Information processing device, data generation method, and program

Abstract

To provide an information processing device capable of suppressing an increase in capacity and preventing a decrease in a shape of an object in generating an image data based on a point group data.SOLUTION: An information processing device comprises: an identification unit which identifies a shape of an object included in point group data; a generation unit which generates first mesh data of a first object whose shape has been identified, and generates second mesh data of a second object whose shape has been identified using a mesh resolution different from the mesh resolution used to generate the first mesh data; and an integration unit which generates three-dimensional data, which is a combination of the first mesh data and the second mesh data.SELECTED DRAWING: Figure 1

Description

[Technical field]

[0001] The present disclosure relates to an information processing device, a data generation method, and a program. [Background technology]

[0002] When inspecting equipment installed in a substation, the equipment may be inspected using images captured by a sensor such as LiDAR (Light Detection And Ranging). The person inspecting the equipment checks the three-dimensional shape of each piece of equipment to determine the normality of the equipment. For example, the person inspecting the equipment checks the three-dimensional shape indicated by mesh data generated based on point cloud data generated using LiDAR or the like to determine the normality of the equipment.

[0003] Patent Document 1 discloses the configuration of a system that controls an unmanned vehicle using image data. The system disclosed in Patent Document 1 generates image data by processing sensor data collected using a LiDAR sensor. Furthermore, the system adjusts the resolution and the like of the image data. An operator monitoring the unmanned vehicle controls the unmanned vehicle by checking the image data with the adjusted resolution and the like, for example, a 3D video. [Prior art documents] [Patent documents]

[0004] [Patent Document 1] Special Publication No. 2018-523331 Summary of the Invention [Problem to be solved by the invention]

[0005] The sensor data collected using the LiDAR sensor corresponds to point cloud data. When generating image data for 3D video from point cloud data using the system disclosed in Patent Document 1, if the shape of an object included in the point cloud data is displayed with high accuracy, the volume of the image data increases. On the other hand, if the accuracy of the shape of an object included in the point cloud data is reduced, the volume of the image data decreases. However, if the accuracy of the shape of an object included in the point cloud data is reduced, the shape of the object cannot be accurately recognized. Thus, when using the system disclosed in Patent Document 1, a problem occurs in that it is difficult to balance the volume of the image data and the accuracy of displaying the shape of the object when generating image data from point cloud data.

[0006] An object of the present disclosure is to provide an information processing device, a data generation method, and a program that can suppress an increase in capacity and prevent a decrease in the accuracy of the shape of an object when generating image data based on point cloud data. [Means for solving the problem]

[0007] An information processing device according to a first aspect of the present disclosure includes an identification unit that identifies a shape of an object included in point cloud data, a generation unit that generates first mesh data of a first object whose shape has been identified and generates second mesh data of a second object whose shape has been identified using a mesh resolution different from the mesh resolution used when generating the first mesh data, and an integration unit that generates three-dimensional data combining the first mesh data and the second mesh data.

[0008] A data generation method according to a second aspect of the present disclosure identifies a shape of an object included in point cloud data, generates first mesh data of a first object whose shape has been identified, generates second mesh data of a second object whose shape has been identified using a mesh resolution different from the mesh resolution used when generating the first mesh data, and generates three-dimensional data combining the first mesh data and the second mesh data.

[0009] A program according to a third aspect of the present disclosure causes a computer to identify a shape of an object included in point cloud data, generate first mesh data of a first object whose shape has been identified, generate second mesh data of a second object whose shape has been identified using a mesh resolution different from the mesh resolution used to generate the first mesh data, and generate three-dimensional data combining the first mesh data and the second mesh data. Effect of the Invention

[0010] The present disclosure makes it possible to provide an information processing device, a data generation method, and a program that can suppress an increase in capacity and prevent a decrease in the accuracy of the shape of an object when generating image data based on point cloud data. [Brief description of the drawings]

