Urban three-dimensional texture automatic identification and modeling method based on millimeter wave radar

Abstract

The invention discloses an urban three-dimensional texture automatic identification and modeling method based on a millimeter wave radar, and the method comprises the following steps: building an air-ground data intelligent collection device, and collecting the three-dimensional data of a target city; a city three-dimensional texture recognition algorithm is constructed, and the three-dimensional texture of the target city is automatically recognized; generating and modeling a three-dimensional texture of a target city through a target detection algorithm; the three-dimensional texture of the target city is displayed through a visual platform, and digital interaction is carried out on the three-dimensional texture of the target city in combination with wearable virtual reality equipment; and through 3D printing equipment, the three-dimensional texture of the target city is printed, and visual display is provided for city managers and city designers. According to the invention, through a millimeter wave radar which is a high-precision and full-dimension data acquisition technology, the problems of low resolution of research data, three-dimensional data missing and the like in the prior art are solved, and the research precision and the research dimension of urban texture are effectively improved.

Description

Technical Field

[0001] The present invention relates to the technical field of urban planning, and in particular to a method for automatic recognition and modeling of urban three-dimensional texture based on millimeter wave radar. Background Art

[0002] Urban three-dimensional texture is one of the important research contents of urban planning and urban renewal, and it is of great significance to urban development. Currently, there are the following limitations:

[0003] First, the acquisition of urban three-dimensional texture data is complex and expensive, usually requiring the use of high-cost technologies such as LiDAR or high-precision aerial photogrammetry, and a lot of effort is needed to complete the basic work of data collection. The labor cost, economic cost, and time cost are high, and a lot of manpower and material resources are required.

[0004] Second, the accuracy of urban 3D texture recognition is limited. Due to the limitations of data acquisition and insufficient detail representation, the accuracy of urban 3D texture models is usually low, especially in terms of the geometric shape, height and location of buildings, which may not meet the needs of some high-precision applications.

[0005] Third, there are few interactive displays of urban three-dimensional texture evaluation. Although urban three-dimensional texture identification has broad application prospects in urban planning, architectural design, virtual reality and other fields, it is currently impossible for managers, decision makers and the public to intuitively understand the problems of urban three-dimensional texture and its influencing factors. Summary of the invention

[0006] In order to solve the deficiencies mentioned in the above background technology, the purpose of the present invention is to provide a method for automatic recognition and modeling of urban three-dimensional texture based on millimeter wave radar.

[0007] The purpose of the present invention can be achieved by the following technical solution: A method for automatic recognition and modeling of urban three-dimensional texture based on millimeter wave radar, comprising the following steps:

[0008] S1 Intelligent collection of urban three-dimensional texture data: Build an air-ground dual-dimensional data intelligent collection device equipped with millimeter-wave radar to collect two types of real scenes of building entities and open spaces in the target city. The collection dimensions include two types: plane and three-dimensional; and translate the air and ground data and store them together in a unified data set to construct the basic data set of the target city;

[0009] S2 Automatic recognition of urban 3D texture: Call the target city basic data set constructed in step S1, generate urban building 3D texture for building entities, rasterize the open space virtual body and construct the spatial triangular surface of each grid, calculate the height of each grid with the millimeter wave radar intelligent ranging equipment, generate urban open space 3D texture, and store the generated urban building 3D texture and urban open space 3D texture into the target city 3D texture data set;

[0010] S3 Automatic modeling of urban 3D texture: Call the urban 3D texture data set calculated in step S2, uniformly translate different types of data into 3D space coordinates, call the image data in the urban basic data set in step S1, use the target detection algorithm to automatically identify the 3D texture plane and facade element information, and unify the coordinates of all data, and generate a 3D real scene of the urban 3D texture identified in step S2 through a laser VR scanner;

[0011] S4: Achievement interaction: Using a handheld controller to display a virtual scene of the three-dimensional urban texture model in step S3, and using VR glasses and virtual reality gloves to achieve scene interaction with the model;

[0012] S5 Results printing: Print the three-dimensional texture model of the target city generated in step S4 through large-scale desktop 3D printing equipment and drawing data output equipment; the printing includes two types: plan view and three-dimensional model, and the three-dimensional model is colored using coloring software.

[0013] Furthermore, in the step S1, an air-ground dual-dimensional data intelligent collection device equipped with a millimeter-wave radar with a transmission frequency of 77 Hz, a ranging range of 50 m, and a ranging angle of 60° is built. The device is mounted on a drone during aerial collection, and is mounted around vehicles and buildings during ground collection to collect real scenes of the target city. The collected data types include two categories: building entities and open spaces, and the collection dimensions include two categories: plane and three-dimensional. The air and ground data are translated and stored together in a unified data set NAS device to construct a basic data set of the target city. Among them, the plane data includes the circumference, area, spatial coordinates, and fifth facade photos of the building entity; the length, width, spatial coordinates, and plane views of the open space; the three-dimensional data includes the height of the building entity and the facade photos of the building.

[0014] Furthermore, in step S1, the aerial equipment uses a millimeter-wave radar drone, and the ground equipment uses a millimeter-wave radar scanner fixed on a vehicle and a millimeter-wave radar sensor installed around buildings (structures). The specific functions of the millimeter-wave radar drone are to collect point cloud data of urban high-rise and super-high-rise buildings and take photos of building facades, the millimeter-wave radar scanner fixed on the vehicle is to collect point cloud data of urban single-story and multi-story buildings and structures of 3m and above, and the millimeter-wave radar sensors installed around buildings (structures) are to collect point cloud data of structures below 3m, plants and decorations in open space virtual bodies.

