Point cloud LOD generation method, system and device based on three-dimensional grid and storage medium

Abstract

The invention discloses a point cloud LOD generation method, system and device based on a three-dimensional grid and a storage medium, and the method comprises the steps: simplifying a three-dimensional network, obtaining a simplified three-dimensional grid with a plurality of simplified triangular surfaces, calculating the relation values between the plurality of simplified triangular surfaces and the adjacent simplified triangular surfaces, and according to the relation values, generating a point cloud LOD according to the relation values; three-dimensional point sampling is executed on the simplified triangular surface to obtain a point cloud formed by a plurality of three-dimensional points, a scene box is constructed according to the point cloud, the scene box comprises a plurality of voxels with different sizes, the voxels are selected for configuration, the plurality of three-dimensional points in one-to-one correspondence with the voxels are extracted from the point cloud, and the extracted three-dimensional points correspond to the voxels in one-to-one correspondence with the voxels in one-to-one correspondence with the voxels. The method comprises the steps of extracting a plurality of three-dimensional points, storing the extracted three-dimensional points as a json tree, receiving an instruction for reading the json tree, and selecting a corresponding voxel to configure a scene box according to a distance between a current view angle and the scene box, thereby realizing the simplification processing of three-dimensional network rendering, and optimizing the use experience of equipment with low-performance configuration.

Description

[0001] Technology Neighborhood

[0002] The present invention relates to the field of data processing technology, and in particular to a point cloud LOD generation method, system, device and storage medium based on a three-dimensional grid. Background Art

[0003] In the case of limited hardware resources, large-scene 3D model data cannot be loaded into the computer for real-time rendering all at once. The main reason is that the amount of data is too large. Although the performance of computers has been greatly improved, the memory capacity, computing and drawing performance of the machine are still very limited for the visualization of large-scale data fields at the city level. It is impossible to read massive amounts of spatial data from external memory for processing at one time. Even if the storage capacity of the hardware is increased, it is impossible to load and display large-scale 3D models in real time. On the one hand, it is impossible to achieve smooth data rendering, and on the other hand, it also causes a huge waste of computer computing power. Summary of the invention

[0004] The purpose of the present invention is to address the technical problems existing in the background technology and to propose a point cloud LOD generation method, system, device and storage medium based on a three-dimensional grid.

[0005] In order to achieve the above technical objectives, the technical solution adopted by the present invention is as follows:

[0006] A first implementation of the first aspect of the present invention provides a point cloud LOD generation method based on a three-dimensional grid, comprising:

[0007] S101, simplifying the three-dimensional network to obtain a simplified three-dimensional mesh having a plurality of simplified triangular faces;

[0008] S102, calculating the relationship values ​​between the plurality of simplified triangular faces and the adjacent simplified triangular faces, and performing three-dimensional point sampling on the simplified triangular faces according to the relationship values ​​to obtain a point cloud formed by the plurality of three-dimensional points;

[0009] S103, constructing a scene box according to the point cloud, wherein the scene box includes a plurality of voxels of different sizes, and selecting one voxel for configuration;

[0010] S104, extracting a plurality of three-dimensional points corresponding to a plurality of voxels one by one from the point cloud, and storing the extracted plurality of three-dimensional points as a json tree;

[0011] S105, receiving an instruction for reading the JSON tree, and selecting corresponding voxels to configure the scene box according to the distance between the current viewing angle and the scene box.

[0012] Optionally, in a second implementation of the first aspect of the present invention, S101 includes:

[0013] S1011, calculating the number of triangles contained in the three-dimensional network;

[0014] S1012: Determine whether the number of triangles is greater than 800 / m 2 ;

[0015] S1013, if more than 800 pieces / m 2 , a preset quadratic edge collapse extraction algorithm is used to simplify multiple triangular faces to obtain a simplified three-dimensional mesh.

[0016] Optionally, in a third implementation of the first aspect of the present invention, S102 includes:

[0017] S1021, calculating cosine values ​​of adjacent normal vectors between a plurality of simplified triangular faces and adjacent simplified triangular faces;

[0018] S1022, determining whether the cosine value is within a preset interval;

[0019] S1023: If the value is within the interval, perform the calculation with 20 3D points / m on the simplified triangular surface. 2 Sampling in the form of

[0020] S1024, if it is not within the interval value, determine again whether the cosine value is greater than the interval value;

[0021] S1025: If it is greater than the interval value, perform 10 3D points / m on the simplified triangular surface. 2 Sampling in the form of

[0022] S1026: If it is less than the interval value, perform 30 3D points / m on the simplified triangular surface. 2 Sampling in the form of .

