KR102125973B1 - Method for detecting and correcting error of mesh and apparatus for the same - Google Patents

Abstract

A method for detecting and correcting errors in a 3D mesh model is disclosed. The error detection method of the mesh model according to the present disclosure is a process of identifying at least one mesh unit based on halfedge information, and based on the normal vector information of the at least one mesh unit, the at least one mesh unit A process of setting at least one cluster including, a process of detecting an inversion error for the at least one cluster, and a process of correcting the at least one mesh unit of the at least one cluster in which an inversion error is detected. It can contain.

Description

METHOD FOR DETECTING AND CORRECTING ERROR OF MESH AND APPARATUS FOR THE SAME

The present disclosure relates to a 3D data processing technology, and more particularly, to a method and apparatus for detecting and correcting errors in 3D data used for 3D printing.

Since 3D data (hereinafter referred to as 3D display data) configured for screen output is for outputting an object on a screen, it has been developed mainly to express or display the appearance of an object. Accordingly, the 3D display data is configured to efficiently and accurately represent the appearance of the object on the display, and may be configured not to include information that is not essential to express the appearance of the object.

For example, the 3D display data may be configured not to represent minute holes provided relatively smaller than the resolution of the display, or may not include information related to the thickness of the object.

On the other hand, since the 3D data (hereinafter, 3D printing data) configured for 3D printing is for forming an object that exists physically, it is required to pass a relatively strict criterion than 3D display data in terms of mesh topology. .

In particular, 3D printing data is essential to information related to the thickness of an object. The thickness of such an object may be expressed using a normal vector for mesh units, and the 3D printing data may be configured to include normal vector information for mesh units.

The normal vector information may be directly input by the user or may be actively generated based on a predetermined algorithm. In the process of inputting or generating normal vector information into the 3D printing data, an error in the direction of the normal vector may be generated. There is a possibility that it will happen.

An object of the present disclosure is to provide a method and apparatus capable of automatically detecting and correcting errors in mesh information included in 3D printing data.

Another technical problem of the present disclosure is to provide a method and apparatus capable of automatically detecting and correcting a mesh flipping error based on mesh information and normal vector information included in 3D printing data.

The technical problems to be achieved in the present disclosure are not limited to the technical problems mentioned above, and other technical problems that are not mentioned will be clearly understood by those skilled in the art from the description below. Will be able to.

According to an aspect of the present disclosure, an error detection method of a mesh model may be provided. The method includes identifying at least one mesh unit based on halfedge information and at least one cluster including the at least one mesh unit based on the normal vector information of the at least one mesh unit. It may include a setting process, detecting a flip error for the at least one cluster, and correcting the at least one mesh unit of the at least one cluster in which a flip error is detected.

According to another aspect of the present disclosure, an error detection apparatus of a mesh model may be provided. The apparatus includes at least one mesh unit that identifies at least one mesh unit based on halfedge information, and at least one mesh unit that includes the at least one mesh unit based on the normal vector information of the at least one mesh unit. It includes a clustering processing unit for setting a cluster, an error detection unit for detecting a flip error for the at least one cluster, and an error correction unit for correcting the at least one mesh unit of the at least one cluster in which a flip error was detected. Can.

The features briefly summarized above with respect to the present disclosure are merely illustrative aspects of the detailed description of the present disclosure described below, and do not limit the scope of the present disclosure.

According to the present disclosure, a method and apparatus for automatically detecting and correcting errors in mesh information included in 3D printing data may be provided.

In addition, according to the present disclosure, a method and apparatus for automatically detecting and correcting a mesh flipping error based on mesh information and normal vector information included in 3D printing data may be provided.

In addition, according to the present disclosure, by automatically detecting and correcting an inversion error of the mesh, it is possible to construct accurate 3D printing data and generate a highly complete 3D printing object based on this.

The effects obtainable in the present disclosure are not limited to the above-mentioned effects, and other effects not mentioned may be clearly understood by those skilled in the art from the following description. will be.

