to {Ha ′ (ma) / Hmaxa}, as shown in [Equation 20], but it may be a real value less than 1. If possible, it is preferable to use a real value in the range of 1/5 to 1/3.

<3.6. Frequency distribution enhancement processing>
Next, the frequency distribution enhancement processing in step S170 will be described. First, the frequency distribution enhancement means 40 performs enhancement processing on the frequency distribution of the matched target model and the frequency distribution stored in the restriction model database 70. Specifically, the frequency distribution emphasizing means 40 is first registered in a record re (re = 0,..., RE-1) among RE records stored in the restriction model database 70. As the frequency distribution, circumscribed circle distribution Hdo (re, md) (md = 0,..., MD-1), inscribed circle distribution Hao (re, ma) (ma = 0,..., MA-1). To extract. Then, by executing the processing according to the following [Equation 21], the circumscribed circle distribution Hd ′ (md) of the matched target model and the circumscribed circle distribution Hdo of each record re stored in the restriction model database 70 For (re, md), enhancement processing is performed so that all values in the range of md = 0,..., MD−1 are updated.

[Formula 21]
Hd ″ (md) = Hd ′ (md) · Nd (md)
Hdo ′ (re, md) = Hdo (re, md) · Nd (md)

  In the above [Formula 21], the circumscribed circle distribution Hd ′ (md) of the matched target model and the circumscribed circle distribution Hdo (re, md) of each record re stored in the regulation model database 70 are as follows. A process of multiplying all values in the range of md = 0,..., MD-1 by the emphasis component Nd (md) is performed. The emphasis component Nd (md) plays a role as a so-called window function that extracts only a specific section of data, and the above [Equation 21] performs a process of multiplying the window function. As a result of executing the process according to the above [Equation 21], the circumscribed circle distribution Hd ″ (md) of the target model subjected to the emphasis process and the circumscribed circle distribution Hdo ′ (re, md) of each of the emphasized regulation models are obtained. Obtained as an emphasis frequency distribution. Subsequently, the frequency distribution emphasizing means 40 stores the inscribed circle distribution Ha ′ (ma) of the matched target model in the regulated model database 70 by executing processing according to the following [Equation 22]. Emphasis processing is performed so that all values in the range of ma = 0,..., MA-1 are updated with respect to the inscribed circle distribution Hao (re, ma) of each record re.

[Formula 22]
Ha ″ (ma) = Ha ′ (ma) · Na (ma)
Hao ′ (re, ma) = Hao (re, ma) · Na (ma)

  In the above [Equation 22], the inscribed circle distribution Ha ′ (ma) of the normalized target model and the inscribed circle distribution Hao (re, ma) of each record re stored in the regulation model database 70 are respectively shown. On the other hand, a process of multiplying all values in the range of ma = 0,..., MA-1 by the emphasis component Na (ma) is performed. The emphasis component Na (ma) plays a role as a so-called window function that extracts only a specific section of data, and the above [Equation 22] performs a process of multiplying the window function as in [Equation 21]. Will be. As a result of executing the processing according to the above [Equation 22], the inscribed circle distribution Ha ″ (ma) of the target model subjected to the emphasis process, and the inscribed circle distribution Hao ′ (re, ma) of each of the emphasized regulation models. ) Is obtained as an emphasis frequency distribution.

<3.7. Frequency distribution matching process>
Next, the frequency distribution matching process in step S200 will be described. FIG. 11 is a flowchart showing the frequency distribution matching process. First, the frequency distribution matching unit 50 acquires a frequency distribution (emphasized frequency distribution) extracted from the restriction model database 70 and subjected to an emphasis process (step S610). As the frequency distribution subjected to the enhancement processing, the circumscribed circle distribution Hdo ′ (re, md) (md = 0,..., MD-1), the inscribed circle distribution Hao ′ (re, ma) (ma = 0,. ., MA-1) is acquired. In the frequency distribution matching processing in step S200, the enhancement frequency distributions Hd ″ (md) and Hdo ′ (re, md) calculated in the above [Formula 21] and [Formula 22] are described for the sake of simplicity. , Ha ″ (ma) and Hao ′ (re, ma) are treated as Hd (md), Hdo (re, md), Ha (ma), and Hao (re, ma), respectively.