[0011] [Figure 1] FIG. 1 is a configuration diagram of an information processing device according to the present disclosure. [Diagram 2] FIG. 2 is a diagram showing a process flow of a data generation method according to the present disclosure. [Diagram 3] FIG. 1 is a configuration diagram of an information processing device according to the present disclosure. [Figure 4] FIG. 2 is a diagram showing management information managed by a management unit according to the present disclosure. [Diagram 5] FIG. 2 is a diagram showing three-dimensional data displayed by a display unit according to the present disclosure. [Figure 6] FIG. 1 is a diagram showing a flow of a display process for three-dimensional data according to the present disclosure. [Figure 7] FIG. 2 is a diagram showing management information managed by a management unit according to the present disclosure. [Figure 8] FIG. 1 is a diagram showing a flow of a display process for three-dimensional data according to the present disclosure. [Figure 9] FIG. 1 is a configuration diagram of an information processing device according to the present disclosure. DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS

[0012] (Embodiment 1) An example of the configuration of the information processing device 10 will be described below with reference to Fig. 1. The information processing device 10 may be a computer device that operates when a processor executes a program stored in a memory.

[0013] The information processing device 10 has an identification unit 11, a generation unit 12, and an integration unit 13. The identification unit 11, the generation unit 12, and the integration unit 13 may be software or modules that are executed by a processor executing a program stored in a memory. Alternatively, the identification unit 11, the generation unit 12, and the integration unit 13 may be hardware such as a circuit or a chip.

[0014] The identification unit 11 identifies the shape of an object included in the point cloud data. The point cloud data is a set of points having three-dimensional information. The point cloud data may be generated by, for example, a sensor that measures distance or an imaging device. The sensor that measures distance may be, for example, a sensor that measures the distance from the sensor to the object using LiDAR (Light Detection And Ranging). The point cloud data may be generated using distance information measured by a sensor that measures distance and position information measured using a GPS (Global Positioning System).

[0015] The various sensors that generate the point cloud data may be mounted on the information processing device 10, or may be connected to the information processing device 10 via a network. The recognition unit 11 acquires the point cloud data generated by the various sensors.

[0016] Alternatively, a user may input point cloud data generated by various sensors as offline data to the information processing device 10. Furthermore, the identification unit 11 may generate point cloud data using data measured by various sensors.

[0017] For example, the point cloud data may be generated using software that generates three-dimensional information using a plurality of two-dimensional image data.

[0018] The objects included in the point cloud data may be, for example, objects fixedly installed on the ground or floor, or may be movable objects. The objects may also include humans, non-human animals, plants, etc. Specifically, the objects may be devices, parts, etc. located in a substation.

[0019] Identifying the shape of an object may mean identifying points that indicate the contour of the object. Furthermore, identifying the shape of an object may mean identifying points that indicate the surface of the object in addition to the contour of the object.

[0020] The generating unit 12 generates first mesh data of the first object whose shape has been identified. The mesh data is data that indicates a three-dimensional shape by using points included in the point cloud data as vertices and triangular or quadrangular surfaces formed by combining the vertices as the surface of the object. The mesh data may be referred to as, for example, a mesh model, polygon data, object data, etc. The mesh data, mesh model, polygon data, object data, etc. are data that indicate the three-dimensional shape of an object.

[0021] The generating unit 12 generates the first mesh data using the mesh resolution. The mesh resolution may be, for example, a parameter that specifies the accuracy of showing the shape of an object. For example, as the mesh resolution increases, the shape of the object shown by the mesh data becomes clearer. The clearer shape of the object may mean that the boundary between the object and the background or other objects is displayed clearly. Furthermore, the clearer shape of the object may mean that the actual object is highly reproduced. The boundary between the object and the background or other objects may be referred to as the edge of the object.

[0022] As the mesh resolution decreases, the shape of the object represented by the mesh data becomes less clear. The shape of the object becomes less clear may mean that the boundary between the object and the background or other objects becomes ambiguous or blurred. The shape of the object becomes less clear may mean that the lines indicating the boundaries of the object become unclear. Alternatively, the shape of the object becomes less clear may mean that the degree of reproduction of the actual object is low.

[0023] The generating unit 12 further generates second mesh data of the second object using a mesh resolution different from the mesh resolution used when generating the first mesh data. That is, the generating unit 12 may determine the mesh resolution used when generating mesh data for each object whose shape has been identified. Alternatively, the generating unit 12 may determine the mesh resolution used when generating mesh data for each group including several objects. For example, objects of the same type may be included in one group. Or, multiple types of objects constituting one device may be included in one group.

[0024] The integration unit 13 generates three-dimensional data by combining the first mesh data and the second mesh data. The three-dimensional data may be image data indicating a three-dimensional shape. The three-dimensional data includes a plurality of mesh data generated using different mesh resolutions. That is, the three-dimensional data may include mesh data generated using a mesh resolution higher than a predetermined standard and mesh data generated using a mesh resolution lower than the predetermined standard.

