Lightweight method and system for 3D asset model

Abstract

The invention discloses a lightweight method and system for a 3D asset model. The system comprises a cloud server and a target vehicle. The cloud server is used for determining transmission delay of the data request, acquiring 3D map data of a corresponding road according to the position information, determining an image compression constant according to the transmission delay and the display equipment information, performing video-based point cloud compression on the 3D map data by using the image compression constant to obtain a map data packet, and sending the map data packet to the target vehicle; and the target vehicle obtains a two-dimensional image around the vehicle body, analyzes the map data packet to perform 3D map reconstruction to obtain an original 3D map, and aligns and fuses the two-dimensional image and the original 3D map to obtain a target 3D map. By dynamically determining the image compression constant and removing the color attribute data, the size of the map data packet is reduced, the transmission efficiency is improved, the two-dimensional image sensed by the vehicle in real time and the three-dimensional map data provided by the cloud are combined, and the accuracy and practicability of the map are improved.

Description

Technical Field

[0001] The present invention relates to the technical field of 3D image processing, and in particular to a lightweight method and system for a 3D asset model. Background Art

[0002] As the level of automobile intelligence continues to improve, consumers' demand for automobile navigation systems is also increasing. As a new generation of navigation systems, 3D navigation has become the first choice of consumers for its intuitive and accurate features. The 3D map of 3D navigation not only provides basic information such as roads, intersections, and buildings that traditional two-dimensional maps have, but also presents real-world elements such as roads, vehicles, pedestrians, and obstacles in a three-dimensional way to users through three-dimensional modeling technology.

[0003] 3D maps are usually stored as 3D asset models on cloud servers. When a vehicle needs 3D navigation, it sends a data request to the cloud server to obtain a 3D map of the area where the vehicle is located. However, traditional 3D map data transmission often takes a long time due to the large amount of data, especially in mobile scenarios, such as when a vehicle is driving. The timeliness of real-time acquisition and display of 3D maps is poor. Summary of the invention

[0004] The purpose of the present invention is to solve the problems of the above-mentioned background technology and to propose a lightweight method and system for 3D asset models.

[0005] The purpose of the present invention can be achieved through the following technical solutions: A first aspect of an embodiment of the present invention provides a lightweight system for a 3D asset model, the system comprising a cloud server and a target vehicle; wherein: The target vehicle sends a data request to the cloud server; the data request includes the location information and display device information of the target vehicle; The cloud server determines a transmission delay of the data request, obtains 3D map data of the corresponding road according to the location information, determines an image compression constant according to the transmission delay and the display device information, uses the image compression constant to perform video-based point cloud compression on the 3D map data to obtain a map data packet, and sends the map data packet to the target vehicle; the map data packet includes a geometric map, an occupancy map, and an attribute map, and the attribute map removes color attribute data; The target vehicle obtains a two-dimensional image around the vehicle body, parses the map data packet to perform 3D map reconstruction to obtain an original 3D map, and aligns and fuses the two-dimensional image with the original 3D map to obtain a target 3D map.

[0006] A second aspect of an embodiment of the present invention provides a lightweight method for a 3D asset model, the method being applied to a cloud server, and the method comprising: Receiving a data request sent by a target vehicle; the data request includes location information and display device information of the target vehicle; Determine a transmission delay of the data request, obtain 3D map data of a corresponding road according to the location information, and determine an image compression constant according to the transmission delay and the display device information; The 3D map data is compressed based on the video using an image compression constant to obtain a map data packet, and the map data packet is sent to the target vehicle; the map data packet includes a geometric map, an occupancy map and an attribute map, and the attribute map removes color attribute data; so that the target vehicle obtains a two-dimensional image around the vehicle body, parses the map data packet to perform 3D map reconstruction to obtain an original 3D map, and aligns and fuses the two-dimensional image with the original 3D map to obtain a target 3D map.