  Next, the frequency distribution matching unit 50 calculates a peak position correlation between the circumscribed circle distribution of the emphasized target model and the circumscribed circle distribution of the regulated model extracted from the regulated model database 70 and enhanced (Step). S620). The peak position correlation indicates a correlation between peak positions that are positions where the frequency is highest in the frequency distribution. The frequency distribution matching unit 50 executes processing according to the following [Equation 23], thereby performing circumscribed circle distribution Hd (md) of the emphasized target model and circumscribed circle distribution Hdo ( The peak position correlation Pd (re) of (re, md) is calculated.

[Formula 23]
Pd (re) = {MD / 4- | mdM-mdoM (re) |} .400 / MD

  In the above [Equation 23], mdM is the value of md when Hd (md) is maximum, and mdoM (re) is the value of md when Hdo (re, md) is maximum, that is, the peak position. is there. Moreover, | mdM−mdoM (re) | is a peak position difference indicating a difference between peak positions. When mdM and mdoM (re) match, the peak position difference | mdM−mdoM (re) | = 0 and the peak position correlation Pd (re) = 100, and when mdM and mdoM (re) are farthest, theoretically Since the peak position difference | mdM−mdoM (re) | = MD−1 and the peak position correlation Pd (re) is approximately −300, the peak position correlation Pd (re) is theoretically −300 <Pd ( re) takes a value of ≦ 100. However, since it is rare that the peak position difference | mdM−mdoM (re) | exceeds MD / 2 based on experience, the peak position correlation Pd (re) is usually −100 <Pd (re) ≦ 100. Takes a value. The peak position correlation Pd (re) is a value that increases as the peak position difference | mdM−mdoM (re) | decreases, and is determined based on the peak position difference.

  Next, the frequency distribution matching unit 50 compares the circumscribed circle distribution peak position correlation Pd (re) calculated in step S620 with the determination threshold (step S630). If the peak position correlation Pd (re) is greater than or equal to the determination threshold value, it is determined to be suitable, and if the peak position correlation Pd (re) is smaller than the determination threshold value, it is determined to be non-conforming. The determination threshold can be arbitrarily set as long as it is a positive value, but here it is +50.0.

  When it is determined to be suitable in step S630, the frequency distribution matching unit 50 determines the inscribed circle distribution of the emphasized target model and the inscribed circle of the regulated model extracted from the regulated model database 70 and enhanced. A peak position correlation is calculated between the distributions (step S640). The frequency distribution matching unit 50 executes processing according to the following [Equation 24], thereby performing the inscribed circle distribution Ha (ma) of the emphasized target model and the inscribed circle distribution of the emphasized restricted model. The peak position correlation Pa (re) of Hao (re, ma) is calculated.

[Formula 24]
Pa (re) = {MA / 4- | maM-maoM (re) |} .400 / MA

  In the above [Equation 24], maM is the value of ma when Ha (ma) is maximum, and maoM (re) is the value of ma when Hao (re, ma) is maximum, that is, the peak position. is there. Further, | maM−maoM (re) | is a peak position difference indicating a difference between peak positions. When maM and maoM (re) match, the peak position difference | maM−maoM (re) | = 0 and the peak position correlation Pa (re) = 100, and when maM and maoM (re) are farthest, theoretically Since the peak position difference | maM−maoM (re) | = MA−1 and the peak position correlation Pa (re) is approximately −300, the peak position correlation Pa (re) is theoretically −300 <Pa ( re) takes a value of ≦ 100. However, since it is rare that the peak position difference | maM−maoM (re) | exceeds MA / 2 based on experience, the peak position correlation Pa (re) is normally −100 <Pa (re) ≦ 100. Takes a value. The peak position correlation Pa (re) is a value that increases as the peak position difference | mdaM−maoM (re) | becomes smaller, and is determined based on the peak position difference.