1 is a block diagram illustrating an apparatus for detecting errors in a mesh model according to an embodiment of the present disclosure.
2A and 2B are diagrams illustrating a mesh structure processed by the apparatus for detecting errors in a mesh model according to an embodiment of the present disclosure.
3 is a diagram illustrating a cluster structure processed by an error detection device of a mesh model according to an embodiment of the present disclosure.
4 is a diagram illustrating an operation of correcting an error of a mesh by an error detection device of a mesh model according to an embodiment of the present disclosure.
5 is a flowchart illustrating a sequence of an error detection method of a mesh model according to an embodiment of the present disclosure.
6 is a block diagram illustrating a computing system executing a method and apparatus for detecting errors in a mesh model according to an embodiment of the present disclosure.

Hereinafter, exemplary embodiments of the present disclosure will be described in detail with reference to the accompanying drawings so that those skilled in the art to which the present disclosure pertains can easily carry out the embodiments. However, the present disclosure can be implemented in many different forms and is not limited to the embodiments described herein.

In describing embodiments of the present disclosure, when it is determined that detailed descriptions of known configurations or functions may obscure the subject matter of the present disclosure, detailed descriptions thereof will be omitted. In the drawings, parts irrelevant to the description of the present disclosure are omitted, and similar reference numerals are used for similar parts.

In the present disclosure, when a component is said to be "connected", "coupled" or "connected" with another component, this is not only a direct connection relationship, but also an indirect connection relationship in which another component exists in the middle. It may also include. Also, when a component is said to "include" or "have" another component, this means that other components may be further included, not specifically excluded, unless otherwise stated. .

In the present disclosure, terms such as a voice parameter, a second, etc. are used only for the purpose of distinguishing one component from other components, and do not limit the order or importance of components, etc., unless otherwise specified. Accordingly, within the scope of the present disclosure, the voice parameter component in one embodiment may be referred to as a second component in another embodiment, and similarly, the second component in one embodiment is a voice parameter component in another embodiment It can also be called.

In the present disclosure, the components that are distinguished from each other are for clarifying each feature, and the components are not necessarily separated. That is, a plurality of components may be integrated to be composed of one hardware or software unit, or one component may be distributed to be composed of a plurality of hardware or software units. Accordingly, such integrated or distributed embodiments are included in the scope of the present disclosure, unless otherwise stated.

In the present disclosure, components described in various embodiments are not necessarily essential components, and some may be optional components. Accordingly, an embodiment consisting of a subset of components described in one embodiment is also included in the scope of the present disclosure. In addition, embodiments including other elements in addition to the elements described in various embodiments are also included in the scope of the present disclosure.

Hereinafter, embodiments of the present disclosure will be described with reference to the accompanying drawings.

1 is a block diagram illustrating an apparatus for detecting errors in a mesh model according to an embodiment of the present disclosure.

Referring to FIG. 1, the mesh model error detection apparatus 10 according to an embodiment of the present disclosure includes a mesh confirmation unit 11, a clustering processing unit 13, an error detection unit 15, and an error correction unit (17).

The mesh identification unit 11 may check 3D polygon mesh information. The 3D polygon mesh information may include a data file used for printing a 3D object, and may further include a data file composed of a halfedge data structure.

In the three-dimensional data structure, a face or a mesh formed by an edge connecting a plurality of (eg, three) vertices may be formed, and at least one face may be combined to Polygons can be constructed. Furthermore, a mesh formed through a plurality of vertices and edges can be expressed as a halfedge data structure expressed using a halfedge.

For example, as illustrated in FIG. 2A, based on a halfedge data structure, vertices (V1, V2, ... Vn), polygons (P1, P2, ...) for a given object 200 Pn), mesh (M1, M2, ... Mn), and can be represented by a structure including a half edge (H1, H2, ... Hn). Based on this, the mesh identification unit 11 includes vertices V1, V2, ... Vn, polygons P1, P2, ...Pn, meshes M1, M2, ...Mn, and half corners. Information indicating each of (H1, H2, ...Hn), that is, three-dimensional polygon mesh information including vertex information, polygon information, mesh information, and half-edge information can be confirmed.

