Polygon data generating apparatus and polygon data generating method

Abstract

To reduce the load of the computer processing of three dimensional data by reducing the number of polygons while maintaining the reproducibility of an object with simple constitution in the case of making the object polygonal.SOLUTION: Generating a candidate polygon that satisfies an allowable distance between the polygon and the three dimensional point group data and an allowable accuracy indicating a maximum allowable size of the polygon with respect to a partial selected region of the three dimensional point group data, and determining the candidate polygon as an optimal polygon when the candidate polygon satisfies a shape constraint condition in which a long / short diameter ratio of a shape of a polygon set in advance is a value of 2 or more or a range of 2 or more; When the shape constraint condition is not satisfied, the next candidate polygon satisfying the shape constraint condition is changed to determine the optimal polygon, and the generation processing of the optimal polygon is repeated for the remaining three dimensional point group data from which the three dimensional point group data targeted by the optimal polygon is deleted.SELECTED DRAWING: Figure 12

Description

[Technical Field]

[0001] The present invention relates to a polygon data generation device and a polygon data generation method that can reduce the number of polygons and the load on computer processing of three-dimensional data when converting a subject including a shape close to a prism or a shape close to a cylinder into polygons, while maintaining the reproducibility of the subject with a simple configuration. [Background technology]

[0002] In designing manufacturing processes, a technology is widely used in which multiple real objects are converted into 3D data and used to simulate process feasibility and more efficient process sequences on a computer, which allows the weight of the objects to be ignored and enables process design to be done more cheaply and quickly without the cost and time required to produce prototypes.

[0003] When this 3D data is displayed on a computer or a display, the image or the point cloud data obtained by processing it places a heavy load on the computer's processing for rendering, which takes a long time. To reduce this load, the image or the point cloud data obtained by processing it is converted into polygons, and these polygons are then rendered, thereby reducing the computer processing load. This polygon data is a common format used to represent 3D shapes on a computer or a display, and is widely used in fields such as measurement data, design data, landscape data, and art (see Patent Documents 1 and 2). [Prior art documents] [Patent documents]

[0004] [Patent Document 1] Japanese Patent Application Laid-Open No. 2005-322002 [Patent Document 2] Japanese Patent Application Laid-Open No. 2000-242812 Summary of the Invention [Problem to be solved by the invention]

[0005] However, when dealing with relatively large 3D data, even if the data is converted into polygons, the load on the computer processing increases and it takes time. In particular, when expressing shapes that are similar to prisms or cylinders using polygons, even though it is known that there is little change in the shape in the longitudinal direction, a large number of polygons are generated in the longitudinal direction, which causes a load on the computer processing.

[0006] The present invention has been made to solve the above-mentioned problems, and aims to provide a polygon data generation device and a polygon data generation method that, when polygonizing a subject including a shape that approximates a prism or a cylinder, can maintain the reproducibility of the subject with a simple configuration, while reducing the number of polygons and reducing the load on the computer processing of three-dimensional data. [Means for solving the problem]

[0007] In order to solve the above-mentioned problems and achieve the object, the polygon data generation device of the present invention has a polygon data generation unit that generates polygon data consisting of a plurality of polygons based on three-dimensional point cloud data obtained by reading point cloud data of a subject into three-dimensional space, and the polygon data generation unit generates a candidate polygon for a selected partial area of ​​the three-dimensional point cloud data that satisfies an allowable distance between the polygon and the three-dimensional point cloud data and an allowable accuracy that indicates the maximum allowable size of the polygon, and if the candidate polygon satisfies a shape constraint condition that specifies a predetermined long-short axis ratio of the polygon shape to a value or range of 2 or more, the candidate polygon is determined to be an optimal polygon, and if the candidate polygon does not satisfy the shape constraint condition, the candidate polygon is changed to the next candidate polygon that satisfies the shape constraint condition and the optimal polygon is determined, and the 3D point cloud data targeted by the optimal polygon is deleted and the generation process of the optimal polygon is repeated for the remaining 3D point cloud data.