  Next, the frequency distribution matching unit 50 compares the peak position correlation Pa (re) of the inscribed circle distribution calculated in step S640 with the determination threshold value (step S650). If the peak position correlation Pa (re) is greater than or equal to the determination threshold, it is determined to be suitable, and if the peak position correlation Pa (re) is smaller than the determination threshold, it is determined to be non-conforming. The determination threshold can be arbitrarily set as long as it is a positive value, but here it is +50.0. If it is determined to be suitable in step S650, the frequency distribution matching unit 50 determines that the output should be restricted (output inappropriate) because the target model and the restriction model of the record re match. Do. FIG. 12 is a diagram showing the relationship between the frequency distribution (histogram) and the peak position. In FIG. 12, the left side shows the restriction model, and the right side shows the target model. The upper row shows the circumscribed circle distribution, and the lower row shows the inscribed circle distribution.

  When it is determined as non-conforming in either step S630 or S650, the frequency distribution matching unit 50 finishes matching the target model with the restriction model of the record re, and proceeds to matching with the restriction model of the next record re + 1. . When the process according to the flowchart of FIG. 11 is performed on all records in the restriction model database 70 and there is at least one applicable restriction model, the output should be restricted to the target model (output inappropriate) ) ", The collation process in step S200 is terminated.

<3.7.1. Collation processing using other indices>
In the above embodiment, the peak position correlations of the frequency distribution are collated, but it is also possible to perform collation using another index. Here, as an example, a case where a correlation coefficient is used instead of the peak position correlation will be described.

  In this case, the frequency distribution matching unit 50 calculates a correlation coefficient between each element between the circumscribed circle distribution calculated from the target model and the circumscribed circle distributions of the restriction model extracted from the restriction model database 70 (step S620). ). The correlation coefficient is an index indicating the strength of the relationship between two array elements. Here, it is used to determine the strength of similarity between the target model and the regulation model. First, by executing processing according to the following [Equation 25], the average value Ad of the circumscribed circle distribution Hd (md) of the target model and the average value Ado () of the circumscribed circle distribution Hdo (re, md) of the regulated model re) is calculated.

[Formula 25]
Ad = Σ _{md = 0, MD-1} Hd (md) / MD
Ado (re) = Σ _{md = 0, MD-1} Hdo (re, md) / MD

  Subsequently, by executing processing according to the following [Equation 26], the standard deviation Sd of the circumscribed circle distribution Hd (md) of the target model and the standard deviation Sdo of the circumscribed circle distribution Hdo (re, md) of the regulated model (Re) is calculated.

[Formula 26]
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 26]. 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 27], which will 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 27] is executed, whereby the circumscribed circle distribution of the target model and the circumscribed circle distribution of the restriction model corresponding to the record re A correlation coefficient Cd (re) is calculated.

[Formula 27]
Cd (re) = [Σ _{md = 0, MD-1} (Hd (md) −Ad) · (Hdo (re, md) −Ado (re))] / (Sdo (re) · Sd)

  In the above [Expression 27], the subscript “md = 0, MD-1” of Σ indicates that the sum is obtained when md takes all integers from 0 to MD-1. The correlation coefficient Cd (re) is obtained by dividing the covariance of the frequency distributions Hd (md) and Hdo (re, md) by 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 [27]. 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 Cd (re) shown in the above [Equation 27] is called “normalized correlation coefficient”. There are also customs.

  Next, the frequency distribution matching unit 50 compares the correlation coefficient Cd (re) of the circumscribed circle distribution calculated in step S620 with the determination threshold value (step S630). When the correlation coefficient Cd (re) is greater than or equal to the determination threshold value, it is determined as conforming, and when the correlation coefficient Dd (re) is smaller than the determination threshold value, it is determined as non-conforming. The determination threshold value can be arbitrarily set as long as it is a positive value, but here, when the correlation coefficient value taking a range of +5.0 (−1 to +1) is multiplied by 100 and expressed in% dimension ).

  If it is determined in step S630 that it is suitable, the frequency distribution matching unit 50 calculates a correlation coefficient between the elements based on the inscribed circle distribution of the target model and the inscribed circle distribution of the restriction model (step S640). ). First, by executing the processing according to the following [Equation 28], the average value Aa of the inscribed circle distribution Ha (ma) of the target model and the average value of the inscribed circle distribution Hao (re, ma) of the regulated model Aao (re) is calculated.

[Formula 28]
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 29], the standard deviation Sa of the inscribed circle distribution Ha (ma) of the target model and the standard of the inscribed circle distribution Hao (re, ma) of the regulated model Deviation Sao (re) is calculated.