Furthermore, each mesh may indicate directionality, and the directionality of the mesh may be expressed using a normal vector. Accordingly, the half edges (H1, H2, ... Hn) of the corresponding mesh may be determined based on the normal vector of each mesh.

For example, as in FIG. 2A, when the first mesh M1 has the directionality of the first direction 211, the first half edge H1 from the second vertex V2 to the first vertex V1. Can exist. In addition, when the second mesh M2 adjacent to the first mesh M1 has directionality in the second direction 212 similar to the first direction 211, the second vertex point from the first vertex V1 There may be a second half edge (H2) directed to (V2).

On the other hand, as shown in FIG. 2B, the first half edge H1 of the first mesh M1 may be configured as a half edge from the second vertex V2 to the first vertex V1 as described above. . In addition, when the second'mesh M2' adjacent to the first mesh M1 has directionality in the third direction 213 opposite to the first direction 211, the second' half edge H2' ) May be composed of a half edge from the second vertex (V2) to the first vertex (V1).

The clustering processing unit 13 may perform clustering on at least one mesh unit in consideration of the above-described half-edge structure. For example, the clustering processing unit 13 may check normal vector information for each mesh unit and set a cluster including at least one mesh.

Furthermore, the clustering processing unit 13 may perform clustering step by step. That is, the clustering processing unit 13 may primarily constitute an initial cluster including at least one mesh, and again constitute a final cluster including at least one initial cluster.

Specifically, the clustering processing unit 13 checks whether the at least one mesh is connected, based on halfedge information for the at least one mesh. In addition, the clustering processing unit 13 may configure at least one initial cluster by reflecting whether the at least one mesh is connected.

For example, referring to FIGS. 2A and 2B described above, when the second mesh M2 adjacent to the first mesh M1 exhibits the same directionality, the edges commonly used for the meshes are respectively oriented in different directions. They may appear as corners, and the corresponding half corners, that is, the first half corner H1 and the second half corner H2 may be classified as a pair. On the other hand, when the second'mesh (M2') adjacent to the first mesh (M1) exhibits opposite directionality, the corners commonly used for the two meshes may appear as half edges pointing in the same direction to each other. The semi-edges, that is, the first half-edge H1 and the second'half-edge H2' are not classified as a pair.

Based on this, the clustering processing unit 13 may configure an initial cluster by combining at least one mesh having half edges classified into one pair.

In addition, the clustering processing unit 13 may configure an initial cluster or a final vector by merging or dividing the initial cluster in consideration of the normal vector of the mesh provided in the initial cluster.

For example, as illustrated in FIG. 3, a plurality of initial clusters 310, 320, 330, ... are present, and each initial cluster 310, 320, 330, ... is provided with a plurality of meshes, respectively. Illustrate what works. In addition, the first initial cluster 310 is provided to be adjacent to the second initial cluster 320 and exemplifies an area where the first initial cluster 310 and the second initial cluster 320 contact as the boundary region 351. . In consideration of this, the clustering processing unit 13 includes at least one mesh 310a, 310b, 310c, 310d contacting the boundary region 351 among the plurality of meshes provided in the first initial cluster 310, and the second initial stage. Among the plurality of meshes provided in the cluster 320, at least one mesh 320a, 320b, 320c, 320d, 320e, 320f contacting the boundary area 351 may be identified. In addition, the clustering processing unit 13 has an average value (hereinafter, referred to as a “first average value”) of the law vector for the meshes 310a, 310b, 310c, and 310d provided in the first initial cluster 310, and the second. The average values (hereinafter referred to as'second average values') of the legal vectors for the meshes 320a, 320b, 320c, 320d, 320e, and 320f provided in the initial cluster 320 are checked, and these (first average values and first 2 average value).

The clustering processing unit 13 compares the identified angle with a predetermined reference value, and configures the final cluster by merging the first initial cluster 310 and the second initial cluster 320, or the initial cluster 310 and The second initial cluster 320 may be divided to determine whether to form a final cluster.