[0008] Furthermore, the polygon data generating device according to the present invention is a polygon data generating device having a polygon data generating unit that generates polygon data consisting of a plurality of polygons based on three-dimensional point cloud data obtained by reading point cloud data of a subject into a three-dimensional space, and the polygon data generating unit generates candidate polygons for a selected partial area of ​​the three-dimensional point cloud data that satisfy an allowable distance between the polygon and the three-dimensional point cloud data and an allowable accuracy that indicates a maximum allowable size of the polygon, and if the candidate polygons satisfy a shape constraint condition that specifies a predetermined long-short axis ratio of the shape of the polygon to a value or range of 2 or more, the polygon data generating unit The method is characterized in that an optimal polygon that satisfies the allowable accuracy and the shape constraint conditions is generated for the 3D point cloud data that is the target of the complementary polygon, and the optimal polygon is determined; if the candidate polygon does not satisfy the shape constraint conditions, it is changed to the next candidate polygon that satisfies the shape constraint conditions; an optimal polygon that satisfies the allowable accuracy and the shape constraint conditions is generated for the 3D point cloud data that is the target of the changed candidate polygon, and the optimal polygon is determined; and the 3D point cloud data that was the target of the optimal polygon is deleted, and the generation process of the optimal polygon is repeated for the remaining 3D point cloud data.

[0009] In addition, in the polygon data generating device according to the present invention, the polygons are triangles.

[0010] In addition, the polygon data generation device according to the present invention is characterized in that, in the above invention, the shape constraint condition is the allowable ratio of each side of the triangle corresponding to the ratio of the major axis to the minor axis or the allowable angle of the interior angle of one vertex of the triangle.

[0011] In addition, the polygon data generation device according to the present invention is characterized in that, in the above invention, the change to the next candidate polygon is a change to increase the long / short axis ratio of the candidate polygon, and includes a change to decrease the short axis direction of the candidate polygon, or a change to increase the long axis direction of the candidate polygon.

[0012] In addition, the polygon data generation device according to the present invention is characterized in that, in the above invention, if the number of optimal polygons is not less than the set upper limit number of polygons, the allowable accuracy and / or the shape constraint conditions are changed and polygon data is generated again.

[0013] Furthermore, in the polygon data generating device according to the present invention, the point cloud data is data acquired by a three-dimensional laser scanner or an imaging device.

[0014] Furthermore, the polygon data generation method according to the present invention is a polygon data generation method by a polygon data generation device that generates polygon data consisting of a plurality of polygons based on three-dimensional point cloud data obtained by reading point cloud data of a subject into three-dimensional space, wherein the polygon data generation unit of the polygon data generation device generates a candidate polygon for a selected partial area of ​​the three-dimensional point cloud data that satisfies an allowable distance between the polygon and the three-dimensional point cloud data and an allowable accuracy indicating the maximum allowable size of the polygon, and if the candidate polygon satisfies a shape constraint condition that specifies a predetermined long-short axis ratio of the polygon shape to a value or range of 2 or more, determines the candidate polygon as an optimal polygon, and if the candidate polygon does not satisfy the shape constraint condition, changes to the next candidate polygon that satisfies the shape constraint condition and determines the optimal polygon, and then repeats the generation process of the optimal polygon for the remaining 3D point cloud data after deleting the 3D point cloud data that was the target of the optimal polygon.

[0015] Further, the polygon data generation method according to the present invention is a polygon data generation method by a polygon data generation device that generates polygon data consisting of a plurality of polygons based on three-dimensional point cloud data obtained by reading point cloud data of a subject into a three-dimensional space, wherein a polygon data generation unit of the polygon data generation device generates candidate polygons for a selected partial area of ​​the three-dimensional point cloud data that satisfy an allowable distance between the polygon and the three-dimensional point cloud data and an allowable accuracy indicating a maximum allowable size of the polygon, and the candidate polygons satisfy a shape constraint condition that a long-short axis ratio of the shape of the polygon that is set in advance is a value or range of 2 or more. In this case, an optimal polygon that satisfies the allowable accuracy and the shape constraint conditions is generated for the 3D point cloud data that is the target of the candidate polygon, and the optimal polygon is determined; if the candidate polygon does not satisfy the shape constraint conditions, the candidate polygon is changed to the next candidate polygon that satisfies the shape constraint conditions; an optimal polygon that satisfies the allowable accuracy and the shape constraint conditions is generated for the 3D point cloud data that is the target of the changed candidate polygon, and the optimal polygon is determined; and the 3D point cloud data that was the target of the optimal polygon is deleted, and the generation process of the optimal polygon is repeated for the remaining 3D point cloud data. [Effects of the Invention]