[Formula 29]
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 29]. 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 30], 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, using the calculated average value and standard deviation, the process according to the following [Equation 30] is executed, whereby the inscribed circle distribution of the target model and the inscribed circle of the restriction model corresponding to the record re A distribution correlation coefficient Ca (re) is calculated.

[Formula 30]
Ca (re) = [Σma _{= 0, MA-1} (Ha (ma) -Aa). (Hao (re, ma) -Aao (re))] / (Sao (re) .Sa)

  In the above [Equation 30], 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 Ca (re) is obtained by dividing the covariance of the frequency distributions Ha (ma) and Hao (re, ma) by the respective standard deviations. Generally, the processing load is high when the division of the square root of the denominator is performed twice as in the calculation of [Expression 30]. 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 Ca (re) shown in the above [Equation 30] is called “normalized correlation coefficient”. There are also customs.

  Next, the frequency distribution matching unit 50 compares the correlation coefficient Ca (re) of the inscribed circle distribution calculated in step S640 with a determination threshold value (step S650). When the correlation coefficient Ca (re) is greater than or equal to the determination threshold value, it is determined as conforming, and when the correlation coefficient Ca (re) is smaller than the determination threshold value, it is determined as non-conforming. The determination threshold value can be arbitrarily set as long as it is a positive value, but here, when the correlation coefficient value taking a range of +5.0 (−1 to +1) is multiplied by 100 and expressed in% dimension ). If it is determined to be suitable in step S650, the frequency distribution matching unit 50 determines that the output should be restricted (output inappropriate) because the target model and the restriction model of the record re match. Do.

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

<Data output to 4.3D printer>
In step S200, if the frequency distribution matching unit 50 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 modeling apparatus. That is, the target model that is a polygon model whose output is permitted by the three-dimensional object modeling data output restriction device is output to the three-dimensional object modeling device that models the three-dimensional object. On the other hand, if the frequency distribution matching means 50 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 shaping 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.

<5. Calculation and registration of regulatory model frequency distribution>
As a modified example, the circumscribed circle distribution and the inscribed circle distribution calculated with respect to the restriction model at the place different from the three-dimensional object shaping data output restriction device provided with the frequency distribution matching means 50 are used as the three-dimensional object shaping data. You may make it transmit and register to an output control apparatus via a network.

  FIG. 13 is a hardware configuration diagram of a three-dimensional object formation system including a three-dimensional object formation data output restriction device according to a modification. In the three-dimensional object formation system shown in FIG. 13, the three-dimensional object formation data output restriction device 101 communicates with the public network such as the Internet in the configuration of the three-dimensional object formation data output restriction device 100 shown in FIG. 4. The network communication unit 8 is provided. The regulation model frequency distribution calculation device 102 can be realized by a general-purpose computer. As shown in FIG. 13, a CPU (Central Processing Unit) 1a, a RAM (Random Access Memory) 2a that is a main memory of the computer, A large-capacity storage device 3a such as a hard disk or flash memory for storing programs and data executed by the CPU 1a, a key input I / F (interface) 4a such as a keyboard and a mouse, and an external device such as a data storage medium A data input / output I / F (interface) 5a for data communication, a display unit 6a that is a display device such as a liquid crystal display, and a network communication unit 8a for communicating with a public network such as the Internet are provided. Connected through. The network communication unit 8 of the three-dimensional object formation data output restriction device 101 and the network communication unit 8a of the restriction model frequency distribution calculation device 102 communicate with each other, and the restriction model frequency distribution calculation device 102 transmits the three-dimensional object formation data output restriction device 101. Thus, it is possible to transmit two types of frequency distributions calculated for the regulatory model.

  In FIG. 13, the three-dimensional object formation data output restriction device 101 and the 3D printer 7 are shown in a separated form. However, most of the currently available 3D printer products include the three-dimensional object formation data output restriction device 101. Constituent elements, such as CPU1, RAM2, storage device 3, key input I / F4 (not a keyboard / mouse for general-purpose computers, but several buttons at a numeric keypad level), data input / output I / F5, display unit 6 (several lines) A small liquid crystal panel capable of displaying the above-mentioned characters, a touch panel is often overlapped to serve also as a key input I / F 4), and a network communication unit 8 (wireless LAN function) is also provided in a small but overlapping manner. Accordingly, the 3D printer itself can directly receive the data for modeling a three-dimensional object via an external storage medium or the Internet, and can be operated to model a three-dimensional object alone (especially in the case of a 3D printer for consumer use, this form is preferred. Many). That is, the three-dimensional object formation data output restriction device 101 and the 3D printer 7 shown in FIG. 13 are often housed in a single casing and distributed as a product called “3D printer”.