Furthermore, the predetermined reference value may be set based on the distribution of the normal vector of the mesh provided in the initial cluster. Since the predetermined reference value is for setting an initial cluster having a mesh of similar normal vectors to the same cluster, it may be a value that becomes a reference for merging adjacent initial clusters into one final cluster. The predetermined reference value may be set using a minimum value or a maximum value of angles formed by a normal vector of at least one mesh provided in the same cluster. As another example, the predetermined reference value may be set based on an average value and a standard distribution of angles formed by a normal vector of at least one mesh provided in the same cluster.

On the other hand, when the cluster is set by the clustering processing unit 13 as described above, the error detection unit 15 may check whether or not an error occurs for the set cluster unit.

For example, the error detector 15 may check the normal vector of at least one mesh included in the final cluster, and check whether there is an error using the identified normal vector of the at least one mesh. Specifically, the error detection unit 15 selects a reference mesh (hereinafter referred to as a'reference mesh') from among the meshes included in the final cluster, and finalizes it based on the number of meshes contacted by the normal vector of the selected reference mesh. It is possible to check whether the cluster unit is in error. That is, when the normal vector of the reference mesh is extended and the number of meshes that the extended line touches is 0 or even (2n), the error detection unit 15 determines that the final cluster is in the normal direction. On the other hand, if the number of meshes in contact with the line extending from the normal vector of the reference mesh is odd (2n + 1), the error detection unit 15 may determine that the final cluster is an error.

Furthermore, the error detection unit 15 may determine the mesh closest to the central region of the final cluster as a reference mesh. As another example, the error detection unit 15 may calculate the average value of the normal vectors of all the meshes included in the final cluster, and determine a mesh having the normal vector closest to the average value of the calculated normal vectors as the reference mesh.

Additionally, a plurality of initial clusters may exist, and the clustering processing unit 13 may configure a final cluster by merging or dividing the plurality of initial clusters. Preferably, the clustering processor 13 may further perform an operation of sorting a plurality of initial clusters in ascending order based on the number of meshes provided in the initial cluster. In addition, the clustering processing unit 13 may sequentially determine the final clusters for a plurality of initial clusters arranged in ascending order.

Likewise, a plurality of final clusters may exist, and the error detection unit 15 may sequentially determine whether or not the plurality of final clusters are in error. Preferably, the error detection unit 15 may arrange a plurality of final clusters in ascending order based on the number of meshes provided, and sequentially determine whether or not an error occurs for the plurality of final clusters sorted in ascending order.

On the other hand, the error correction unit 17 may correct the error of the cluster determined to exist. The error correction of the cluster may be to correct the inversion of the mesh, and based on this, the error correction unit 17 may check the mesh of the cluster determined to have an error, and change the order of vertices constituting the mesh. . For example, referring to FIG. 4, when there is an error in the second cluster, the error correcting unit 17 includes the vertices constituting at least one mesh (M10, M11, M12, M13, M14) included in the second cluster. By changing the order, the mesh flipping can be corrected.

5 is a flowchart illustrating a sequence of an error detection method of a mesh model according to an embodiment of the present disclosure.

Referring to FIG. 5, the error detection method of the mesh model may be performed by the error detection device of the mesh model described above.

First, in step S501, the mesh model error detection device may check the 3D polygon mesh information. The 3D polygon mesh information may include a data file used for printing a 3D object, and may further include a data file consisting of a halfedge data structure.

For example, as illustrated in FIG. 2A, based on a halfedge data structure, vertices (V1, V2, ... Vn), polygons (P1, P2, ...) for a given object 200 Pn), mesh (M1, M2, ... Mn), and can be represented by a structure including a half edge (H1, H2, ... Hn). Based on this, the error detection device of the mesh model includes vertices (V1, V2, ...Vn), polygons (P1, P2, ...Pn), meshes (M1, M2, ...Mn), and half corners Information indicating each of (H1, H2, ...Hn), that is, three-dimensional polygon mesh information including vertex information, polygon information, mesh information, and half-edge information can be confirmed.