[0025] Next, the process flow of the data generation method executed in the information processing device 10 will be described with reference to FIG. 2. First, the identification unit 11 identifies the shape of an object included in the point cloud data (S11). Next, the generation unit 12 generates first mesh data of a first object whose shape has been identified (S12). Next, the generation unit 12 generates second mesh data of a second object whose shape has been identified using a mesh resolution different from the mesh resolution used when generating the first mesh data (S13). Next, the integration unit 13 generates three-dimensional data by combining the first mesh data and the second mesh data (S14).

[0026] As described above, the information processing device 10 generates three-dimensional data including mesh data of multiple objects using different mesh resolutions. The volume of three-dimensional data including mesh data generated using high and low resolutions is smaller than the volume of three-dimensional data including mesh data generated using high resolution for all objects. Here, the high and low resolutions may be higher or lower than a predetermined reference resolution. Alternatively, the high and low resolutions may be higher or lower than the other resolution.

[0027] In addition, the shape of an object represented by mesh data generated using a high resolution is more clearly shown than that of an object represented by mesh data generated using a low resolution. Therefore, for example, when a user or the like determines the normality of an object based on the shape of the object, the normality of the object represented by mesh data generated using a high resolution can be appropriately determined.

[0028] (Embodiment 2) Next, a configuration example of the information processing device 20 will be described with reference to Fig. 3. The information processing device 20 has a configuration in which a management unit 21 and a display unit 22 are added to the information processing device 10. The components constituting the information processing device 20 may be software in which processing is performed by a processor executing a program stored in a memory. Alternatively, the components constituting the information processing device 20 may be hardware such as a circuit or a chip. In the following, a description of functions or operations similar to those of the information processing device 10 will be omitted.

[0029] The identification unit 11 identifies the shape of an object contained in the point cloud data from the point cloud data using a learning model that has learned the shape of the object. Identifying the shape of the object may be rephrased as extracting, outputting, determining, or the like the shape of the object.

[0030] The learning model may use point cloud data indicating the shape of the object as training data to machine-learn the shape of the object. The learning model may use point cloud data generated by a sensor using LiDAR as input, and output point cloud data indicating the shape of the object. When the learning model has learned the shapes of multiple objects using point cloud data indicating the shapes of multiple objects as training data, the learning model may output multiple point cloud data indicating the shapes of multiple objects included in the point cloud data. The learning model may learn, for example, the shape of a substation installed in a substation.

[0031] Furthermore, the learning model may use the object shape and object name as training data and perform machine learning to learn the object shape and object name. In this case, the learning model may use point cloud data as input and output the object shape and object name.

[0032] The identification unit 11 may identify the shape of an object using semantic segmentation as the identification of the shape of an object using a learning model.

[0033] The management unit 21 manages parameters that define mesh resolution associated with objects included in the point cloud data. Here, the management information managed by the management unit 21 will be explained with reference to Fig. 4. Fig. 4 shows that object names and parameters indicating resolution levels are managed in advance in association with each other.

[0034] The object name is the name of an object included in the point cloud data. The object name may be the name of an object identified by the identification unit 11. In Fig. 4, a substation facility installed in a substation is shown as the object name.

[0035] In FIG. 4, "HIGH" and "LOW" are shown as resolution levels. The resolution level "HIGH" indicates a high resolution. Furthermore, the resolution level "LOW" indicates a low resolution level. The resolution level may be shown as "HIGH" when the resolution is higher than a predetermined standard, and may be shown as "LOW" when the resolution is lower than the predetermined standard. Alternatively, when there are two different resolution levels, the higher resolution level may be shown as "HIGH" and the lower resolution level may be shown as "LOW". Furthermore, although two resolution levels are shown in FIG. 4, three or more resolution levels may be defined.

[0036] The association between the object name and the resolution level may be performed, for example, based on information input by an operator of the information processing device 20. Specifically, the operator may input the mesh resolution used when generating mesh data of each object to the information processing device 20. The operator may set the resolution level for each object while checking the object name displayed on the display, for example.

[0037] Alternatively, the management unit 21 may acquire information that associates object names with resolution levels stored in another information processing device, etc., via a network, or may acquire information that associates object names with resolution levels offline.