  In the regulated model frequency distribution calculation device 102, the CPU 1a executes a program stored in the storage device 3a, thereby realizing a second frequency distribution calculation unit and a frequency distribution transmission unit. The second frequency distribution calculation means realized by the restriction model frequency distribution calculation device 102 has the same function as the frequency distribution calculation means 10 realized by the three-dimensional object formation data output restriction device 100, and is compatible with the restriction model. The circumscribed circle distribution and the inscribed circle distribution are calculated as two types of frequency distribution. The frequency distribution transmission unit transmits the circumscribed circle distribution and the inscribed circle distribution, which are two types of frequency distributions calculated for the restriction model, to the three-dimensional object formation data output restriction device 101. In the three-dimensional object shaping data output restriction device 101, when the network communication unit 8 receives the frequency distribution from the restriction model frequency distribution calculation device 102, the CPU 1 executes a predetermined program and functions as a frequency distribution registration unit. The frequency distribution is registered in the regulation model database 70 realized by the storage device 3.

<6. Cloud-type 3D object modeling system>
The present invention can also be applied to a cloud-type three-dimensional object modeling system. FIG. 14 is a hardware configuration diagram of a cloud-type three-dimensional object modeling system. In the three-dimensional object modeling system shown in FIG. 14, 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. 14, 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. 13. 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. 14, a CPU 11a, a RAM 12a, a storage device 13a, a key input I / F 14a, a data input / output I / F 15a, a display unit 16a, and a network communication unit 18a are connected to each other via a bus. The network communication unit 18 of the output control terminal 201 and the network communication unit 18a of the processing server 202 communicate with each other, and send output suitability data from the processing server 202 to the output control terminal 201 based on the judgment of output suitability. It is possible. In FIG. 14, the output control terminal 201 and the 3D printer 7 are shown in a separated form. However, as in the example of FIG. 13, the CPU 11, the RAM 12, and the constituent elements of the output control terminal 201 are added to the 3D printer product. 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. 15 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 80 and an output suitability data transmitting unit 90 in addition to the units of the three-dimensional object modeling data output restriction device shown in FIG. It has a configuration. The target model storage means 61 stores not the target model itself but two types of frequency distributions calculated for the target model. Further, the frequency distribution calculating means 10 is configured to be provided not in the processing server 202 but in the output control terminal 201.

  Components having the same functions as those of the three-dimensional object shaping data output restricting device shown in FIG. The target model receiving unit 80 is a unit that receives the frequency distribution of the target model transmitted from the output control terminal 201 and registers it in the target model storage unit 61. The CPU 11a executes a predetermined program, and the network communication unit This is realized by controlling 18a. The output suitability data transmission means 90 is means for sending output suitability data to the output control terminal 201 based on “whether or not the output should be regulated” determined by the frequency distribution matching means 50, and the CPU 11 a This is realized by executing a predetermined program and controlling the network communication unit 18a.

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

  The processing operation of the cloud-type three-dimensional object modeling system shown in FIGS. 14 and 15 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. 8 or FIG. Generate a frequency distribution. Further, the CPU 11 transmits the generated frequency distribution to an address such as a URL set in advance in the storage device 13 via the network communication unit 18. By using a method of transmitting a frequency distribution with a significantly smaller amount of information than polygon data, not only the transmission time is greatly shortened, but also the polygon data that is a copyrighted work is not transmitted to the processing server 202 side. Since no copy remains on the processing server 202 side, copyright infringement can be avoided.

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

  In the processing server 202, when the target model receiving unit 80 receives the frequency distribution of the target model transmitted from the output control terminal 201, it stores the frequency distribution in the target model storage unit 61. Here, according to the flowchart shown in FIG. 6, the frequency distribution matching means 20, the emphasis component creating means 30, the frequency distribution emphasizing means 40, and the frequency distribution matching means 50 perform processing, and output of the target model corresponding to the received frequency distribution. Judge suitability. Output propriety data as a result of output propriety is passed from the frequency distribution matching unit 50 to the output propriety data transmitting unit 90. Then, the output suitability data transmission means 90 transmits the output suitability data with the model ID added to the output control terminal 201 that is the transmission source (access source) of the frequency distribution.