Furthermore, each mesh may indicate directionality, and the directionality of the mesh may be expressed using a normal vector. Accordingly, the half edges (H1, H2, ... Hn) of the corresponding mesh may be determined based on the normal vector of each mesh.

For example, as in FIG. 2A, when the first mesh M1 has the directionality of the first direction 211, the first half edge H1 from the second vertex V2 to the first vertex V1. Can exist. In addition, when the second mesh M2 adjacent to the first mesh M1 has directionality in the second direction 212 similar to the first direction 211, the second vertex point from the first vertex V1 There may be a second half edge (H2) directed to (V2).

On the other hand, as shown in FIG. 2B, the first half edge H1 of the first mesh M1 may be configured as a half edge from the second vertex V2 to the first vertex V1 as described above. . In addition, when the second'mesh M2' adjacent to the first mesh M1 has directionality in the third direction 213 opposite to the first direction 211, the second' half edge H2' ) May be composed of a half edge from the second vertex (V2) to the first vertex (V1).

In step S502, the error detection device of the mesh model may perform clustering on at least one mesh unit in consideration of the above-described half-edge structure. For example, the error detection device of the mesh model may check normal vector information for each mesh unit and set a cluster including at least one mesh in consideration of the normal vector information for each mesh unit.

Furthermore, the error detection device of the mesh model may perform clustering step by step. That is, the error detection device of the mesh model may primarily constitute an initial cluster including at least one mesh, and again constitute a final cluster including at least one initial cluster.

Specifically, the error detection device of the mesh model checks whether or not the at least one mesh is connected, based on halfedge information for the at least one mesh. Then, the error detection device of the mesh model may configure at least one initial cluster by reflecting whether or not the at least one mesh is connected (S502a).

For example, referring to FIGS. 2A and 2B described above, when the second mesh M1 adjacent to the first mesh M1 exhibits the same directionality, the edges commonly used for the meshes are respectively oriented in different directions. They may appear as corners, and the corresponding half corners, that is, the first half corner H1 and the second half corner H2 may be classified as a pair. On the other hand, when the second mesh M2' adjacent to the first mesh M1 exhibits opposite directions, the corners commonly used for the two meshes may appear as half edges pointing in the same direction. The corners, that is, the first half corner H1 and the second half corner H2 are not classified as a pair.

Based on this, the error detection device of the mesh model may configure an initial cluster by combining at least one mesh having semi-edges classified into one pair.

On the other hand, the error detection device of the mesh model may configure the final cluster by identifying or initializing the initial cluster or the normal vector of the mesh provided in the initial cluster, and considering or identifying the identified normal vector (502c). .

For example, as illustrated in FIG. 3, a plurality of initial clusters 310, 320, 330, ... are present, and each initial cluster 310, 320, 330, ... is provided with a plurality of meshes, respectively. Illustrate what works. In addition, the first initial cluster 310 is provided to be adjacent to the second initial cluster 320 and exemplifies an area where the first initial cluster 310 and the second initial cluster 320 contact as the boundary region 351. . In consideration of this, the error detection device of the mesh model includes at least one of the meshes 310a, 310b, and 310c contacting the boundary region 351 among the plurality of meshes provided in the first initial cluster 310 and the second initial cluster ( Of the plurality of meshes provided in 320, at least one mesh 320a, 320b, 320c, 320d contacting the boundary area 351 may be identified. In addition, the error detection device of the mesh model includes an average value (hereinafter referred to as a'first average value') of the law vector for the meshes 310a, 310b, and 310c provided in the first initial cluster 310 and the second initial cluster. Check the average value (hereinafter referred to as the'second average value') of the law vector for the meshes 320a, 320b, 320c, and 320d provided in 320, and the angle between them (the first average value and the second average value) You can check

The error detection device of the mesh model compares the identified angle with a predetermined reference value, to form a final cluster by merging the first initial cluster 310 and the second initial cluster 320, or the initial cluster 310 and The second initial cluster 320 may be divided to determine whether to form a final cluster.