[0038] The generation unit 12 specifies the mesh resolution associated with the object identified by the identification unit 11, using management information managed by the management unit 21. The generation unit 12 uses the mesh resolution previously associated with the object identified by the identification unit 11 to generate mesh data of the object.

[0039] The generation unit 12 may generate mesh data using a predetermined mesh resolution when an object identified by the identification unit 11 is not managed by the management unit 21. The predetermined mesh resolution may be a mesh resolution indicated as "HIGH", a mesh resolution indicated as "LOW", or a mesh resolution at a level between "HIGH" and "LOW".

[0040] Here, mesh resolution will be described in detail. Mesh resolution may, for example, define the distance between two of a plurality of points used in mesh data. The shorter the distance between two points, the greater the number of points used in mesh data. The longer the distance between two points, the fewer the number of points used in mesh data. In other words, the shorter the distance between two points, the greater the data capacity of the mesh data. The longer the distance between two points, the smaller the data capacity of the mesh data. In other words, the shorter the distance between two points, the greater the accuracy with which the mesh data represents an object. The longer the distance between two points, the less accurate the accuracy with which the mesh data represents an object.

[0041] Alternatively, the mesh resolution may set an upper limit on the number of points used in the mesh data within an area of ​​a given size, i.e. the mesh resolution may set an upper limit on the density of points within an area of ​​a given size.

[0042] Returning to Fig. 3, the display unit 22 displays the three-dimensional data in which the mesh data of each object generated using different levels of mesh resolution are integrated. The display unit 22 may be, for example, a display used integrally with the information processing device 20, or may be a display connected to the information processing device 20 via a cable, a network, or the like. When the integration unit 13 generates three-dimensional data by integrating the mesh data of each object, it outputs the three-dimensional data to the display unit 22. The display unit 22 displays the received three-dimensional data.

[0043] 5 shows an example of three-dimensional data displayed by the display unit 22. It is assumed that mesh data of objects is shown in areas A, B, and C surrounded by dotted lines. The mesh resolutions used to generate the mesh data of the objects included in areas A to C may be different from each other. For example, the mesh data of areas A and B may be generated using the same mesh resolution, and the mesh data of area C may be generated using a mesh resolution different from that of areas A and B. The dotted lines representing areas A to C are used for convenience in easily explaining the positions of the areas, and the dotted lines may not be displayed in the actual three-dimensional data displayed on the display unit 22, and only the mesh data of the objects included in the dotted lines may be displayed.

[0044] Display unit 22 may display the mesh data of each object included in areas A to C without distinguishing between different mesh resolutions. In this case, a user visually viewing the three-dimensional data displayed on display unit 22 may not be able to recognize the difference in mesh resolution used when generating each object in areas A to C, or may be able to recognize the difference in mesh resolution from the clarity of the boundaries of the objects.

[0045] Alternatively, the display unit 22 may display the mesh data of each object included in the areas A to C with distinction between different mesh resolutions. For example, the display unit 22 may make the color of the mesh data of objects generated using the same mesh resolution the same. That is, the display unit 22 may change the color of the mesh data generated for each mesh resolution. For example, the display unit 22 may make the mesh data of objects generated with a mesh resolution having a resolution level of "HIGH" red, and the mesh data of objects generated with a mesh resolution having a resolution level of "LOW" blue. The types of colors are merely examples, and the colors used are not limited thereto.

[0046] Alternatively, the display unit 22 may display a dotted line surrounding an object as shown in FIG. 5. Furthermore, the display unit 22 may change the color of the dotted line surrounding an object for each mesh resolution used in generating the mesh data of the object. For example, the display unit 22 may display a dotted line surrounding mesh data of an object generated with a mesh resolution having a resolution level of "HIGH" in red. Furthermore, the display unit 22 may display a dotted line surrounding mesh data of an object generated with a mesh resolution having a resolution level of "LOW" in blue. The types of colors are merely examples, and the colors used are not limited thereto. Furthermore, the dotted line surrounding mesh data of an object may be a solid line.

[0047] Alternatively, the display unit 22 may change the type or thickness of the line surrounding the object for each mesh resolution used when generating the mesh data of the object.