[0016] According to the present invention, when polygonizing a subject that includes a shape that is close to a prism or a shape that is close to a cylinder, it is possible to maintain the reproducibility of the subject with a simple configuration, while reducing the number of polygons and reducing the load on the computer processing of the three-dimensional data. [Brief explanation of the drawings]

[0017] [Figure 1] FIG. 1 is a block diagram showing a schematic configuration of a polygon data generating device according to this embodiment. [Figure 2] FIG. 2 is a diagram illustrating an example of three-dimensional point cloud data. [Figure 3] FIG. 3 is an explanatory diagram for explaining the allowable accuracy. [Figure 4]FIG. 4 is an explanatory diagram for explaining the determination of three-dimensional spatial data to be processed for generating one polygon from three-dimensional point cloud data. [Figure 5] FIG. 5 is an explanatory diagram for explaining the generation of an infinite plane and the identification of target peripheral calculation points. [Figure 6] FIG. 6 is an explanatory diagram for explaining generation of candidate polygons of optimal shapes for target peripheral calculation points in three-dimensional space data. [Figure 7] FIG. 7 is an explanatory diagram for explaining modification of a polygon shape when a candidate polygon does not satisfy the shape constraint conditions. [Figure 8] FIG. 8 is an explanatory diagram illustrating a process for generating an optimum polygon from point cloud data that is the target of a candidate polygon that satisfies the shape constraint conditions. [Figure 9] FIG. 9 is a diagram showing an example of three-dimensional space data after one polygon is generated. [Figure 10] FIG. 10 is a diagram illustrating an example of shape constraint conditions. [Figure 11] FIG. 11 is a diagram showing an example of shape constraints for polygons other than triangles. [Figure 12] FIG. 12 is a flowchart showing the procedure of the polygon data generation process including the optimum polygon generation process by the polygon data generation unit. [Figure 13] FIG. 13 is a flowchart showing the procedure of the polygon data generation process performed by the polygon data generation unit to set a candidate polygon as the optimum polygon. [Figure 14] FIG. 14 is a diagram comparing conventional polygon data with polygon data according to this embodiment. DETAILED DESCRIPTION OF THE INVENTION

[0018] Hereinafter, a polygon data generating device and a polygon data generating method according to the present embodiment will be described with reference to the accompanying drawings.

[0019] <Summary equipment configuration> FIG. 1 is a block diagram showing a schematic configuration of a polygon data generation device 1 according to this embodiment. As shown in FIG. 1, polygon data generation device 1 has an input unit 2, a display unit 3, a storage unit 4, and a control unit 5. The input unit 2 is an input interface such as a mouse or keyboard for inputting various operations. The display unit 3 is an output interface such as a liquid crystal device for displaying and outputting various contents. The storage unit 4 is a storage device such as a hard disk drive or nonvolatile memory, and stores point cloud data D0, allowable accuracy D1, shape constraint conditions D2, etc.

[0020] The control unit 5 is a control unit that controls the entire polygon data generating device 1, and has a polygon data generating unit 6. The control unit 5 stores programs corresponding to the polygon data generating unit 6 in a storage device such as a non-volatile memory or a magnetic disk device, and loads these programs into memory and executes them on the CPU, thereby causing the corresponding processes to be executed.

[0021] The polygon data generation unit 6 generates polygon data consisting of a plurality of polygons based on three-dimensional point cloud data obtained by reading point cloud data D0 of the subject into a three-dimensional space. The point cloud data D0 is data acquired by a three-dimensional laser scanner or an imaging device. The polygon data generation unit 6 generates a candidate polygon for a selected partial area of ​​the 3D point cloud data that satisfies an allowable accuracy D1 indicating the allowable distance between the polygon and the 3D point cloud data and the maximum allowable size of the polygon, and if this candidate polygon satisfies a shape constraint condition D2 that specifies a preset value or range of 2 or more for the long-short axis ratio of the polygon shape, generates an optimal polygon that satisfies the allowable accuracy D1 and the shape constraint condition D2 for the 3D point cloud data that the candidate polygon targets, and determines the optimal polygon, and if the candidate polygon does not satisfy the shape constraint condition D2, changes it to the next candidate polygon that satisfies the shape constraint condition D2, generates an optimal polygon that satisfies the allowable accuracy D1 and the shape constraint condition D2 for the 3D point cloud data that the changed candidate polygon targets, and determines the optimal polygon, and repeats the optimal polygon generation process for the remaining 3D point cloud data after deleting the 3D point cloud data that was the target of this optimal polygon, thereby generating polygon data. It is also possible to not generate an optimum polygon, and to use a candidate polygon that satisfies the shape constraint condition D2 as the optimum polygon.