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

<7. Matching case>
16 and 17 show a collation example of the entire model and its parts. The entire model corresponds to the aforementioned regulatory model, and the model parts correspond to the aforementioned target model. FIG. 16 shows the case where the frequency distribution matching process of the target model on the part side is not performed, and FIG. 17 shows the case where the frequency distribution matching process of the target model is performed. In the figure, the values of Corr (D, A) and Peak (D, A) described in two middle stages are the values before performing the enhancement process (described in the center of the middle stage) and after performing the enhancement process (middle stage). (Corresponding to the right end of FIG. 4) shows the matching result between the frequency distributions shown above and below, Corr (D, A) shows the correlation coefficients Cd (re) and Ca (re), and Peak (D, A) shows , Peak position correlations Pd (re) and Pa (re) are shown, respectively. Each value of D indicates a matching result of the circumscribed circle distribution, and value of A indicates a matching result of the inscribed circle distribution. Before performing the matching process, the results of matching with different parts that should be judged as nonconforming are shown. By performing the matching process, there may be little difference in the correlation coefficient, or conversely, the correlation may be low. When the peak position correlation is used, the difference opens before and after the matching process and becomes a positive side. This is because the regulatory model that has undergone the alignment process includes components of other part models that are not included in the target model. If there are many other component component models, it is rather preferable to perform the alignment process. Correlation coefficient decreases. On the other hand, the peak position correlation is relatively less affected by the components of other component models. Further, it can be seen that whether or not the emphasis process is performed further increases the difference in peak position correlation before and after the matching process, thereby enabling highly accurate collation. This is because the regulated model that has undergone the alignment process includes components of other part models that are not included in the target model, but by performing the enhancement process, the components of other part models that are not included in the target model This is because is suppressed.

  The three-dimensional object formation data output restriction device 100 not only determines conformity / nonconformity, but also includes the peak position correlation, correlation coefficient, and the like calculated by the frequency distribution matching unit 50 in steps S620 and S640 shown in FIG. The display unit 6 can also output. By visually confirming the peak position correlation and the correlation coefficient displayed and output by the display unit 6, for example, in the case of non-conformity, it is possible to determine not only the non-conformity but also the degree of the non-conformity. .

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

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

  Further, in the above embodiment, an emphasis component is created by correcting the frequency value of the matched target model so that the value of each frequency falls within a predetermined range, and the original frequency distribution is multiplied by the emphasis component. However, the emphasis frequency distribution of the target model and the emphasis frequency distribution of the regulation model are collated, but without such emphasis processing, the frequency distribution of the matched target model and the regulation model extracted from the database You may make it collate frequency distribution.

1, 1a, 11, 11a... CPU (Central Processing Unit)
2, 2a, 12, 12a ... RAM (Random Access Memory)
3, 3a, 13, 13a ... Storage device 4, 4a, 14, 14a ... Key input I / F
5, 5a, 15, 15a ... Data I / O I / F
6, 6a, 16, 16a ... display unit 7 ... 3D printer (three-dimensional object modeling apparatus)
7a: Data processing unit 7b: Output unit 8, 8a, 18, 18a: Network communication unit 10: Frequency distribution calculating means (frequency distribution calculating device)
20 ... Frequency distribution matching means 30 ... Emphasis component creation means 40 ... Frequency distribution enhancement means 50 ... Frequency distribution matching means 60, 61 ... Target model storage means 70 ... Regulatory model database 80 ... Target model receiving means 90 ... Output propriety data sending means 100, 101 ... Three-dimensional object shaping data output restricting device 102 ... Restricted model frequency distribution calculating device 201 ... Output control terminal 202 ..Processing server

Claims (21)