[0023] Optionally, in a fourth implementation of the first aspect of the present invention, the interval value is [0.866, 0.89].

[0024] Optionally, in a fifth implementation of the first aspect of the present invention, the voxels include eight voxels, and the sizes of the eight voxels increase by two times one by one.

[0025] Optionally, in a sixth implementation of the first aspect of the present invention, the maximum size of the eight voxels is 384 cm.

[0026] Optionally, in a seventh implementation of the first aspect of the present invention, S104 includes:

[0027] S1041, extracting three-dimensional points corresponding to the current voxel from the point cloud in order of sizes of eight voxels from large to small, wherein the extracted three-dimensional points are closest to the center point of the current voxel;

[0028] S1042, determining whether there are any unextracted 3D points remaining in the point cloud;

[0029] S1043, if there are still remaining, reconstruct the ninth voxel, and correspond the remaining unextracted three-dimensional points to the ninth voxel, and continue to execute S1045;

[0030] S1044: If there is nothing left, continue to execute S1045;

[0031] S1045. Store the extracted multiple three-dimensional points as a JSON tree.

[0032] A first implementation of the second aspect of the present invention provides a point cloud LOD generation system based on a three-dimensional grid, comprising:

[0033] A simplification module, used to simplify the three-dimensional network to obtain a simplified three-dimensional mesh with multiple simplified triangular faces;

[0034] A sampling module is used to calculate the relationship values ​​between multiple simplified triangular faces and adjacent simplified triangular faces, and perform three-dimensional point sampling on the simplified triangular faces according to the relationship values ​​to obtain a point cloud formed by multiple three-dimensional points;

[0035] A first construction module is used to construct a scene box according to the point cloud, wherein the scene box includes a plurality of voxels of different sizes, and selects one of the voxels for configuration;

[0036] The second building module is used to extract a plurality of three-dimensional points corresponding to a plurality of voxels from the point cloud, and store the extracted plurality of three-dimensional points as a json tree;

[0037] The scene configuration module is used to receive instructions for reading the JSON tree, and select corresponding voxels to configure the scene box according to the distance between the current viewing angle and the scene box.

[0038] A first implementation of the third aspect of the present invention provides a three-dimensional grid-based point cloud LOD generation device, the three-dimensional grid-based point cloud LOD generation device comprising: a memory and at least one processor, the memory storing instructions, the memory and the at least one processor being interconnected via a line;

[0039] The at least one processor calls the instructions in the memory so that the three-dimensional grid-based point cloud LOD generation device executes the three-dimensional grid-based point cloud LOD generation method as described in any one of the first aspects of the present invention.

[0040] A first implementation method of the fourth aspect of the present invention provides a computer-readable storage medium, on which a computer program is stored. When the computer program is executed by a processor, a three-dimensional grid-based point cloud LOD generation method as described in any one of the first aspects of the present invention is implemented.

[0041] Compared with the prior art, the present invention has the following beneficial technical effects: simplifying the three-dimensional network to obtain a simplified three-dimensional mesh with multiple simplified triangular faces, calculating the relationship values ​​between the multiple simplified triangular faces and the adjacent simplified triangular faces, and performing three-dimensional point sampling on the simplified triangular faces according to the relationship values ​​to obtain a point cloud formed by multiple three-dimensional points, and constructing a scene box according to the point cloud, wherein the scene box includes multiple voxels of different sizes, and one voxel is selected for configuration, and multiple three-dimensional points corresponding to the multiple voxels are extracted from the point cloud, and the extracted multiple three-dimensional points are stored as a json tree, receiving an instruction for reading the json tree, and selecting the corresponding voxel to configure the scene box according to the distance between the current viewing angle and the scene box, thereby realizing simplified processing of three-dimensional network rendering and optimizing the user experience of low-performance configuration devices. BRIEF DESCRIPTION OF THE DRAWINGS