[0022] <Polygon data generation process> 2 to 9, an overview of the polygon data generation process performed by the polygon data generation unit 6 will be described. As shown in Fig. 2, first, the polygon data generation unit 6 reads point cloud data of the subject into a three-dimensional space and generates three-dimensional point cloud data DD.

[0023] Furthermore, the polygon data generation unit 6 reads the allowable accuracy D1. As shown in FIG. 3, the allowable accuracy is data indicating the allowable distance d between the polygon PL to be generated and the 3D point cloud data P, and the maximum allowable size of the polygon. This maximum allowable size is expressed, for example, by the diameter of a circle C1 circumscribing the polygon. When the value of this allowable accuracy D1 is large, the number of polygons generated will be small and the polygon sizes will be large. On the other hand, when the value of this allowable accuracy D1 is small, the number of polygons generated will be large and the polygon sizes will be small. In this embodiment, a case will be described in which polygon data having a triangular polygon shape is generated.

[0024] The polygon data generation unit 6 also reads shape constraint conditions D2. The shape constraint conditions D2 are values ​​or ranges that require the long-short axis ratio of the polygon shape to be 2 or greater. The long-short axis ratio r is r=w / h, where w is the long axis and h is the short axis. As a result, the generated polygon is a long, thin polygon. Note that the range of the long-short axis ratio of 2 or greater includes cases where the long-short axis ratio is 2 or greater. Furthermore, instead of the shape constraint condition requiring the long-short axis ratio to be 2 or greater, an allowable ratio of each side of the polygon may be used. For example, if the polygon is a triangle, the shape constraint condition may be a ratio of each side of 1:4:5 or 1:4-6:4-6. However, even with these allowable ratios, a long, thin polygon shape equivalent to the long-short axis ratio r of 2 or greater can be identified. In this embodiment, the shape constraint conditions D2 are described as a condition based on an allowable ratio. Specific examples of shape constraint conditions will be described later.

[0025] 4, the polygon data generation unit 6 determines a processing start point P1 for polygonization processing of the 3D point cloud data. This processing start point P1 is a point located at a corner of the 3D point cloud data, but may be any other point. Then, the polygon data generation unit 6 determines a calculation area E1, which is 3D space data DD1 including surrounding calculation points PP1, which are 3D point cloud data within a range corresponding to the allowable accuracy D1 and including the processing start point P1.

[0026] Thereafter, the polygon data generation unit 6 generates an infinite plane SF that best represents the peripheral calculation points PP1, as shown in FIG. 5(a), and identifies target peripheral calculation points that are point cloud data that satisfy the allowable distance d of the allowable accuracy D1 from the infinite plane SF. Then, peripheral calculation points P2 that are point cloud data that do not satisfy the allowable distance d are excluded from the processing target. As a result, three-dimensional space data DD2 that includes only the target peripheral calculation points is generated, as shown in FIG. 5(c). For example, as in the two-dimensional image shown in FIG. 5(b), the target peripheral calculation points are identified by setting point cloud data whose distance from the infinite plane SF is within the allowable distance d as the target peripheral calculation points, and excluding peripheral calculation points P2 whose distance from the infinite plane SF exceeds the allowable distance d from the processing target.

[0027] Thereafter, as shown in Fig. 6, the polygon data generation unit 6 generates a candidate polygon PL2 having an optimal shape for the target peripheral calculation points in the three-dimensional space data DD2. This candidate polygon PL2 is generated using a well-known polygon generation technique for the target peripheral calculation points. Note that in this case, the shape constraint condition D2 is not applied. By not applying this shape constraint condition D2, general-purpose polygon generation software can be used to generate the candidate polygon PL2.