A device for determining whether or not to regulate when outputting a polygon model expressed as a set of polygons to a three-dimensional object modeling apparatus as three-dimensional object modeling data,
A database in which a frequency distribution of parameters characterizing a triangle formed based on three polygons in a restriction model, which is a polygon model whose output is to be restricted, is registered;
For a target model that is a polygon model to be output, parameters that characterize a triangle formed by predetermined points on three polygons selected from the target model are calculated, and the areas and normals of the three polygons are calculated. A frequency distribution calculating means for calculating a frequency distribution of the parameter while weighting based on a vector;
The frequency of creating the frequency distribution of the matched target model by performing processing for matching the scale of the parameter of the frequency distribution of the target model with the scale of the parameter of the frequency distribution of the regulatory model registered in the database Distribution matching means;
A frequency distribution matching unit that matches the frequency distribution of the matched target model with the frequency distribution of the restriction model and determines whether or not the output should be restricted;
A data output regulating device for three-dimensional object formation characterized by comprising:   2. The three-dimensional object formation data output restriction device according to claim 1, wherein the parameter characterizing the triangle is a circumscribed circle radius of a triangle formed by a predetermined point of each of the three polygons. Emphasis component creation for correcting the frequency value of the matched target model so that the value of each frequency falls within a predetermined range, and creating an emphasis component that is a power of less than 1 to the corrected frequency value Means,
Frequency distribution emphasizing means for creating the frequency distribution of the target model and the frequency distribution of the regulatory model by multiplying the frequency distribution of the matched target model and the frequency distribution of the regulatory model by the emphasis component, respectively. In addition,
3. The three-dimensional object formation data output restriction device according to claim 1, wherein the frequency distribution collating unit collates the enhancement frequency distribution of the target model with the enhancement frequency distribution of the restriction model.   The frequency distribution calculating unit classifies the polygons in the target model into three groups of a first group, a second group, and a third group, and selects one polygon from each group. The three-dimensional object shaping data output restriction device according to any one of claims 1 to 3, wherein the three polygons are selected.   The frequency distribution calculating means creates a random number array in which N / 3 consecutive integers are randomly replaced when the number of polygons in each group is N / 3, and from the beginning to the end of the random number array. The reverse random number array is created by reversing the order of the above, and the random number array is applied to one of the three groups, and the reverse random number array is applied to the other group, and the order of the polygons in the group is determined. 5. The three-dimensional object formation data output restriction device according to claim 4, wherein the polygons are selected in order from the top of each group after the replacement. The frequency distribution calculating means includes:
The random number array is applied to the first group of polygons and the inverted random number array is applied to the second group of polygons to change the order, and the polygons are selected without changing the order of the third group of polygons. N / 3 triangles are formed,
The random number array is applied to the second group of polygons and the inverted random number array is applied to the third group of polygons to change the order, and the polygons are selected without changing the order of the first group of polygons. N / 3 triangles are formed,
The random number array is applied to the third group of polygons and the inverted random number array is applied to the first group of polygons to change the order, and the polygons are selected without changing the order of the second group of polygons. 6. The three-dimensional object formation data output restriction device according to claim 5, wherein N / 3 triangles are formed. The frequency distribution calculating means includes:
Each time the frequency distribution is calculated, the order of polygons in two groups out of the three groups is changed, and the frequency distribution is recalculated a predetermined number of times, and the calculated frequency is calculated. The three-dimensional object shaping data output restriction device according to claim 5 or 6, wherein an average value of the distribution is a frequency distribution to be collated. The frequency distribution calculating means includes:
Each time the frequency distribution is calculated, the order of polygons in two groups among the three groups is changed, and the process of calculating the frequency distribution is repeated.
When the frequency distribution immediately after the calculation is compared with the frequency distribution obtained immediately before that, and the similarity is found as a result of the comparison, the frequency distribution immediately after the calculation is the frequency distribution to be verified. The three-dimensional object formation data output restriction device according to claim 5 or 6,   In calculating the frequency distribution, the frequency distribution calculating unit prepares a one-dimensional array composed of a predetermined number of elements, and equally divides the calculated parameter maximum value range into the predetermined number In the above, by assigning each parameter to any one of the predetermined number of elements based on the value of the parameter, and adding the weight value calculated based on the area and normal vector of the three polygons to the element 9. The three-dimensional object formation data output restriction device according to claim 7 or 8, wherein the frequency distribution is calculated.   