[0042] Figure 1 Schematic diagram of a first embodiment of a point cloud LOD generation method based on a three-dimensional grid in an embodiment of the present invention;

[0043] Figure 2 Schematic diagram of a second embodiment of a point cloud LOD generation method based on a three-dimensional grid in an embodiment of the present invention;

[0044] Figure 3 Schematic diagram of a third embodiment of a point cloud LOD generation method based on a three-dimensional grid in an embodiment of the present invention;

[0045] Figure 4 Schematic diagram of a seventh embodiment of a point cloud LOD generation method based on a three-dimensional grid in an embodiment of the present invention;

[0046] Figure 5 Schematic diagram of an embodiment of a point cloud LOD generation system based on a three-dimensional grid in an embodiment of the present invention;

[0047] Figure 6 Schematic diagram of an embodiment of a point cloud LOD generation device based on a three-dimensional grid in an embodiment of the present invention;

[0048] Figure 7 The present invention is a flowchart of the execution of the three-dimensional grid-based point cloud LOD generation method in an embodiment of the present invention. DETAILED DESCRIPTION

[0049] It should be noted that, in the absence of conflict, the embodiments of the present invention and the features in the embodiments may be combined with each other.

[0050] In the description of the present invention, it should be understood that the terms "center", "longitudinal", "lateral", "up", "down", "front", "back", "left", "right", "vertical", "horizontal", "top", "bottom", "inside", "outside" and the like indicate positions or positional relationships based on the positions or positional relationships shown in the accompanying drawings, and are only for the convenience of describing the present invention and simplifying the description, rather than indicating or implying that the device or component referred to must have a specific orientation, be constructed and operated in a specific orientation, and therefore cannot be understood as limiting the present invention. In addition, the terms "first", "second", and the like are only used for descriptive purposes, and cannot be understood as indicating or implying relative importance or implicitly indicating the number of technical features indicated. Thus, features defined as "first", "second", and the like may explicitly or implicitly include one or more features. In the description of the present invention, unless otherwise specified, "multiple" means two or more.

[0051] In the description of the present invention, it should be noted that, unless otherwise clearly specified and limited, the terms "installed", "connected", and "connected" should be understood in a broad sense, for example, it can be a fixed connection, a detachable connection, or a specific connection; it can be a mechanical connection or an electrical connection; it can be a direct connection or an indirect connection through an intermediate medium, or it can be the internal communication of two components. For ordinary technicians in this field, the specific meanings of the above terms in the present invention can be understood by specific circumstances.

[0052] For ease of understanding, the specific process of the embodiment of the present invention is described below. Figure 1-Figure 4 as well as Figure 7 , a point cloud LOD generation method based on a three-dimensional grid in an embodiment of the present invention comprises:

[0053] S101, simplifying the three-dimensional network to obtain a simplified three-dimensional mesh having a plurality of simplified triangular faces;

[0054] Furthermore, S101 can also specifically execute:

[0055] S1011, calculating the number of triangles contained in the three-dimensional network;

[0056] S1012: Determine whether the number of triangles is greater than 800 / m 2 ;

[0057] S1013, if more than 800 pieces / m 2 , a preset quadratic edge collapse extraction algorithm is used to simplify multiple triangular faces to obtain a simplified three-dimensional mesh.

[0058] In this embodiment, the more refined the 3D network is, the more faces it has, which places too high a requirement on the corresponding device hardware. In order to enable low-configuration devices to load the 3D network, the number of triangular faces on the processed 3D network needs to be calculated in advance in S1011. The purpose is to determine whether the number of triangular faces of the 3D network exceeds the tolerance range of the low-configuration device. If it is not set, it will lead to a waste of computing power. In this implementation, this judgment standard is 800 / m 2 , this benchmark can be set according to the specific implementation plan.

[0059] The Quadric Edge Collapse Decimation algorithm is used to simplify the three-dimensional network. The specific simplification parameters are set as follows:

[0060] The desired number of patches;

[0061] Set the quality threshold (between 0 and 1), where the higher the quality threshold, the harder the model displayed by the 3D point cloud visualization tool will try to maintain the shape of the original model;

[0062] Demarcate network boundaries to avoid simplification in the process;

[0063] Maintaining the original orientation of triangle faces, choosing the best locations when collapsing edges, and adding additional simplification constraints to preserve the current shape of triangles can significantly improve the shape quality of the final triangles in completely planar mesh parts.