[0028] The polygon data generation unit 6 then determines whether the generated candidate polygon PL2 satisfies the shape constraint conditions. As shown in FIG. 7, if the shape constraint conditions are an allowable ratio (1:a:b) for each side, for example, if the allowable ratio is 1:4 to 6:4 to 6, the candidate polygon PL2 shown in FIG. 7 has a ratio of 1:3:3, which does not satisfy the shape constraint conditions. Therefore, the shape of the candidate polygon PL2 is changed to a shape that satisfies the shape constraint conditions. This shape change can be performed, for example, by shrinking, dividing, or extending. In the case of extension, extension processing is performed so that the maximum allowable size of the allowable accuracy D1 is not exceeded. FIG. 7 shows a candidate polygon PL3 that has been reduced, candidate polygons PL4 and PL5 that have been divided, and candidate polygon PL6 that has been extended. Changes to the long / short axis ratio can be made by increasing the long / short axis ratio, decreasing the short axis, or increasing the long axis.

[0029] 8(a), when an optimal polygon PL10 is generated, which is a candidate polygon that satisfies the shape constraint condition D2 by including a shape change of the candidate polygon PL2, peripheral calculation points that satisfy the allowable accuracy D1 for this optimal polygon PL10 are determined, and the other peripheral calculation points are excluded from processing as excluded peripheral calculation points PP4. The excluded peripheral calculation points PP4 shown in FIG. 8(a) include the excluded peripheral calculation point P2 that does not satisfy the allowable accuracy D1 for the infinite plane SF.

[0030] 8(b), the polygon data generation unit 6 generates an optimal polygon PL20 for the peripheral calculation points PP3 excluding the excluded peripheral calculation points PP4. The optimal polygon PL20 may be similar to the optimal polygon PL10, but in many cases fewer peripheral calculation points are used compared to when generating the optimal polygon PL10, and the polygon is generated as one that more closely resembles the shape of the subject.

[0031] Thereafter, as shown in Figure 9(a), the peripheral calculation point PP3 used when generating the optimal polygon PL20 is deleted from the three-dimensional point cloud data DD, and as shown in Figure 9(b), the above process is repeated for the three-dimensional point cloud data DD' from which the peripheral calculation point PP3 has been deleted, to generate the next optimal polygon in sequence, and finally polygon data for the three-dimensional point cloud data DD is generated.

[0032] It should be noted that polygon data may be generated by using the optimum polygon PL10 as the optimum polygon PL20 as is.

[0033] <Shape constraints> 10A and 10B are diagrams showing examples of shape constraint conditions. Fig. 10A shows an example of a shape constraint condition based on the allowable ratio of each side of a triangle, where the allowable ratio of each side is 1:a:b, for example, 1:4 to 6:4 to 6. By increasing the values ​​of a and b, it is possible to reduce the number of polygons for a subject with a substantially elongated shape, including a shape close to a prismatic column or a shape close to a cylinder.

[0034] FIG. 10(b) shows an example of a shape constraint condition based on the long-short axis ratio. By setting the long-short axis ratio r, which is the ratio of the long axis w to the short axis h, to a value or range of 2 or greater, it is possible to reduce the number of polygons for subjects with approximately elongated shapes, including shapes that resemble prisms or cylinders.

[0035] FIG. 11 shows an example of shape constraint conditions for polygons other than triangles. FIG. 11(a) shows a case where a rectangle is specified by setting the long / short axis ratio r to 10 for a quadrangle. FIG. 11(b) shows a case where a rhombus is specified by setting the long / short axis ratio r to 4 for a quadrangle. When specifying a rhombus, an overlapping condition can be set such that the allowable ratios of each side are the same. Similarly, when specifying a rectangle, an overlapping condition can be set such that the allowable ratios of each opposing side are the same. Furthermore, when specifying a parallelogram, an overlapping condition can be set such that the allowable ratios of each opposing side are the same. Furthermore, the range of the interior angles of each vertex can be set as a shape constraint condition, and can also be set as an overlapping condition for shape constraint conditions.