[0007] Optionally, the image compression constant includes a scaling constant and a quantization parameter; and determining the image compression constant according to the transmission delay and the display device information includes: According to the transmission delay, a preset quantization parameter table is queried to obtain a quantization parameter; the quantization parameter table records the corresponding relationship between the transmission delay and the quantization parameter; A preset scaling constant table is queried according to the display device information to obtain the scaling constant; the scaling constant table records the corresponding relationship between the display device information and the scaling constant.

[0008] Optionally, the step of performing video-based point cloud compression on the 3D map data using an image compression constant to obtain a map data packet includes: Step 1: Calculate the target data preservation rate and target frame utilization rate of compression according to the scaling constant and the quantization parameter, divide the point cloud of the 3D map data into a preset number of cuboids, and remove the cuboids that do not contain point cloud points to obtain an initial cuboid set; Step 2: If there is no cuboid in the initial cuboid set, execute step 5; otherwise, for each cuboid in the initial cuboid set, project the original point cloud in the cuboid into a 2D space with different angles to generate a patch; Step 3: If the size of the cuboid is not a preset minimum size and the patch of the cuboid does not meet the preset conditions, the point cloud of the cuboid is divided into a preset number of cuboids, and the cuboids that do not contain point cloud points are removed to update the initial cuboid set, and the process returns to step 2; the preset conditions are whether the data preservation rate and frame utilization rate of the patch relative to the original point cloud are greater than the target data preservation rate and target frame utilization rate, respectively; Step 4: If the size of the cuboid is the preset minimum size, or the patch of the cuboid meets the preset conditions, the cuboid is removed from the initial cuboid set and divided into the target cuboid set, and the process returns to step 2; Step 5: Put the patches corresponding to each cuboid in the target cuboid set into a 2D video frame, encode the 2D video frame to obtain a geometry map, an occupancy map and an attribute map, and package the geometry map, the occupancy map and the attribute map into a V-PCC bit stream as a map data packet.

[0009] Optionally, projecting the original point cloud in the cuboid into a 2D space with different angles to generate patches includes: According to the six faces of the cuboid, the original point cloud in the cuboid is projected into the 2D space to obtain six 2D images; Determine the 2D image that captures the most point cloud points among the six 2D images as the first image, and the 2D image that captures the second most projected points as the second image; Determine the point cloud points in the original point cloud within the cuboid that are not projected onto the first image to generate a third image; The first image, the second image and the third image are fused to obtain the patch of the cuboid.

[0010] Optionally, fusing the first image, the second image, and the third image to obtain the patch of the cuboid includes: Determine the blank points in the third image and fill them with the point cloud points at the corresponding positions in the first image to obtain a fourth image; The first image and the fourth image are unioned to obtain a fifth image, and the fifth image and the second image are added to obtain a target image as a patch of the cuboid.

[0011] Optionally, the target data preservation rate and target frame utilization rate of the compression are calculated according to the scaling constant and the quantization parameter as follows: Where B is the target data preservation rate, Z is the target frame utilization rate, a is the scaling constant, S is the quantization parameter, and ω is a preset constant.

[0012] A third aspect of an embodiment of the present invention provides a lightweight method for a 3D asset model, the method being applied to a target vehicle, the method comprising: Sending a data request to the cloud server; the data request includes the location information and display device information of the target vehicle; so that the cloud server determines the transmission delay of the data request, obtains the 3D map data of the corresponding road according to the location information, determines the image compression constant according to the transmission delay and the display device information, uses the image compression constant to perform video-based point cloud compression on the 3D map data to obtain a map data packet, and sends the map data packet to the target vehicle; the map data packet includes a geometric map, an occupancy map and an attribute map, and the attribute map removes the color attribute data; A two-dimensional image around the vehicle body is acquired, the map data packet is parsed to perform 3D map reconstruction to obtain an original 3D map, and the two-dimensional image is aligned and fused with the original 3D map to obtain a target 3D map.

[0013] Optionally, the image compression constant includes a scaling constant and a quantization parameter; the quantization parameter is obtained by querying a preset quantization parameter table according to the transmission delay; the quantization parameter table records the correspondence between the transmission delay and the quantization parameter; the scaling constant is obtained by querying a preset scaling constant table according to the display device information; the scaling constant table records the correspondence between the display device information and the scaling constant.