[0048] The integrating unit 13 may generate three-dimensional data that distinguishes the mesh data of the object according to differences in mesh resolution. Alternatively, when the display unit 22 displays the three-dimensional data received from the integrating unit 13, the display unit 22 may display the mesh data of the object so as to distinguish the mesh data according to differences in mesh resolution.

[0049] Next, the flow of the 3D data display process executed in the information processing device 20 will be described with reference to FIG. 6. First, the identification unit 11 identifies the shape of an object included in the point cloud data (S21). Next, the generation unit 12 selects an object for which mesh data is to be generated (S22). The point cloud data includes multiple objects. Therefore, the identification unit 11 identifies or extracts the multiple objects. The generation unit 12 selects one object from the multiple objects identified or extracted by the identification unit 11.

[0050] Next, the generation unit 12 generates mesh data of the selected object using a mesh resolution of the resolution level associated with the selected object (S23). The generation unit 12 determines the resolution level associated with the selected object using the management unit 21. The generation unit 12 may temporarily record the generated mesh data in a memory or the like in the information processing device 20.

[0051] Next, the generation unit 12 determines whether all objects have been selected (S24). For example, the identification unit 11 may generate information indicating a list of all identified objects, for example, list information. The generation unit 12 may add information indicating whether each object included in the list information has been selected in step S22. For example, the generation unit 12 may set a checked flag to an object that has been selected in step S22 in the list information. The generation unit 12 may determine whether all objects have been selected by determining whether selected information has been added to each object.

[0052] When the generation unit 12 determines in step S24 that not all objects have been selected, the generation unit 12 repeats the processes from step S22 onward. When not all objects have been selected, this means that there are objects that have not been selected. When repeating the process of step S22, the generation unit 12 selects an object that has not been selected.

[0053] If the generation unit 12 determines in step S24 that all objects have been selected, the integration unit 13 integrates the mesh data of all objects (S25). Integrating the mesh data of all objects means combining the mesh data of all objects. The integration unit 13 may generate three-dimensional data by combining the mesh data so as to distinguish between differences in the mesh resolution used when combining the mesh data. Next, the display unit 22 displays the three-dimensional data (S26).

[0054] As described above, the information processing device 20 associates a resolution level with each object included in the point cloud data in advance. For example, when a manager of a substation monitors substation equipment in the substation using three-dimensional image data, the management unit 21 may associate a high resolution level with equipment to be monitored, equipment whose detailed shape needs to be confirmed, and the like, and manage them. In addition, the management unit 21 may associate a low resolution level with equipment not to be monitored, and manage them. This makes it possible to suppress an increase in the data volume of three-dimensional data compared to a case where mesh data of all objects is generated using a mesh resolution of a high resolution level. On the other hand, since the mesh data of the object to be monitored is generated using a mesh resolution of a high resolution level, the display accuracy of the object's shape can be improved.

[0055] Furthermore, the information processing device 20 may display mesh data generated using mesh resolutions of different resolution levels in a differentiated manner, thereby enabling the manager or the like to easily identify the equipment to be monitored.

[0056] (Embodiment 3) Next, the management information managed by the management unit 21 will be described with reference to Fig. 7. Fig. 7 shows that the management information includes objects designated by an administrator or the like as facilities to be monitored. Furthermore, Fig. 7 shows that the management information includes mesh resolutions of "HIGH" and "LOW" as resolution levels. Although two resolution levels are shown in Fig. 7, three or more resolution levels may be defined.

[0057] If the selected object is included in the management information, the generation unit 12 generates mesh data for the selected object using a mesh resolution with a resolution level of "HIGH". If the selected object is not included in the management information, the generation unit 12 generates mesh data for the selected object using a mesh resolution with a resolution level of "LOW".

[0058] Next, the flow of the three-dimensional data display process executed in the information processing device 20 will be described with reference to Fig. 8. Steps S31 and S32 are similar to steps S21 and S22 in Fig. 6, and therefore detailed description thereof will be omitted.

[0059] Next, the generation unit 12 determines whether or not the object selected in step S32 is a pre-specified object (S33). The generation unit 12 may determine that the selected object is a pre-defined object when the management information managed by the management unit 21 includes the object selected in step S32. The generation unit 12 may also determine that the selected object is not a pre-defined object when the management information managed by the management unit 21 does not include the object selected in step S32.

[0060] If the generation unit 12 determines that the object selected in step S32 is a pre-specified object, it generates mesh data for the selected object using the mesh resolution indicated as "HIGH" in the management unit 21 (S34).