[0064] S102, calculating the relationship values ​​between the plurality of simplified triangular faces and the adjacent simplified triangular faces, and performing three-dimensional point sampling on the simplified triangular faces according to the relationship values ​​to obtain a point cloud formed by the plurality of three-dimensional points;

[0065] In this embodiment, after simplifying the high-precision triangular faces, their features need to be sampled, but these sampling points must ensure that they can reflect the details of the scene while not using too many sampling points to reduce the demand for computing power. Therefore, adaptive sampling points are used for different scene finenesses. In this embodiment, the relationship value between the simplified triangular face and its adjacent simplified triangular faces is calculated, and adaptive sampling points are performed according to the relationship value.

[0066] Furthermore, S102 may further specifically execute:

[0067] S1021, calculating cosine values ​​of adjacent normal vectors between a plurality of simplified triangular faces and adjacent simplified triangular faces;

[0068] S1022, determining whether the cosine value is within a preset interval;

[0069] S1023: If the value is within the interval, perform the calculation with 20 3D points / m on the simplified triangular surface. 2 Sampling in the form of

[0070] S1024, if it is not within the interval value, determine again whether the cosine value is greater than the interval value;

[0071] S1025: If it is greater than the interval value, perform 10 3D points / m on the simplified triangular surface. 2 Sampling in the form of

[0072] S1026: If it is less than the interval value, perform 30 3D points / m on the simplified triangular surface. 2 Sampling in the form of .

[0073] In this embodiment, the relationship value is actually the cosine value of the normal vectors of two adjacent simplified triangular faces, and the number of sampling points of the corresponding simplified triangular face is divided according to the value of the cosine value. The specific interval value is [0.866, 0.89]. When the cosine value is within this interval, the surface of the simplified triangular face is relatively uniform, and 20 3D points / m are used. 2 The cosine value is greater than the interval value, that is, greater than 0.89, which means that the simplified triangle surface is a large plane, then this large plane can be sampled a little, specifically, by performing 10 three-dimensional points / m 2 , thereby reducing the computing power. If the cosine value is less than the interval value, that is, less than 0.866, then the simplified triangle surface is a place with large geometric jumps and more details. Then more sampling is required. Specifically, 30 3D points / m are executed. 2 The sampling in the form of is carried out through the above judgment method to realize adaptive sampling, so as to fully allocate computing power and avoid waste of computing power.

[0074] S103, constructing a scene box according to the point cloud, wherein the scene box includes a plurality of voxels of different sizes, and selecting one of the voxels for configuration;

[0075] In this embodiment, there are eight voxels, and the sizes of the eight voxels increase by two times. The largest size of the eight voxels is 384 cm, and the sizes of the eight voxels from large to small are 384 cm, 192 cm, 96 cm, 48 cm, 24 cm, 12 cm, 6 cm and 3 cm, respectively, where these eight voxels serve as the fineness of the scene box.

[0076] S104, extracting a plurality of three-dimensional points corresponding to a plurality of voxels one by one from the point cloud, and storing the extracted plurality of three-dimensional points as a json tree;

[0077] Furthermore, S104 may further specifically execute:

[0078] S1041, extracting three-dimensional points corresponding to the current voxel from the point cloud in order of sizes of eight voxels from large to small, wherein the extracted three-dimensional points are closest to the center point of the current voxel;

[0079] S1042, determining whether there are any unextracted 3D points remaining in the point cloud;

[0080] S1043, if there are still remaining, reconstruct the ninth voxel, and correspond the remaining unextracted three-dimensional points to the ninth voxel, and continue to execute S1045;

[0081] S1044: If there is nothing left, continue to execute S1045;

[0082] S1045. Store the extracted multiple three-dimensional points as a JSON tree.

[0083] In this embodiment, the ninth voxel is a voxel of minimum size, specifically 1.5 cm. When a three-dimensional point corresponding to a voxel is extracted from the point cloud, the three-dimensional point is stored in the voxel for subsequent extraction.

[0084] S105, receiving an instruction for reading the JSON tree, and selecting corresponding voxels to configure the scene box according to the distance between the current viewing angle and the scene box.