[0014] Optionally, the format of the map data packet is a V-PCC bit stream; parsing the map data packet to reconstruct a 3D map to obtain an original 3D map, and aligning and fusing the two-dimensional image with the original 3D map to obtain a target 3D map includes: extracting an occupancy bitstream, a geometry bitstream and an attribute bitstream from the map data packet; Decoding the three bit streams to obtain an occupancy map, a geometric image, and an initial attribute image, and generating three video frame sequences; Perform geometric reconstruction and smoothing on the occupancy map, geometric image and attribute image to obtain the reconstructed point cloud as the original 3D map; The two-dimensional image is aligned with the original 3D map, color features of the two-dimensional image are extracted, and the original 3D map is recolored according to the color features to obtain a target 3D map.

[0015] Beneficial effects of the present invention: An embodiment of the present invention provides a lightweight system for a 3D asset model, the system comprising a cloud server and a target vehicle; wherein: the target vehicle sends a data request to the cloud server; the data request comprises the location information and display device information of the target vehicle; the cloud server determines the transmission delay of the data request, obtains 3D map data of the corresponding road according to the location information, determines an image compression constant according to the transmission delay and the display device information, uses the image compression constant to perform video-based point cloud compression on the 3D map data to obtain a map data packet, and sends the map data packet to the target vehicle; the map data packet comprises a geometric map, an occupancy map and an attribute map, and the attribute map removes color attribute data; the target vehicle obtains a two-dimensional image around the vehicle body, parses the map data packet to perform 3D map reconstruction to obtain an original 3D map, and aligns and fuses the two-dimensional image with the original 3D map to obtain a target 3D map.

[0016] This solution determines the image compression constant based on the current network status (transmission delay) and display device information of the target vehicle. The vehicle obtains the map data packet from the cloud to generate a colorless original 3D map, and then uses the two-dimensional image collected locally by the vehicle to color the original 3D map to obtain the target 3D map. By dynamically determining the image compression constant and removing the color attribute data, the size of the map data packet is reduced, the transmission efficiency is improved, and the two-dimensional image perceived by the vehicle in real time and the three-dimensional map data provided by the cloud are combined to improve the accuracy and practicality of the map. BRIEF DESCRIPTION OF THE DRAWINGS

[0017] The present invention will be further described below in conjunction with the accompanying drawings.

[0018] Figure 1 is a flow chart of a lightweight method for 3D asset models applied to a cloud server; Figure 2 A flowchart of performing video-based point cloud compression on 3D map data provided by an embodiment of the present invention; Figure 3 A flowchart of a lightweight method for a 3D asset model of a target vehicle is shown. DETAILED DESCRIPTION

[0019] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0020] An embodiment of the present invention provides a lightweight system for a 3D asset model, the system comprising a cloud server and a target vehicle; wherein: The target vehicle sends a data request to the cloud server; the data request includes the location information and display device information of the target vehicle; The cloud server determines the transmission delay of the data request, obtains the 3D map data of the corresponding road according to the location information, determines the image compression constant according to the transmission delay and the display device information, uses the image compression constant to perform video-based point cloud compression on the 3D map data to obtain a map data packet, and sends the map data packet to the target vehicle; the map data packet includes a geometric map, an occupancy map, and an attribute map, and the attribute map removes the color attribute data; For the target vehicle, obtain the two-dimensional image around the vehicle body, parse the map data packet to reconstruct the 3D map to obtain the original 3D map, and align and fuse the two-dimensional image with the original 3D map to obtain the target 3D map.

[0021] The embodiment of the present invention provides a lightweight system for 3D asset models, which determines the image compression constant according to the current network status (transmission delay) of the target vehicle and the display device information. The vehicle obtains the map data packet from the cloud to generate a colorless original 3D map, and then uses the two-dimensional image collected locally by the vehicle to color the original 3D map to obtain the target 3D map. By dynamically determining the image compression constant and removing the color attribute data, the size of the map data packet is reduced, the transmission efficiency is improved, and the two-dimensional image perceived by the vehicle in real time and the three-dimensional map data provided by the cloud are combined to improve the accuracy and practicality of the map.