[0036] 11(c) and 11(d) show examples of long hexagonal polygons set with a long-to-short axis ratio of r. Such shape constraints can also be set for polygons with seven or more sides, and by adding the above-mentioned overlapping conditions, the polygon shape can be further specified.

[0037] <Polygon data generation processing procedure including optimal polygon generation processing> 12 is a flowchart showing the polygon data generation processing procedure including the optimum polygon generation processing by the polygon data generation unit 6. As shown in FIG. 12, first, the polygon data generation unit 6 reads point cloud data of the subject into a three-dimensional space and generates three-dimensional point cloud data (step S101). Then, the allowable accuracy and shape constraint conditions are read (step S102). Then, the polygon data generation unit 6 determines a processing start point for polygonization processing on the three-dimensional point cloud data (step S103). Then, it determines a calculation area including peripheral calculation points for generating one polygon (step S104).

[0038] Thereafter, the polygon data generation unit 6 generates an infinite plane that best represents the peripheral calculation points in this calculation region, identifies target peripheral calculation points that satisfy the allowable accuracy for this infinite plane, and excludes peripheral calculation points that do not satisfy the allowable accuracy from processing targets (step S105).The polygon data generation unit 6 then generates candidate polygons for the target peripheral calculation points (step S106).

[0039] Thereafter, it is determined whether the candidate polygon satisfies the shape constraint conditions (step S107). If the candidate polygon does not satisfy the shape constraint conditions (step S107: No), a candidate polygon whose major axis ratio is changed to the set major axis ratio (shape constraint conditions) is generated (step S108), and the determination process of step S107 is performed.

[0040] On the other hand, if the candidate polygon or the next candidate polygon satisfies the shape constraint conditions (step S107: Yes), point cloud data that is not subject to processing of the candidate polygon is excluded (step S109). After that, an optimal polygon is generated from the point cloud data subject to the candidate polygon (step S110). After that, point cloud data subject to processing of the optimal polygon is deleted from all 3D point cloud data (step S111).

[0041] Thereafter, it is determined whether processing has been completed for all 3D point cloud data (step S112). If processing has not been completed for all 3D point cloud data (step S112: No), the process proceeds to step S104, where processing to generate the next optimal polygon is performed. On the other hand, if processing has been completed for all 3D point cloud data (step S112: Yes), it is further determined whether the number of optimal polygons is equal to or less than the upper limit number of polygons (step S113). If the number of optimal polygons is not equal to or less than the upper limit number of polygons (step S113: No), the allowable accuracy and / or shape constraint conditions are changed (step S114), and the process proceeds to step S103, where polygon data generation processing is performed again. On the other hand, if the number of optimal polygons is equal to or less than the upper limit number of polygons (step S113: Yes), the generated polygon data is output, including being displayed on the display unit 3 and stored in the memory unit 4, and this process ends. Note that the change in allowable accuracy in step S114 means increasing the allowable distance d or the maximum allowable size. Moreover, the change in the shape constraint condition is, for example, to increase the ratio r of the major axis to the minor axis.

[0042] <Polygon data generation process procedure for candidate polygons as optimal polygons> Fig. 13 is a flowchart showing the procedure of polygon data generation processing for setting a candidate polygon as an optimum polygon by the polygon data generation unit 6. In the polygon data generation processing shown in Fig. 12, an optimum polygon is generated from point cloud data of the candidate polygon (step S110), but in the polygon data generation processing shown in Fig. 13, the candidate polygon is directly set as the optimum polygon.

[0043] The flowchart shown in FIG. 13 corresponds to the flowchart shown in FIG. 12, and the processes of steps S101 to S108 and S112 to S115 shown in FIG. 13 and FIG. 12 are the same, but the process of determining the optimal polygon by steps S109' to S111' shown in FIG. 13 is different from the process of determining the optimal polygon by steps S109 to S111 in FIG. 12.

[0044] That is, in the optimum polygon determination process shown in FIG. 13, if the candidate polygon or the next candidate polygon satisfies the shape constraint conditions (step S107: Yes), point cloud data that is not the processing target of the candidate polygon is excluded (step S109'). Note that the process of step S109' may or may not be executed. Then, the candidate polygon is determined as the optimum polygon as is (step S110'). Thereafter, the point cloud data that is the processing target of the optimum polygon is deleted from all 3D point cloud data (step S111').