[0061] If the generation unit 12 determines in step S32 that the object selected is not a pre-specified object, it generates mesh data for the selected object using the mesh resolution indicated as "LOW" in the management unit 21 (S35).

[0062] The process from step S36 onwards is similar to the process from step S24 onwards in FIG. 6, and therefore a detailed description thereof will be omitted.

[0063] As described above, the management unit 21 manages the specified object and the resolution level as separate information without associating them with each other. This allows for more flexible management of the object and the resolution level used when generating mesh data for the object, compared to when the object and the resolution level are managed in association with each other. For example, when changing the resolution level used when generating mesh data for a specified object, the parameters specified in the resolution level can be changed. Or, when changing an object that uses a mesh resolution with a resolution level of "HIGH", the object specified as the specified object can be changed. In this way, when changing the combination of an object and a resolution level, it is not necessary to change the resolution level for each object.

[0064] In addition, in the third embodiment, when generating mesh data of an object designated as a facility to be monitored, the mesh resolution indicated as "HIGH" is used. Furthermore, when generating mesh data of an object that is not designated, the mesh resolution indicated as "LOW" is used. The association between objects and mesh resolutions is not limited to this.

[0065] For example, the association between the object and the mesh resolution may be determined using a learning model. For example, the learning model may be a model trained by machine learning to determine an appropriate mesh resolution depending on the complexity of the object's structure. Specifically, the learning model may associate a high-resolution mesh resolution with an object having a complex structure, and may associate a low-resolution mesh resolution with an object having a simple structure.

[0066] The generation unit 12 may input the object whose shape has been identified by the identification unit 11 into the learning model, thereby acquiring parameters of mesh resolution associated with the input object. By associating the object with the mesh resolution in this way, the accuracy of the shape of the object represented by the mesh data can be improved by using a high mesh resolution for an object with a complex structure. On the other hand, the volume of mesh data can be reduced by using a low mesh resolution for an object with a simple structure.

[0067] Fig. 9 is a block diagram showing a configuration example of the information processing device 10 and the information processing device 20 (hereinafter referred to as the information processing device 10, etc.) described in the above-mentioned embodiment. Referring to Fig. 9, the information processing device 10, etc. includes a network interface 1201, a processor 1202, and a memory 1203. The network interface 1201 may be used to communicate with a network node. The network interface 1201 may include, for example, a network interface card (NIC) conforming to the IEEE 802.3 series. IEEE stands for Institute of Electrical and Electronics Engineers.

[0068] The processor 1202 reads out and executes software (computer programs) from the memory 1203 to perform the processing of the information processing device 10 and the like described using the flowcharts in the above-mentioned embodiments. The processor 1202 may be, for example, a microprocessor, an MPU, or a CPU. The processor 1202 may include multiple processors.

[0069] The memory 1203 is configured by a combination of volatile memory and non-volatile memory. The memory 1203 may include storage located away from the processor 1202. In this case, the processor 1202 may access the memory 1203 via an I / O (Input / Output) interface (not shown).

[0070] 9, the memory 1203 is used to store a group of software modules. The processor 1202 reads out and executes the group of software modules from the memory 1203, thereby performing the processing of the information processing device 10 and the like described in the above-mentioned embodiment.

[0071] As described with reference to FIG. 9, each of the processors of the information processing device 10 in the above-described embodiment executes one or more programs including a group of instructions for causing a computer to execute the algorithm described with reference to the drawings.

[0072] In the above examples, the program includes instructions (or software code) that, when loaded into a computer, cause the computer to perform one or more functions described in the embodiments. The program may be stored in a non-transitory computer-readable medium or a tangible storage medium. By way of example and not limitation, the computer-readable medium or tangible storage medium includes random-access memory (RAM), read-only memory (ROM), flash memory, solid-state drive (SSD) or other memory technology, CD-ROM, digital versatile disc (DVD), Blu-ray® disk or other optical disk storage, magnetic cassette, magnetic tape, magnetic disk storage or other magnetic storage device. The program may be transmitted on a transitory computer-readable medium or communication medium. By way of example and not limitation, the transitory computer-readable medium or communication medium includes electrical, optical, acoustic, or other forms of propagated signals.