The frequency distribution calculating means multiplies the area of the polygon by an inner product value of a unit vector from the average point of the triangle formed based on the three polygons to each vertex and a normal vector of the polygon including the vertex. 10. The weighting is performed based on an area and a normal vector of the three polygons, using an average value of the three polygons as a weight value. 11. The data output regulation apparatus for three-dimensional object modeling of description.   The frequency distribution calculating means divides the value of each element by the sum of the weight values for each polygon constituting each target model, and gives the absolute value if the element is a negative value The data output regulation apparatus for three-dimensional object formation according to claim 10, wherein the frequency distribution is calculated.   As parameters for characterizing the triangle, two types of parameters, a first parameter and a second parameter, are used, and the frequency distribution includes a first frequency distribution corresponding to the first parameter and a second frequency corresponding to the second parameter. The three-dimensional object shaping data output restriction device according to claim 1, wherein the distributions are two kinds of frequency distributions.   The radius of the circumscribed circle of the triangle is used as the first parameter, the radius of the inscribed circle of the triangle is used as the second parameter, and the circumscribed circle distribution is a frequency distribution of the radius of the circumscribed circle as the first frequency distribution. The inscribed circle distribution that is a frequency distribution of the radius of the inscribed circle is used as the second frequency distribution, and the three-dimensional object formation data output restriction device according to claim 12.   In calculating the inscribed circle distribution, the frequency distribution calculating unit prepares a one-dimensional array composed of a predetermined number of elements, and 35% to 50% of the maximum radius of the calculated circumscribed circle The range normalized by the value of is equally divided into the predetermined number, and the radius of each inscribed circle is assigned to any element of the predetermined number based on the value of the radius of the inscribed circle, 14. The three-dimensional object according to claim 13, wherein the inscribed circle distribution is calculated by adding a weight value calculated based on the area and normal vector of the three polygons to the element. Data output regulation device for object modeling.   The frequency distribution matching unit calculates a peak position that gives a maximum value when matching the frequency distribution of the matched target model with the frequency distribution of the regulatory model, and based on the difference between the calculated peak positions. 15. The three-dimensional object formation data output restriction device according to claim 1, wherein it is determined whether or not the output should be restricted.   A parameter characterizing a triangle formed by a predetermined point on three polygons selected from the regulation model is calculated for the regulation model, and weighted based on the area and normal vector of the three polygons. However, the apparatus further comprises registration means for receiving the frequency distribution calculated by the regulatory model frequency distribution calculating device for calculating the frequency distribution of the parameter and registering the received frequency distribution in the database. The three-dimensional object modeling data output restriction device according to claim 15. Means for outputting the target model to a connected three-dimensional object shaping apparatus;
When it is determined that the output should be regulated by the frequency distribution matching unit that is executed in parallel with the three-dimensional object modeling process by the three-dimensional object modeling apparatus, the three-dimensional object modeling apparatus includes the target model. Means for outputting an output stop command;
The three-dimensional object formation data output restriction device according to any one of claims 1 to 16, further comprising: The output control terminal and the processing server are connected via a network,
The output control terminal has the frequency distribution calculating means,
The processing server
The database;
Receiving means for receiving a frequency distribution of the target model from the output control terminal via a network;
The frequency distribution matching means;
Output suitability data transmission means for transmitting data based on whether the output should be regulated or not, determined by the frequency distribution matching means, to the output control terminal;
The three-dimensional object formation data output restriction device according to any one of claims 1 to 17, wherein the three-dimensional object formation data output restriction device is provided. A data output regulation device for three-dimensional object modeling according to any one of claims 1 to 18,
A three-dimensional object modeling apparatus that models a three-dimensional object using a polygon model whose output is permitted by the three-dimensional object modeling data output restriction device;
A three-dimensional object forming system characterized by comprising: A device that creates a frequency distribution based on the polygon model in order to determine whether or not to restrict when a polygon model expressed as a set of polygons is output to the three-dimensional object modeling device as three-dimensional object modeling data. There,
For a target model that is a polygon model to be output, parameters that characterize a triangle formed by predetermined points on three polygons selected from the target model are calculated, and the areas and normals of the three polygons are calculated. A frequency distribution calculation device that calculates a frequency distribution of the parameter while weighting based on a vector.   A program for causing a computer to function as the three-dimensional object formation data output restriction device according to any one of claims 1 to 18.