[0045] Note that a polygon data generation process that uses this candidate polygon as the optimal polygon as is does not involve the process of generating an optimal polygon, thereby shortening the overall processing time for generating polygon data. On the other hand, a polygon data generation process that includes the process of generating an optimal polygon takes processing time because it generates an optimal polygon, but it can generate polygon data with high reproducibility that more closely reflects the actual object shape of the subject.

[0046] <An example of polygon data> FIG. 14 compares conventional polygon data with polygon data according to this embodiment. FIG. 14(a) shows an example of an image display of conventional polygon data, which is an image formed from triangular polygons without any shape constraints. On the other hand, FIG. 14(b) shows an example of an image display of polygon data according to this embodiment, which is an image formed from triangular polygons with shape constraints added. Note that both FIGS. 14(a) and 14(b) are polygon data generated from point cloud data for the same object. While the polygon data in FIG. 14(a) is difficult to understand due to the dense polygon density, the polygon data in FIG. 14(b) clearly has fewer polygons than the polygon data in FIG. 14(a). This is because the shape constraints allowed for a significant reduction in the number of polygons for a columnar object. Generating polygon data with this reduced number of polygons reduces the load on computer image processing while maintaining the reproducibility of the object shape, enabling faster 3D image display.

[0047] The values ​​indicating the shape constraints are not limited to integers, but may be numbers including decimals or fractions.

[0048] Furthermore, by setting the interior angle of one vertex as an ultra-acute angle in the above-mentioned shape constraint condition, a polygon with a large diameter ratio r can be identified. Therefore, an ultra-acute angle corresponding to the diameter ratio r may be set as a shape constraint condition or a superimposed condition of a shape constraint condition.

[0049] Note that the configurations illustrated in the above embodiments are merely functional schematics and do not necessarily have to be physically configured as shown. In other words, the distribution and integration of each device is not limited to that illustrated, and all or part of them can be functionally or physically distributed and integrated in any unit depending on various loads, usage conditions, etc. [Industrial Applicability]

[0050] The polygon data generation device and polygon data generation method of the present invention are useful when polygonizing a subject that includes a shape that is close to a prism or a shape that is close to a cylinder, by reducing the number of polygons and reducing the load on the computer processing of the three-dimensional data while maintaining the reproducibility of the subject with a simple configuration. [Explanation of symbols]

[0051] 1 Polygon data generator 2 Input section 3 Display section 4 Storage section 5. Control section 6 Polygon data generation section C1 Yen d Allowable distance D0 point cloud data D1 tolerance D2 Shape constraints DD 3D point cloud data DD1,DD2 3D spatial data E1 calculation area h Short diameter P 3D point cloud data P1 Processing start point P2, PP1, PP3 surrounding calculation points PL Polygon PL2~PL6 candidate polygons PL10, PL20 optimal polygon PP4 Excluded peripheral calculation points r ratio of major axis to minor axis SF infinite plane w major axis

Claims

1. A polygon data generating device having a polygon data generating unit that generates polygon data consisting of a plurality of polygons based on three-dimensional point cloud data obtained by reading point cloud data of a subject into a three-dimensional space, The polygon data generation unit a polygon data generation device for generating a candidate polygon for a selected partial area of ​​the three-dimensional point cloud data, the candidate polygon satisfying an allowable distance between the polygon and the three-dimensional point cloud data and an allowable accuracy indicating the maximum allowable size of the polygon, determining the candidate polygon as an optimal polygon if the candidate polygon satisfies a shape constraint condition that specifies a predetermined long-short axis ratio of the polygon shape to a value or range of 2 or more, and if the candidate polygon does not satisfy the shape constraint condition, changing to the next candidate polygon that satisfies the shape constraint condition to determine the optimal polygon, and repeating the generation process of the optimal polygon for the remaining three-dimensional point cloud data after deleting the three-dimensional point cloud data that was the target of the optimal polygon.