[0022] The embodiment of the present invention provides a lightweight method for 3D asset model applied to a cloud server, see Figure 1 , Figure 1 The flowchart is a lightweight method for a 3D asset model applied to a cloud server, the method comprising: S101, receiving a data request sent by a target vehicle; S102, determining a transmission delay of a data request, acquiring 3D map data of a corresponding road according to the location information, and determining an image compression constant according to the transmission delay and display device information; S103, using image compression constants to perform video-based point cloud compression on 3D map data to obtain a map data packet, and sending the map data packet to the target vehicle; so that the target vehicle can obtain a two-dimensional image around the vehicle body, parse the map data packet to perform 3D map reconstruction to obtain an original 3D map, and align and fuse the two-dimensional image with the original 3D map to obtain a target 3D map.

[0023] The data request includes the location information and display device information of the target vehicle; the map data packet includes a geometry map, an occupancy map, and an attribute map, and the attribute map removes the color attribute data.

[0024] The embodiment of the present invention provides a lightweight method for 3D asset models applied to cloud servers. The image compression constant is determined according to the current network status (transmission delay) of the target vehicle and the display device information. The vehicle obtains the map data packet from the cloud to generate a colorless original 3D map, and then the original 3D map is colored by the two-dimensional image collected locally by the vehicle to obtain the target 3D map. By dynamically determining the image compression constant and removing the color attribute data, the size of the map data packet is reduced, the transmission efficiency is improved, and the two-dimensional image perceived by the vehicle in real time and the three-dimensional map data provided by the cloud are combined to improve the accuracy and practicality of the map.

[0025] In one implementation, the transmission delay reflects the current network status of the target vehicle. When the transmission delay is small, indicating that the network status is good, the image compression constant can be adjusted to retain more point cloud points when compressing the 3D map data, making the reconstructed target 3D map clearer; conversely, a reasonable number of point cloud points are retained when compressing the 3D map data, so that the target 3D map can be displayed in real time to ensure basic 3D navigation functions.

[0026] In one implementation, in Video-Based Point Cloud Compression (V-PCC), geometry map, occupancy map and attribute map are three important components.

[0027] The geometric map stores the spatial position information of the point cloud data and is related to the point cloud reconstruction geometry. It is obtained by projecting the geometric information of the point cloud video onto a two-dimensional plane. The geometric map plays an important role in the V-PCC coding standard because it not only directly reflects the spatial structure of the point cloud, but is also used to guide the generation of the attribute map. During the encoding process, the encoding quality of the geometric map will directly affect the encoding quality of the attribute map.

[0028] The occupancy map is used in V-PCC to represent the occupancy of the point cloud in three-dimensional space. It is usually a binary image in which white pixels represent the space occupied by the point cloud and black pixels represent unoccupied space. The occupancy map plays a key role in the encoding process because it helps the encoder identify which areas are valid point cloud data and which areas are invalid or background data. This helps the encoder allocate encoding resources more efficiently, thereby improving compression efficiency. In V-PCC, the occupancy map is usually generated by the geometric map. The occupancy map can be obtained by thresholding or segmenting the geometric map. The occupancy map is further used to generate depth maps and texture maps, which are used to recover the geometry and attribute information of the point cloud in the subsequent encoding process.

[0029] The attribute map saves the attribute information of the point cloud data, such as color, reflectivity, normal, etc. These attribute information are crucial for the visual presentation and subsequent processing of the point cloud. In V-PCC, the attribute map is usually obtained by projecting the attribute information of the point cloud onto a two-dimensional plane corresponding to the geometric map. The encoding quality of the attribute map will directly affect the visual effect of the point cloud video. During the encoding process, the attribute map is encoded based on the geometric map and the occupancy map. In the embodiment of the present invention, the attribute map only contains reflectivity and normal attribute information and does not contain color attribute information. Eliminating color attribute information can greatly reduce the data volume of the map data packet and improve the transmission timeliness. It will not affect the reconstruction of the original 3D map, and the original 3D map can also realize the function of 3D navigation function.