[0085] In this embodiment, since the json tree includes multiple three-dimensional points, when the json tree is accessed and read through an external device, a corresponding point cloud can be formed according to the multiple three-dimensional points on the json tree. At the same time, it should be noted that the selection of voxels in the scene box is determined according to the distance of the current viewing angle of the scene. For example, the farther the viewing angle is from the scene, the more it can be seen that the content in the scene cannot be clearly presented. Therefore, the voxels in the scene box are selected to be large in size to ensure that the approximate content of the scene can be presented. When the viewing angle is constantly approaching the scene, the voxels in the scene box are also constantly replaced with small ones. Of course, it needs to be mentioned here that when the viewing angle is very close to the scene, that is, the observable scene is only a small part of the window, and the scene box here only replaces the voxels located in this window with small ones, and the rest of the unobserved areas are all large voxels, so that the rendering of the scene can be completed with extremely low computing power.

[0086] The above describes the point cloud LOD generation method based on a three-dimensional grid in an embodiment of the present invention. The following describes the point cloud LOD generation system based on a three-dimensional grid in an embodiment of the present invention. Figure 5 , the three-dimensional grid-based point cloud LOD generation system includes:

[0087] A simplification module 201 is used to simplify the three-dimensional network to obtain a simplified three-dimensional mesh having a plurality of simplified triangular faces;

[0088] The sampling module 202 is used to calculate the relationship values ​​between the plurality of simplified triangular faces and the adjacent simplified triangular faces, and perform sampling of three-dimensional points on the simplified triangular faces according to the relationship values ​​to obtain a point cloud formed by the plurality of three-dimensional points;

[0089] A first construction module 203 is used to construct a scene box according to the point cloud, wherein the scene box includes a plurality of voxels of different sizes, and selects one of the voxels for configuration;

[0090] The second construction module 204 is used to extract a plurality of three-dimensional points corresponding to a plurality of voxels from the point cloud, and store the extracted plurality of three-dimensional points as a JSON tree;

[0091] The scene configuration module 205 is used to receive an instruction for reading the JSON tree, and select corresponding voxels to configure the scene box according to the distance between the current viewing angle and the scene box.

[0092] Specifically, it simplifies the three-dimensional network, obtains a simplified three-dimensional mesh with multiple simplified triangular faces, calculates the relationship values ​​between the multiple simplified triangular faces and the adjacent simplified triangular faces, and performs sampling of three-dimensional points on the simplified triangular faces according to the relationship values ​​to obtain a point cloud formed by multiple three-dimensional points. According to the point cloud, a scene box is constructed, wherein the scene box includes multiple voxels of different sizes, and one voxel is selected for configuration, and multiple three-dimensional points corresponding to the multiple voxels are extracted from the point cloud, and the extracted multiple three-dimensional points are stored as a json tree, and an instruction for reading the json tree is received, and according to the distance between the current viewing angle and the scene box, the corresponding voxel is selected to configure the scene box, thereby realizing simplified processing of three-dimensional network rendering and optimizing the usage experience of devices with low-performance configurations.

[0093] The simplification module 201 may also be specifically used for:

[0094] Calculate the number of triangles contained in the three-dimensional network;

[0095] Determine whether the number of triangles is greater than 800 / m 2 ;

[0096] If more than 800 pieces / m 2 , a preset quadratic edge collapse extraction algorithm is used to simplify multiple triangular faces to obtain a simplified three-dimensional mesh.

[0097] The sampling module 202 may also be specifically used for:

[0098] Calculate the cosine values ​​of adjacent normal vectors between multiple simplified triangular faces and adjacent simplified triangular faces;

[0099] Determine whether the cosine value is greater than the preset interval value;

[0100] If it is greater than the interval value, the simplified triangular surface is executed with 10 3D points / m 2 Sampling in the form of

[0101] If it is less than the interval value, the simplified triangular surface is executed with 30 3D points / m 2 Sampling in the form of

[0102] If it is within the interval, perform a 3D search on the simplified triangular surface with 20 3D points / m 2 Sampling in the form of .

[0103] The first building block 203 includes:

[0104] There are eight voxels, and the sizes of the eight voxels increase by two times, and the largest size of the eight voxels is 384 cm.