[0073] Although the present disclosure has been described above with reference to the embodiments, the present disclosure is not limited to the above-mentioned embodiments. Various modifications that can be understood by a person skilled in the art can be made to the configuration and details of the present disclosure within the scope of the present disclosure. Furthermore, each embodiment can be appropriately combined with other embodiments.

[0074] Each drawing is merely an example for describing one or more embodiments. Each drawing may relate not only to one particular embodiment, but to one or more other embodiments. As can be understood by those skilled in the art, various features or steps described with reference to any one drawing may be combined with features or steps shown in one or more other drawings to create an embodiment not explicitly shown or described, for example. Not all features or steps shown in any one drawing are necessary to describe an exemplary embodiment, and some features or steps may be omitted. The order of steps described in any drawing may be changed as appropriate.

[0075] A part or all of the above-described embodiments can be described as, but is not limited to, the following supplementary notes. (Appendix 1) An identification unit for identifying a shape of an object included in the point cloud data; a generation unit that generates first mesh data of a first object whose shape has been identified, and generates second mesh data of a second object whose shape has been identified, using a mesh resolution different from a mesh resolution used when generating the first mesh data; an integration unit that generates three-dimensional data by combining the first mesh data and the second mesh data. (Appendix 2) The generation unit is 2. The information processing device according to claim 1, further comprising: generating mesh data for each object using a mesh resolution previously associated with each object included in the point cloud data. (Appendix 3) The generation unit is 3. The information processing device according to claim 1, wherein if a mesh resolution is not associated with the object whose shape has been identified, mesh data is generated using a predetermined resolution. (Appendix 4) 4. The information processing device according to claim 1, further comprising a management unit that manages parameters that define a mesh resolution used for each of the objects. (Appendix 5) The mesh resolution is 5. The information processing device according to claim 1, further comprising: a mesh data unit that defines a distance between two points among a plurality of points used in the mesh data. (Appendix 6) The mesh resolution is 6. The information processing device according to claim 1, further comprising: a processor configured to generate mesh data for a particular region based on the determined upper limit of the number of points used to generate mesh data for the particular region; (Appendix 7) 7. The information processing device according to claim 1, further comprising a display unit that displays the three-dimensional data indicating that a resolution used for the first mesh data is different from a resolution used for the second mesh data. (Appendix 8) The identification unit is 8. The information processing device according to claim 1, further comprising: a learning model that has learned the shape of the object, and identifies the shape of the object contained in the point cloud data from the point cloud data. (Appendix 9) The learning model is 9. The information processing device according to claim 8, wherein the shape of the object is learned using point cloud data indicating the shape of the object as training data. (Appendix 10) Identify the shapes of objects contained in point cloud data, generating first mesh data of the first object whose shape has been identified; generating second mesh data of the second object whose shape has been identified using a mesh resolution different from the mesh resolution used to generate the first mesh data; generating three-dimensional data by combining the first mesh data and the second mesh data; (Appendix 11) When generating the first and second mesh data, 11. The data generation method according to claim 10, further comprising generating mesh data for each object using a mesh resolution associated with each object included in the point cloud data. (Appendix 12) When generating the first and second mesh data, 12. The data generation method according to claim 10 or 11, wherein if a mesh resolution is not associated with the object whose shape has been identified, mesh data is generated using a predetermined resolution. (Appendix 13) 13. A data generation method according to any one of appendices 10 to 12, wherein parameters defining the mesh resolution used for each of the objects are managed in a management unit. (Appendix 14) The mesh resolution is 14. A data generation method according to any one of appendixes 10 to 13, further comprising specifying a distance between two points among a plurality of points used in the mesh data. (Appendix 15) The mesh resolution is 15. The data generation method of any one of claims 10 to 14, further comprising defining an upper limit on the number of points used when generating mesh data within a particular region. (Appendix 16) After generating the three-dimensional data, 16. The data generation method according to any one of claims 10 to 15, further comprising displaying the three-dimensional data indicating that a resolution used for the first mesh data is different from a resolution used for the second mesh data. (Appendix 17) When identifying the shape of an object included in the point cloud data, A data generation method according to any one of appendix 10 to 16, further comprising identifying a shape of the object contained in the point cloud data from the point cloud data using a learning model that has already learned the shape of the object. (Appendix 18) The learning model is 18. The data generation method according to claim 17, further comprising learning the shape of the object using point cloud data indicating the shape of the object as training data. (Appendix 19) Identify the shapes of objects contained in point cloud data, generating first mesh data of the first object whose shape has been identified; generating second mesh data of the second object whose shape has been identified using a mesh resolution different from the mesh resolution used to generate the first mesh data; generating three-dimensional data by combining the first mesh data and the second mesh data. (Appendix 20) When generating the first and second mesh data, 20. The program of claim 19, which causes a computer to generate mesh data for each object using a mesh resolution associated with each object included in the point cloud data. (Appendix 21) When generating the first and second mesh data, 21. The program according to claim 19 or 20, which causes a computer to generate mesh data using a predetermined resolution if a mesh resolution is not associated with the object whose shape has been identified. (Appendix 22) 22. The program according to any one of appendices 19 to 21, wherein parameters defining the mesh resolution used for each of the objects are managed in a management unit. (Appendix 23) The mesh resolution is 23. A program according to any one of appendixes 19 to 22, which specifies a distance between two points among a plurality of points used in mesh data. (Appendix 24) The mesh resolution is 24. The program of claim 19, further comprising: determining an upper limit on the number of points used when generating mesh data within a particular region. (Appendix 25) After generating the three-dimensional data, 25. The program of claim 19, further comprising: displaying the three-dimensional data indicating that a resolution used for the first mesh data is different from a resolution used for the second mesh data. (Appendix 26) When identifying the shape of an object included in the point cloud data, 26. The program according to any one of appendices 19 to 25, which causes a computer to execute the steps of: identifying a shape of the object contained in the point cloud data from the point cloud data using a learning model that has learned the shape of the object. (Appendix 27) The learning model is The program described in Appendix 17, further comprising: learning the shape of the object using point cloud data indicating the shape of the object as training data. [Explanation of symbols]