[0030] In one embodiment, the image compression constant includes a scaling constant and a quantization parameter; determining the image compression constant according to the transmission delay and the display device information includes: According to the transmission delay, a preset quantization parameter table is queried to obtain the quantization parameter; the quantization parameter table records the corresponding relationship between the transmission delay and the quantization parameter; The preset scaling constant table is queried according to the display device information to obtain the scaling constant; the scaling constant table records the corresponding relationship between the display device information and the scaling constant.

[0031] In one implementation, scaling constants are often used in V-PCC to adjust the scale of point cloud data to better fit the processing range of the encoder. Quantization Parameter (QP) is a parameter used in V-PCC to control the quantization step size during encoding. Quantization is the process of mapping the continuous value of a signal into multiple discrete amplitudes. The size of the quantization parameter directly determines the size of the quantization step size, which in turn affects the amount and quality of encoded data. Both scaling constants and quantization parameters affect the amount of data in the compressed map data packet.

[0032] The display device information is the size of the target vehicle's display device. The scale of the point cloud data is determined by the size of the display device to optimize data transmission and display effects.

[0033] In one implementation, the preset quantization parameter table and scaling constant table are set by technicians according to actual needs. The quantization parameter is proportional to the transmission delay, and the value range of the quantization parameter is usually between 0 and 51. The scaling constant is inversely proportional to the size of the target vehicle display device, and the scaling constant is between 0.01 and 1.

[0034] In one embodiment, see Figure 2 , Figure 2The flowchart of performing video-based point cloud compression on 3D map data provided by an embodiment of the present invention. Performing video-based point cloud compression on 3D map data using image compression constants to obtain a map data packet includes: Step 1: Calculate the target data preservation rate and target frame utilization rate of compression according to the scaling constant and the quantization parameter, divide the point cloud of the 3D map data into a preset number of cuboids, and remove the cuboids that do not contain point cloud points to obtain an initial cuboid set; Step 2: If there is no cuboid in the initial cuboid set, execute step 5; otherwise, for each cuboid in the initial cuboid set, project the original point cloud in the cuboid into a 2D space with different angles to generate a patch; Step 3: If the size of the cuboid is not the preset minimum size and the patch of the cuboid does not meet the preset conditions, the point cloud of the cuboid is divided into a preset number of cuboids, and the cuboids that do not contain point cloud points are removed to update the initial cuboid set, and then return to execute step 2; the preset conditions are whether the data preservation rate and frame utilization rate of the patch relative to the original point cloud are greater than the target data preservation rate and target frame utilization rate respectively; Step 4: If the size of the cuboid is the preset minimum size, or the patch of the cuboid meets the preset conditions, the cuboid is removed from the initial cuboid set and divided into the target cuboid set, and the process returns to step 2; Step 5: Put the patches corresponding to each cuboid in the target cuboid set into a 2D video frame, encode the 2D video frame to obtain a geometry map, an occupancy map and an attribute map, and package the geometry map, the occupancy map and the attribute map into a V-PCC bit stream as a map data packet.

[0035] In one implementation, the target data preservation rate reflects the information loss during the encoding process, that is, to what extent the encoded data can restore the geometry and attribute information of the original point cloud data. The higher the target data preservation rate, the closer the encoded data is to the original data and the less information loss is, but correspondingly, the amount of encoded data may also be larger. In practical applications, the target data preservation rate is usually not less than 87%.

[0036] In one implementation, the target frame utilization rate is used to measure the efficiency of frame resource utilization during the encoding process. It reflects how effectively the encoder uses each frame to transmit information when processing point cloud data. In V-PCC, point cloud data is mapped into a series of video frames, and the encoding of point cloud data is achieved by compressing these video frames. The higher the target frame utilization rate, the fewer frames the encoder needs to transmit the same amount of information, thereby being able to use frame resources more effectively and reduce the amount of encoded data. In practical applications, the target frame utilization rate is usually higher than 75%.