[0105] The abnormal condition also includes a parking violation threshold, and the detection module 204 can also be specifically used for:

[0106] According to the order of the sizes of the eight voxels from large to small, the three-dimensional points corresponding to the current voxel are extracted from the point cloud in sequence, wherein the extracted three-dimensional points are closest to the center point of the current voxel;

[0107] Determine whether there are any unextracted 3D points remaining in the point cloud;

[0108] If there are still some remaining, the ninth voxel is constructed again, and the remaining unextracted three-dimensional points correspond to the ninth voxel, and the next step is continued;

[0109] If there is nothing left, proceed to the next step;

[0110] The extracted multiple 3D points are stored as a JSON tree.

[0111] above Figure 5 The three-dimensional grid-based point cloud LOD generation system in the embodiment of the present invention is described in detail from the perspective of modular functional entities. The three-dimensional grid-based point cloud LOD generation device in the embodiment of the present invention is described in detail from the perspective of hardware processing.

[0112] Figure 63 is a schematic diagram of the structure of a point cloud LOD generation device based on a three-dimensional grid provided by an embodiment of the present invention. The point cloud LOD generation device 300 based on a three-dimensional grid may have relatively large differences due to different configurations or performances, and may include one or more processors (central processing units, CPU) 310 (for example, one or more processors) and a memory 320, and one or more storage media 330 (for example, one or more mass storage devices) storing application programs 333 or data 332. Among them, the memory 320 and the storage medium 330 may be short-term storage or permanent storage. The program stored in the storage medium 330 may include one or more modules (not shown in the figure), and each module may include a series of instruction operations in the point cloud LOD generation device 300 based on a three-dimensional grid. Furthermore, the processor 310 may be configured to communicate with the storage medium 330 to execute a series of instruction operations in the storage medium 330 on the point cloud LOD generation device 300 based on a three-dimensional grid.

[0113] The point cloud LOD generation device 300 based on the three-dimensional grid may also include one or more power supplies 340, one or more wired or wireless network interfaces 350, one or more input and output interfaces 360, and / or one or more operating systems 331, such as Windows Serve, Mac OS X, Unix, Linux, FreeBSD, etc. Those skilled in the art will appreciate that Figure 3 The three-dimensional grid-based point cloud LOD generation device structure shown does not constitute a limitation on the communication protocol device based on LAN screen projection, and may include more or fewer components than shown in the figure, or a combination of certain components, or a different arrangement of components.

[0114] The present invention also provides a computer-readable storage medium, which may be a non-volatile computer-readable storage medium or a volatile computer-readable storage medium. Instructions are stored in the computer-readable storage medium. When the instructions are executed on a computer, the computer executes the steps of the three-dimensional grid-based point cloud LOD generation method.

[0115] If the integrated unit is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention is essentially or the part that contributes to the prior art or all or part of the technical solution can be embodied in the form of a software product. The computer software product is stored in a storage medium, including a number of instructions to enable a computer device (which can be a personal computer, a server, or a network device, etc.) to perform all or part of the steps of the method described in each embodiment of the present invention. The aforementioned storage medium includes: U disk, mobile hard disk, read-only memory (ROM), random access memory (RAM), disk or optical disk and other media that can store program codes.

[0116] The above is a point cloud LOD generation method or multiple implementation methods based on a three-dimensional grid provided in combination with specific content, and it is not considered that the specific implementation of the present invention is limited to these descriptions. Any method, structure, etc. similar to or identical to the present invention, or a number of technical deductions or replacements based on the concept of the present invention, should be regarded as the protection scope of the present invention.

Claims

1. A point cloud LOD generation method based on three-dimensional grid, characterized in that: include: S101, simplifying the three-dimensional network to obtain a simplified three-dimensional mesh having a plurality of simplified triangular faces; S102, calculating the relationship values ​​between the plurality of simplified triangular faces and the adjacent simplified triangular faces, and performing three-dimensional point sampling on the simplified triangular faces according to the relationship values ​​to obtain a point cloud formed by the plurality of three-dimensional points; S103, constructing a scene box according to the point cloud, wherein the scene box includes a plurality of voxels of different sizes, and selecting one of the voxels for configuration; S104, extracting a plurality of the three-dimensional points corresponding to the plurality of the voxels one by one from the point cloud, and storing the extracted plurality of the three-dimensional points as a json tree; S105 , receiving an instruction for reading the JSON tree, and selecting corresponding voxels to configure the scene box according to the distance between the current viewing angle and the scene box.