[0076] 10. Information processing device 11 Identification section 12 Generation part 13 Integration Department 20 Information processing device 21 Management Department 22 Display section

Claims

1. An identification unit for identifying a shape of an object included in the point cloud data; a generation unit that generates first mesh data of a first object whose shape has been identified, and generates second mesh data of a second object whose shape has been identified, using a mesh resolution different from a mesh resolution used when generating the first mesh data; an integration unit that generates three-dimensional data by combining the first mesh data and the second mesh data; a display unit that displays the first mesh data and the second mesh data, which are generated using different mesh resolutions, in different colors when the three-dimensional data is displayed.

2. The generation unit is The information processing apparatus according to claim 1 , further comprising: generating mesh data for each object using a mesh resolution associated with each object included in the point cloud data.

3. The generation unit is The information processing apparatus according to claim 2 , wherein if a mesh resolution is not associated with the object whose shape has been identified, mesh data is generated using a predetermined resolution.

4. The information processing apparatus according to claim 1 , further comprising a management unit that manages parameters that define a mesh resolution used for each of the objects.

5. The mesh resolution is The information processing apparatus according to claim 1 , further comprising: a distance between two of a plurality of points used in mesh data.

6. The mesh resolution is The information processing apparatus according to claim 1 , further comprising: a setting for an upper limit number of points to be used when generating mesh data within a specific region.

7. The display unit, The information processing apparatus according to claim 1 , further comprising: displaying the three-dimensional data indicating that a resolution used for the first mesh data is different from a resolution used for the second mesh data.

8. The identification unit is The information processing device according to claim 1 , further comprising: a learning model that has learned the shape of the object, and that identifies the shape of the object contained in the point cloud data from the point cloud data using the learning model.

9. Identify the shapes of objects contained in point cloud data, generating first mesh data of the first object whose shape has been identified; generating second mesh data of the second object whose shape has been identified using a mesh resolution different from the mesh resolution used to generate the first mesh data; generating three-dimensional data by combining the first mesh data and the second mesh data; The data generating method, when displaying the three-dimensional data, displays the first mesh data and the second mesh data generated using different mesh resolutions in different colors.

10. Identify the shapes of objects contained in point cloud data, generating first mesh data of the first object whose shape has been identified; generating second mesh data of the second object whose shape has been identified using a mesh resolution different from the mesh resolution used to generate the first mesh data; generating three-dimensional data by combining the first mesh data and the second mesh data; A program that causes a computer to execute the following steps: when displaying the three-dimensional data, the first mesh data and the second mesh data that are generated using different mesh resolutions are displayed in different colors.

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