[0037] In one implementation, the preset number may be set to 8, and the preset minimum size is 1 / 64 of the point cloud volume of the 3D map data.

[0038] In one embodiment, projecting the original point cloud in the cuboid into a 2D space with different angles to generate patches includes: According to the six faces of the cuboid, the original point cloud in the cuboid is projected into the 2D space to obtain six 2D images; Determine the 2D image that captures the most point cloud points among the six 2D images as the first image, and the 2D image that captures the second most projected points as the second image; Determine the point cloud points in the original point cloud within the cuboid that are not projected onto the first image to generate a third image; The first image, the second image and the third image are fused to obtain the patch of the cuboid.

[0039] In one implementation, the original point cloud in the cuboid is saved in the first image and the third image respectively, and the first image and the third image are enhanced by the second image, which can reduce the discontinuity of the boundaries between the patches and improve the image quality of the 3D map reconstruction.

[0040] In one embodiment, fusing the first image, the second image, and the third image to obtain the patch of the cuboid includes: Determine the blank points in the third image and fill them with the point cloud points at the corresponding positions in the first image to obtain a fourth image; The first image and the fourth image are unioned to obtain a fifth image, and the fifth image and the second image are added to obtain a target image as a patch of the cuboid.

[0041] In one embodiment, the target data preservation rate and target frame utilization rate of compression are calculated based on the scaling constant and the quantization parameter as follows: Where B is the target data preservation rate, Z is the target frame utilization rate, a is the scaling constant, S is the quantization parameter, and ω is a preset constant.

[0042] In one implementation, ω is set to 100.

[0043] The embodiment of the present invention provides a lightweight method for a 3D asset model of a target vehicle, see Figure 3 , Figure 3 A flowchart of a lightweight method for a 3D asset model of a target vehicle is provided, the method comprising: S301, sending a data request to a cloud server; so that the cloud server determines the transmission delay of the data request, obtains 3D map data of the corresponding road according to the location information, determines an image compression constant according to the transmission delay and the display device information, uses the image compression constant to perform video-based point cloud compression on the 3D map data to obtain a map data packet, and sends the map data packet to the target vehicle; S302, obtaining a two-dimensional image around the vehicle body, parsing the map data packet to perform 3D map reconstruction to obtain an original 3D map, and aligning and fusing the two-dimensional image with the original 3D map to obtain a target 3D map.

[0044] The data request includes the location information and display device information of the target vehicle; the map data packet includes a geometric map, an occupancy map, and an attribute map, and the attribute map removes the color attribute data.

[0045] The embodiment of the present invention provides a lightweight method for 3D asset models applied to cloud servers. The image compression constant is determined according to the current network status (transmission delay) of the target vehicle and the display device information. The vehicle obtains the map data packet from the cloud to generate a colorless original 3D map, and then the original 3D map is colored by the two-dimensional image collected locally by the vehicle to obtain the target 3D map. By dynamically determining the image compression constant and removing the color attribute data, the size of the map data packet is reduced, the transmission efficiency is improved, and the two-dimensional image perceived by the vehicle in real time and the three-dimensional map data provided by the cloud are combined to improve the accuracy and practicality of the map.

[0046] In one embodiment, the image compression constant includes a scaling constant and a quantization parameter; the quantization parameter is obtained by querying a preset quantization parameter table based on the transmission delay; the quantization parameter table records the correspondence between the transmission delay and the quantization parameter; the scaling constant is obtained by querying a preset scaling constant table based on the display device information; the scaling constant table records the correspondence between the display device information and the scaling constant.