Furthermore, the predetermined reference value may be set based on the distribution of the normal vector of the mesh provided in the initial cluster. Since the predetermined reference value is for setting an initial cluster having a mesh of similar normal vectors to the same cluster, it may be a value that becomes a reference for merging adjacent initial clusters into one final cluster. The predetermined reference value may be set using a minimum value or a maximum value of angles formed by a normal vector of at least one mesh provided in the same cluster. As another example, the predetermined reference value may be set based on an average value and a standard distribution of angles formed by a normal vector of at least one mesh provided in the same cluster.

On the other hand, when the cluster is set as described above, the error detecting device of the mesh model may check whether or not the error is set for the set cluster unit (S504).

For example, the error detection device of the mesh model may check the normal vector of at least one mesh included in the final cluster, and check whether there is an error using the identified normal vector of the at least one mesh. Specifically, the error detection device of the mesh model may select a reference mesh (hereinafter referred to as a'reference mesh') among the meshes included in the final cluster, and extend the normal vector of the selected reference mesh. In addition, it is possible to check whether an error occurs in the final cluster unit based on the number of meshes that the line extending from the normal vector of the reference mesh touches. That is, when the number of meshes that the line extending from the normal vector of the reference mesh touches is 0 or even (2n), the error detecting apparatus of the mesh model determines that the final cluster is in the normal direction. On the other hand, if the number of meshes that the line extending from the normal vector of the reference mesh touches is odd (2n + 1), the error detection device of the mesh model may determine that the final cluster is an error.

Furthermore, the error detection device of the mesh model may determine the mesh closest to the central region of the final cluster as the reference mesh. As another example, the error detection apparatus of the mesh model may calculate the average values of the normal vectors of all the meshes included in the final cluster, and determine the mesh having the normal vector closest to the average value of the calculated normal vectors as the reference mesh.

Additionally, a plurality of initial clusters may exist, and the error detection device of the mesh model may configure a final cluster by merging or dividing the plurality of initial clusters. Preferably, the error detection device of the mesh model may further perform an operation of sorting a plurality of initial clusters in ascending order based on the number of meshes provided in the initial cluster (S502b). Then, the error detection device of the mesh model may sequentially determine the final cluster for the plurality of initial clusters arranged in ascending order in step S502c.

Likewise, a plurality of final clusters may exist, and the error detection device of the mesh model may sequentially determine whether or not there are errors for the plurality of final clusters. Preferably, the error detection device of the mesh model may further perform an operation of sorting a plurality of final clusters in ascending order based on the number of provided meshes (S503). In addition, the error detection device of the mesh model may sequentially determine whether or not there is an error for a plurality of final clusters arranged in ascending order in step S504.

On the other hand, the error detection device of the mesh model may correct the error of the cluster determined to exist the error (S505). For example, the error detection device of the mesh model may correct the mesh inversion by checking the mesh of the cluster determined to exist an error and changing the order of vertices constituting the mesh.

6 is a block diagram illustrating a computing system executing a method and apparatus for detecting errors in a mesh model according to an embodiment of the present disclosure.

Referring to FIG. 6, the computing system 1000 includes at least one processor 1100 connected through a bus 1200, a memory 1300, a user interface input device 1400, a user interface output device 1500, and storage 1600, and the network interface 1700.

The processor 1100 may be a central processing unit (CPU) or a semiconductor device that executes processing for instructions stored in the memory 1300 and/or storage 1600. The memory 1300 and the storage 1600 may include various types of volatile or nonvolatile storage media. For example, the memory 1300 may include read only memory (ROM) and random access memory (RAM).

Accordingly, steps of a method or algorithm described in connection with the embodiments disclosed herein may be directly implemented in hardware, software modules, or a combination of the two, executed by processor 1100. The software modules reside in storage media (ie, memory 1300 and/or storage 1600) such as RAM memory, flash memory, ROM memory, EPROM memory, EEPROM memory, registers, hard disk, removable disk, CD-ROM. You may. An exemplary storage medium is coupled to the processor 1100, which can read information from and write information to the storage medium. Alternatively, the storage medium may be integral with the processor 1100. Processors and storage media may reside within an application specific integrated circuit (ASIC). The ASIC may reside in a user terminal. Alternatively, the processor and storage medium may reside as separate components within the user terminal.