2. A point cloud LOD generation method based on three-dimensional grid according to claim 1, characterized in that: The S101 includes: S1011, calculating the number of triangular faces included in the three-dimensional network; S1012: Determine whether the number of triangular faces is greater than 800 / m 2 ; S1013, if more than 800 pieces / m 2 , a preset quadratic edge collapse extraction algorithm is used to simplify the multiple triangular faces to obtain a simplified three-dimensional mesh.

3. The method for generating point cloud LOD based on three-dimensional grid according to claim 1, characterized in that: The S102 includes: S1021, calculating cosine values ​​of adjacent normal vectors between a plurality of the simplified triangular faces and adjacent simplified triangular faces; S1022, determining whether the cosine value is within a preset interval value; S1023, if it is within the interval value, perform the operation of 20 three-dimensional points / m on the simplified triangular surface. 2 Sampling in the form of S1024: If the value is not within the interval, determine again whether the cosine value is greater than the interval; S1025. If it is greater than the interval value, perform the operation of 10 three-dimensional points / m on the simplified triangular surface. 2 Sampling in the form of S1026: If it is less than the interval value, perform the operation of 30 three-dimensional points / m on the simplified triangular surface. 2 Sampling in the form of .

4. The method for generating point cloud LOD based on three-dimensional grid according to claim 3, characterized in that: The interval value is [0.866, 0.89].

5. The method for generating point cloud LOD based on three-dimensional grid according to claim 1, characterized in that: The voxels include eight voxels, and the sizes of the eight voxels increase by a factor of two.

6. The method for generating point cloud LOD based on three-dimensional grid according to claim 5, characterized in that: The largest dimension of the eight voxels is 384 cm.

7. The method for generating point cloud LOD based on three-dimensional grid according to claim 5, characterized in that: The S104 includes: Extracting a plurality of the three-dimensional points corresponding to the plurality of voxels one by one from the point cloud, and storing the extracted plurality of the three-dimensional points as a json tree; S1041, extracting the three-dimensional point corresponding to the current voxel from the point cloud in order of the sizes of the eight voxels from large to small, wherein the extracted three-dimensional point is closest to the center point of the current voxel; S1042, determining whether there are any unextracted three-dimensional points remaining in the point cloud; S1043, if there are still remaining, reconstruct the ninth voxel, and correspond the remaining unextracted three-dimensional points to the ninth voxel, and continue to execute S1045; S1044: If there is nothing left, continue to execute S1045; S1045. Store the extracted multiple three-dimensional points as a JSON tree.

8. A point cloud LOD generation system based on three-dimensional grid, characterized in that: include: A simplification module, used for simplifying the three-dimensional network to obtain a simplified three-dimensional mesh having a plurality of simplified triangular faces; A sampling module, used to calculate the relationship values ​​between the plurality of simplified triangular faces and the adjacent simplified triangular faces, and perform sampling of three-dimensional points on the simplified triangular faces according to the relationship values ​​to obtain a point cloud formed by the plurality of three-dimensional points; A first construction module is used to construct a scene box according to the point cloud, wherein the scene box includes a plurality of voxels of different sizes, and selects one of the voxels for configuration; A second construction module is used to extract a plurality of the three-dimensional points corresponding to the plurality of the voxels one by one from the point cloud, and store the extracted plurality of the three-dimensional points as a JSON tree; The scene configuration module is used to receive an instruction for reading the JSON tree, and select the corresponding voxel to configure the scene box according to the distance between the current viewing angle and the scene box.

9. A point cloud LOD generation device based on a three-dimensional grid, characterized in that: The three-dimensional grid-based point cloud LOD generation device includes: a memory and at least one processor, the memory stores instructions, and the memory and the at least one processor are interconnected through a line; The at least one processor calls the instructions in the memory to enable the three-dimensional grid-based point cloud LOD generation device to execute the three-dimensional grid-based point cloud LOD generation method as described in any one of claims 1-7.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the three-dimensional grid-based point cloud LOD generation method as described in any one of claims 1 to 7 is implemented.

Images