[0047] In one embodiment, the format of the map data packet is a V-PCC bit stream; Parsing the map data packet to reconstruct the 3D map to obtain the original 3D map, aligning and fusing the two-dimensional image with the original 3D map to obtain the target 3D map includes: extracting an occupancy bit stream, a geometry bit stream, and an attribute bit stream from a map data packet; Decoding the three bit streams to obtain an occupancy map, a geometric image, and an initial attribute image, and generating three video frame sequences; Perform geometric reconstruction and smoothing on the occupancy map, geometric image and attribute image to obtain the reconstructed point cloud as the original 3D map; The two-dimensional image is aligned with the original 3D map, the color features of the two-dimensional image are extracted, and the original 3D map is recolored according to the color features to obtain the target 3D map. In one implementation, the following steps can be performed to align the 2D image with the original 3D map: identify the common feature points (at least three) of the 2D image and the 3D map; establish a coordinate system that matches the 3D map based on the scale of the 2D image and the 3D map, and match the common feature points in the same coordinate system; use the spatial correction tool in ArcGIS to align the 2D image and the 3D map. Use the georeferencing tool to specify the spatial position of each point in the 2D image.

[0048] The above is a detailed description of an embodiment of the present invention, but the content is only a preferred embodiment of the present invention and cannot be considered to limit the scope of implementation of the present invention. All equivalent changes and improvements made within the scope of the present invention should still fall within the scope of the patent coverage of the present invention.

Claims

1. A lightweight system for 3D asset models, characterized in that: The system includes a cloud server and a target vehicle; wherein: The target vehicle sends a data request to the cloud server; the data request includes the location information and display device information of the target vehicle; The cloud server determines a transmission delay of the data request, obtains 3D map data of the corresponding road according to the location information, determines an image compression constant according to the transmission delay and the display device information, uses the image compression constant to perform video-based point cloud compression on the 3D map data to obtain a map data packet, and sends the map data packet to the target vehicle; the map data packet includes a geometric map, an occupancy map, and an attribute map, and the attribute map removes color attribute data; The target vehicle obtains a two-dimensional image around the vehicle body, parses the map data packet to perform 3D map reconstruction to obtain an original 3D map, and aligns and fuses the two-dimensional image with the original 3D map to obtain a target 3D map.

2. A lightweight method for a 3D asset model, characterized in that: The method is applied to a cloud server, and the method includes: Receiving a data request sent by a target vehicle; the data request includes location information and display device information of the target vehicle; Determine a transmission delay of the data request, obtain 3D map data of a corresponding road according to the location information, and determine an image compression constant according to the transmission delay and the display device information; The 3D map data is compressed based on the video using an image compression constant to obtain a map data packet, and the map data packet is sent to the target vehicle; the map data packet includes a geometric map, an occupancy map and an attribute map, and the attribute map removes color attribute data; so that the target vehicle obtains a two-dimensional image around the vehicle body, parses the map data packet to perform 3D map reconstruction to obtain an original 3D map, and aligns and fuses the two-dimensional image with the original 3D map to obtain a target 3D map.

3. The lightweight method of a 3D asset model according to claim 2, characterized in that: The image compression constant includes a scaling constant and a quantization parameter; and determining the image compression constant according to the transmission delay and the display device information includes: According to the transmission delay, a preset quantization parameter table is queried to obtain a quantization parameter; the quantization parameter table records the corresponding relationship between the transmission delay and the quantization parameter; A preset scaling constant table is queried according to the display device information to obtain the scaling constant; the scaling constant table records the corresponding relationship between the display device information and the scaling constant.

4. The lightweight method of a 3D asset model according to claim 3, characterized in that: The step of using an image compression constant to perform video-based point cloud compression on the 3D map data to obtain a map data packet includes: Step 1: Calculate the target data preservation rate and target frame utilization rate of compression according to the scaling constant and the quantization parameter, divide the point cloud of the 3D map data into a preset number of cuboids, and remove the cuboids that do not contain point cloud points to obtain an initial cuboid set; Step 2: If there is no cuboid in the initial cuboid set, execute step 5; otherwise, for each cuboid in the initial cuboid set, project the original point cloud in the cuboid into a 2D space with different angles to generate a patch; Step 3: If the size of the cuboid is not a preset minimum size and the patch of the cuboid does not meet the preset conditions, the point cloud of the cuboid is divided into a preset number of cuboids, and the cuboids that do not contain point cloud points are removed to update the initial cuboid set, and the process returns to step 2; the preset conditions are whether the data preservation rate and frame utilization rate of the patch relative to the original point cloud are greater than the target data preservation rate and target frame utilization rate, respectively; Step 4: If the size of the cuboid is the preset minimum size, or the patch of the cuboid meets the preset conditions, the cuboid is removed from the initial cuboid set and divided into the target cuboid set, and the process returns to step 2; Step 5: Put the patches corresponding to each cuboid in the target cuboid set into a 2D video frame, encode the 2D video frame to obtain a geometry map, an occupancy map and an attribute map, and package the geometry map, the occupancy map and the attribute map into a V-PCC bit stream as a map data packet.