Exemplary methods of the present disclosure are expressed as a series of operations for clarity of description, but are not intended to limit the order in which the steps are performed, and if necessary, each step may be performed simultaneously or in a different order. In order to implement the method according to the present disclosure, the steps illustrated may include other steps in addition, other steps may be included in addition to the remaining steps, or other additional steps may be included in addition to some steps.

Various embodiments of the present disclosure are not intended to list all possible combinations, but are intended to describe representative aspects of the present disclosure, and details described in various embodiments may be applied independently or in combination of two or more.

Further, various embodiments of the present disclosure may be implemented by hardware, firmware, software, or a combination thereof. For implementation by hardware, one or more Application Specific Integrated Circuits (ASICs), Digital Signal Processors (DSPs), Digital Signal Processing Devices (DSPDs), Programmable Logic Devices (PLDs), Field Programmable Gate Arrays (FPGAs), Universal It can be implemented by a processor (general processor), a controller, a microcontroller, a microprocessor.

The scope of the present disclosure includes software or machine-executable instructions (eg, operating systems, applications, firmware, programs, etc.) that cause an operation according to methods of various embodiments to be executed on a device or computer, and such software or Instructions include a non-transitory computer-readable medium that is stored and executable on a device or computer.

Claims (20)

The process of identifying at least one mesh unit based on halfedge information,
Setting at least one cluster including the at least one mesh unit based on the normal vector information of the at least one mesh unit;
Detecting an inversion error for the at least one cluster,
Compensating the at least one mesh unit of the at least one cluster in which an inversion error is detected,
The process of correcting the at least one mesh unit,
By changing the order of vertices constituting the at least one mesh unit included in the at least one cluster in the at least one cluster in which an error exists, all of the mesh units included in the at least one cluster in which the error exists A method for detecting errors in a mesh model, characterized by correcting flipping.
According to claim 1,
The process of setting the at least one cluster,
Checking whether the at least one mesh unit is connected, based on halfedge information for the at least one mesh unit,
And generating at least one initial cluster by reflecting whether or not the at least one mesh unit is connected.
According to claim 2,
The process of setting the at least one cluster,
And constructing a final cluster by merging or classifying the at least one initial cluster based on the normal vector information of the at least one mesh unit included in the at least one initial cluster. Error detection method.
According to claim 2,
The process of setting the at least one cluster,
Confirming the number of the at least one mesh unit included in the at least one initial cluster;
And sorting the at least one initial cluster in ascending order based on the number of the at least one mesh unit.
The method of claim 2 or 4,
The process of setting the at least one cluster,
At least one first normal vector corresponding to the at least one mesh unit included in the first initial cluster, and at least one corresponding to the at least one mesh unit included in a second initial cluster different from the first initial cluster. Confirming the angle between the second normal vectors of
And merging the first and second initial clusters in consideration of an angle between the at least one first normal vector and the at least one second normal vector. .
The method of claim 5,
The process of merging the first and second initial clusters,
Confirming an angle between the at least one first normal vector and the at least one second normal vector;
The angle is compared with a predetermined reference value to merge the second initial cluster with the first initial cluster, wherein the predetermined reference value is the minimum or maximum value of each angle formed by the normal vector of at least one mesh provided in the same cluster. Method of error detection of the mesh model, characterized in that it is set using.
According to claim 3,
The process of setting the at least one cluster,
Checking the number of the at least one mesh unit included in the at least one final cluster;
And sorting the at least one final cluster in ascending order based on the number of the at least one mesh unit.
The method of claim 7,
The process of detecting a flip error for the at least one cluster is:
Confirming the normal vector of the at least one mesh unit provided in the at least one final cluster;
Confirming the number of lines extending from the normal vector and other meshes are in contact;
And determining the flipping error for the at least one final cluster sequentially, considering the identified number.
The method of claim 8,
The process of checking the normal vector of the at least one mesh unit provided in the at least one final cluster may include:
And checking the normal vector of the mesh located in the central region of each of the final clusters.
The method of claim 8,
The process of checking the normal vector of the at least one mesh unit provided in the at least one final cluster may include:
Confirming an average value of a normal vector of the at least one mesh provided in each final cluster;
And checking the normal vector of the mesh corresponding to the average value of the normal vectors of the at least one mesh.
The method of claim 8,
Considering the identified number, the process of determining an inversion error for the at least one final cluster is:
It is determined that the at least one final cluster is normal, corresponding to the number of the lines extending from the normal vector and the mesh contacting each other is 0 or even,
A mesh model error detection method characterized in that it is determined that there is an inversion error in the at least one final cluster in response to an odd number of the line extending from the normal vector and another mesh contacting each other.
A mesh identification unit for identifying at least one mesh unit based on halfedge information;
A clustering processor configured to set at least one cluster including the at least one mesh unit based on the normal vector information of the at least one mesh unit;
An error detection unit for detecting a flip error for the at least one cluster,
And an error correction unit correcting the at least one mesh unit of the at least one cluster in which an inversion error is detected,
The error correction unit,
By changing the order of vertices constituting the at least one mesh unit included in the at least one cluster in the at least one cluster in which an error exists, all of the mesh units included in the at least one cluster in which the error exists An error detection device for a mesh model, characterized by correcting flipping.
The method of claim 12,
The clustering processing unit,
On the basis of the half-edge (halfedge) information for the at least one mesh unit, check whether the at least one mesh unit is connected,
The apparatus for detecting errors in a mesh model, wherein at least one initial cluster is generated by reflecting whether the at least one mesh unit is connected.
The method of claim 13,
The clustering processing unit,
A mesh model error detection apparatus characterized in that a final cluster is formed by merging or classifying the at least one initial cluster based on the normal vector information of the at least one mesh unit included in the at least one initial cluster.
The method of claim 13,
The clustering processing unit,
Check the number of the at least one mesh unit included in the at least one initial cluster,
Based on the number of the at least one mesh unit, the error detection device of the mesh model, characterized in that the at least one initial cluster is arranged in ascending order.
The method of claim 13 or 15,
The clustering processing unit,
At least one first normal vector corresponding to the at least one mesh unit included in the first initial cluster, and at least one corresponding to the at least one mesh unit included in a second initial cluster different from the first initial cluster. Check the angle between the second normal vectors of
And the first and second initial clusters are merged in consideration of an angle between the at least one first normal vector and the at least one second normal vector.
The method of claim 16,
The clustering processing unit,
Confirm at least one angle between the at least one first normal vector and the at least one second normal vector,
The second initial cluster is merged with the first initial cluster by comparing the at least one angle with a predetermined reference value, wherein the predetermined reference value is a minimum value of an angle formed by a normal vector of at least one mesh provided in the same cluster, or A mesh model error detection device, characterized in that it is set using a maximum value.
The method of claim 14,
The clustering processing unit,
Checking the number of the at least one mesh unit included in the at least one final cluster,
Based on the number of the at least one mesh unit, the error detection device of the mesh model, characterized in that the at least one final cluster is arranged in ascending order.
The method of claim 18,
The error detection unit,
Confirming the normal vector of the at least one mesh unit provided in the at least one final cluster,
Check the number of the line extending from the normal vector and another mesh contact,
In consideration of the identified number, the error detection device of the mesh model, characterized in that for sequentially determining the flip error for the at least one final cluster.
The method of claim 19,
The error detection unit,
It is determined that the at least one final cluster is normal, corresponding to the number of the lines extending from the normal vector and the mesh contacting each other is 0 or even,
The apparatus for detecting errors in a mesh model, characterized in that it determines that there is an inversion error in the at least one final cluster in response to an odd number of a line extending from the normal vector and another mesh contacting each other.

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