2. A polygon data generating device having a polygon data generating unit that generates polygon data consisting of a plurality of polygons based on three-dimensional point cloud data obtained by reading point cloud data of a subject into a three-dimensional space, The polygon data generation unit a polygon data generation device for generating, for a selected partial area of ​​the three-dimensional point cloud data, a candidate polygon that satisfies an allowable distance between the polygon and the three-dimensional point cloud data and an allowable accuracy that indicates a maximum allowable size of the polygon, and if the candidate polygon satisfies a shape constraint condition that specifies a predetermined long-short axis ratio of the polygon shape to be a value or range of 2 or more, an optimal polygon that satisfies the allowable accuracy and the shape constraint condition for the three-dimensional point cloud data that is the target of the candidate polygon is generated and determined as the optimal polygon, and if the candidate polygon does not satisfy the shape constraint condition, the device changes to a next candidate polygon that satisfies the shape constraint condition, and generates an optimal polygon that satisfies the allowable accuracy and the shape constraint condition for the three-dimensional point cloud data that is the target of the changed candidate polygon and determines the optimal polygon, and then repeats the generation process of the optimal polygon for the remaining three-dimensional point cloud data after deleting the three-dimensional point cloud data that was the target of the optimal polygon.

3. 3. The polygon data generating device according to claim 1, wherein the polygon is a triangle.

4. 4. The polygon data generating device according to claim 3, wherein the shape constraint is a permissible ratio of each side of a triangle corresponding to the ratio of the major axis to the minor axis or a permissible angle of an interior angle of one vertex of a triangle.

5. The polygon data generating device according to claim 1 or 2, characterized in that the change to the next candidate polygon is a change to increase the long-to-short axis ratio of the candidate polygon, and includes a change to decrease the short axis direction of the candidate polygon or a change to increase the long axis direction of the candidate polygon.

6. 3. The polygon data generating device according to claim 1, wherein if the number of optimal polygons is not equal to or less than the set upper limit number of polygons, the allowable accuracy and / or the shape constraint conditions are changed and polygon data is generated again.

7. 3. The polygon data generating device according to claim 1, wherein the point cloud data is data acquired by a three-dimensional laser scanner or an imaging device.

8. 1. A polygon data generation method using a polygon data generation device that generates polygon data consisting of a plurality of polygons based on three-dimensional point cloud data obtained by reading point cloud data of a subject into a three-dimensional space, comprising: The polygon data generating unit of the polygon data generating device a polygon data generation method for generating a candidate polygon for a selected partial area of ​​the three-dimensional point cloud data, the candidate polygon satisfying an allowable distance between the polygon and the three-dimensional point cloud data and an allowable accuracy indicating the maximum allowable size of the polygon; if the candidate polygon satisfies a shape constraint condition that specifies a predetermined long-short axis ratio of the polygon shape to be a value or range of 2 or more, the candidate polygon is determined to be an optimal polygon; if the candidate polygon does not satisfy the shape constraint condition, the next candidate polygon that satisfies the shape constraint condition is changed to the next candidate polygon that satisfies the shape constraint condition, and the optimal polygon is determined; and the 3D point cloud data targeted by the optimal polygon is deleted, and the generation process of the optimal polygon is repeated for the remaining 3D point cloud data.

9. 1. A polygon data generation method using a polygon data generation device that generates polygon data consisting of a plurality of polygons based on three-dimensional point cloud data obtained by reading point cloud data of a subject into a three-dimensional space, comprising: The polygon data generating unit of the polygon data generating device a polygon data generation method for generating, for a selected partial area of ​​the three-dimensional point cloud data, a candidate polygon that satisfies an allowable distance between the polygon and the three-dimensional point cloud data and an allowable accuracy that indicates a maximum allowable size of the polygon; if the candidate polygon satisfies a shape constraint condition that specifies a predetermined long-short axis ratio of the polygon shape to be a value or range of 2 or more, an optimal polygon that satisfies the allowable accuracy and the shape constraint condition for the three-dimensional point cloud data that is the target of the candidate polygon is generated and determined as the optimal polygon; if the candidate polygon does not satisfy the shape constraint condition, changing to a next candidate polygon that satisfies the shape constraint condition, generating an optimal polygon that satisfies the allowable accuracy and the shape constraint condition for the three-dimensional point cloud data that is the target of the changed candidate polygon and determining the optimal polygon; and repeating the generation process of the optimal polygon for the remaining three-dimensional point cloud data after deleting the three-dimensional point cloud data that was the target of the optimal polygon.

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