5. The lightweight method of a 3D asset model according to claim 4, characterized in that: Projecting the original point cloud in the cuboid into 2D space with different angles to generate patches includes: According to the six faces of the cuboid, the original point cloud in the cuboid is projected into the 2D space to obtain six 2D images; Determine the 2D image that captures the most point cloud points among the six 2D images as the first image, and the 2D image that captures the second most projected points as the second image; Determine the point cloud points in the original point cloud within the cuboid that are not projected onto the first image to generate a third image; The first image, the second image and the third image are fused to obtain the patch of the cuboid.

6. A lightweight method for a 3D asset model according to claim 5, characterized in that: The patch of the cuboid obtained by fusing the first image, the second image and the third image includes: Determine the blank points in the third image and fill them with the point cloud points at the corresponding positions in the first image to obtain a fourth image; The first image and the fourth image are unioned to obtain a fifth image, and the fifth image and the second image are added to obtain a target image as a patch of the cuboid.

7. The lightweight method of a 3D asset model according to claim 4, characterized in that: The target data preservation rate and target frame utilization rate of compression are calculated based on the scaling constant and quantization parameter: Where B is the target data preservation rate, Z is the target frame utilization rate, a is the scaling constant, S is the quantization parameter, and ω is a preset constant.

8. A lightweight method for a 3D asset model, characterized in that: The method is applied to a target vehicle, and the method comprises: Sending a data request to a cloud server; the data request includes the location information and display device information of the target vehicle; so that the cloud server determines the transmission delay of the data request, obtains the 3D map data of the corresponding road according to the location information, determines the image compression constant according to the transmission delay and the display device information, uses the image compression constant to perform video-based point cloud compression on the 3D map data to obtain a map data packet, and sends the map data packet to the target vehicle; the map data packet includes a geometric map, an occupancy map, and an attribute map, and the attribute map removes color attribute data; A two-dimensional image around the vehicle body is acquired, the map data packet is parsed to perform 3D map reconstruction to obtain an original 3D map, and the two-dimensional image is aligned and fused with the original 3D map to obtain a target 3D map.

9. The lightweight method of a 3D asset model according to claim 8, characterized in that: The image compression constant includes a scaling constant and a quantization parameter; the quantization parameter is obtained by querying a preset quantization parameter table according to the transmission delay; the quantization parameter table records the corresponding relationship between the transmission delay and the quantization parameter; The scaling constant is obtained by querying a preset scaling constant table according to the display device information; the scaling constant table records the corresponding relationship between the display device information and the scaling constant.

10. The lightweight method of a 3D asset model according to claim 9, characterized in that: The format of the map data packet is a V-PCC bit stream; Parsing the map data packet to reconstruct the 3D map to obtain an original 3D map, and aligning and fusing the two-dimensional image with the original 3D map to obtain a target 3D map includes: extracting an occupancy bitstream, a geometry bitstream and an attribute bitstream from the map data packet; Decoding the three bit streams to obtain an occupancy map, a geometric image, and an initial attribute image, and generating three video frame sequences; Perform geometric reconstruction and smoothing on the occupancy map, geometric image and attribute image to obtain the reconstructed point cloud as the original 3D map; The two-dimensional image is aligned with the original 3D map, color features of the two-dimensional image are extracted, and the original 3D map is recolored according to the color features to obtain a target 3D map.

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