[0015] Furthermore, the step S2 specifically includes:

[0016] Step S2-1: Retrieve the perimeter and height information of the target city basic data set constructed in step S1, and divide the three-dimensional urban texture into building entities and open space virtual bodies; wherein the building entity must simultaneously meet the requirements of being a quadrilateral, having a shape completeness greater than 50%, and having a height greater than 3m;

[0017] Step S2-2: for the building entity, call the building height information in the target city basic data set in step S1, generate the three-dimensional texture of the city building, and store it in the target city three-dimensional texture data set;

[0018] Step S2-3: For the open space virtual body, divide the open space plane into 10m×10m grids, rename each grid in sequence as "open space-n"; where "n" represents the number sequence, and delete the grids with an area less than 10m×10m;

[0019] Step S2-4: Use an intelligent laser ranging device equipped with a millimeter-wave radar with a transmission frequency of 77 GHz, a ranging range of 40 m, and a side angle range of 150° to scan the vertices and midpoints of the four sides of the grid generated in step S2-3, and construct spatial triangular faces of 8 points respectively, and name them in sequence as "n spatial triangular face-m", where "n" is consistent with the grid number, and "m" represents the number sequence number;

[0020] Step S2-5: Retrieve the target city building plan and height data obtained in step S1, calculate the open space height in combination with the grid space triangle surface constructed in step S2-4, generate the three-dimensional texture of the urban open space, and store it in the target city three-dimensional texture dataset.

[0021] Furthermore, the spatial triangles of each grid are constructed by connecting the midpoint of the grid, the top edge of the roof of the adjacent building, and the vertex of the bottom edge of the ground of the adjacent building to obtain 8 "spatial triangles"; wherein the adjacent building refers to a building entity with a width of less than or equal to 40m between the bottom edge of the building and the bottom edge of the open space; the building edge refers to the line connecting the top edge of the roof of the adjacent building and the vertex of the bottom edge of the ground, that is, the edge of the "spatial triangle" coincides with a certain facade of the building.

[0022] Furthermore, the height of each grid is calculated by combining the millimeter wave radar intelligent ranging device, which means taking the line connecting the four corners of the grid as the midpoint of the grid, and calculating the height of each grid by the midpoint S of the grid. M Use an intelligent outdoor laser ranging device equipped with a millimeter-wave radar with a transmission frequency of 77GHz, a ranging range of 40m, and a side angle range of 150° to draw a vertical line to the surrounding buildings, and record the vertical point as the building point S A , measure the distance from the grid midpoint to the building point; then draw a vertical line from the building point to the building facade, and record the intersection of the vertical line and the top edge of the building as the building top edge point S B ; Measure building point S A To the top edge point S of the building B The distance is recorded as the building point height H AB , grid height Where m∈(1,8).

[0023] Furthermore, the step S3 specifically includes:

[0024] Step S3-1 calls the urban three-dimensional texture data set calculated in step S2 to collect the building entity height of the urban building three-dimensional texture and the open space height of the urban open space three-dimensional texture, and uniformly translates the building entity height data and the open space height data into three-dimensional space coordinates;

[0025] Step S3-2 calls the fifth facade image of the building entity, the open space plane photo, and the building (structure) facade photo data in the urban basic data set of step S1, and uses the R-FCN target detection algorithm to automatically identify the image data; the recognition process includes, first preprocessing the photo to eliminate irrelevant information; importing the preprocessed image into the trained ResNet-101 classification network; there are 3 cognitions on the feature map obtained by the last convolutional layer of the pre-trained network, the first branch performs RPN operation on the feature map to obtain the corresponding ROI, the second branch obtains the K*K*(C+1)-dimensional position sensitive score map for classification on the feature map, and the third branch obtains the 4*K*K-dimensional position sensitive score map for regression on the feature map; position-sensitive average pooling operations are performed on the K*K*(C+1)-dimensional position sensitive score map and the 4*K*K-dimensional position sensitive score map to obtain corresponding categories and position information; the recognition content includes three-dimensional texture plane and facade element information; the recognition result is the coordinate information and spatial relationship information of the image;

[0026] Step S3-3 unifies the coordinate data of steps S3-1 and S3-2 into the WGS 84 coordinate system;

[0027] In step S3-4, the spatial coordinates after the unified coordinate system in step S3-3 and the point cloud data obtained by the millimeter wave radar scanning in step S1 are processed to remove overlapping points and contradictory points, and a laser VR scanner is used to generate a three-dimensional real scene of the three-dimensional texture of the city.

[0028] Furthermore, the display of the results in step S4 uses a projector with a resolution of 4K or above, a depth sensor, a motion sensor, a touch sensor, and a sand table to construct a three-dimensional digital sand table, and cooperates with a handheld controller to display a virtual scene of the three-dimensional texture model of the city in step S3, and uses VR glasses and virtual reality gloves to realize scene interaction with the model; wherein, an in-depth experience of the model is realized through the handheld controller; the selection, scaling, and modification of the model are realized through the virtual reality gloves; the interactive display of the model is realized through the VR glasses, and the model can be operated together with the virtual reality gloves.

[0029] Furthermore, in the step S5, the result printing is performed by printing the three-dimensional texture model of the target city generated in S4 through a large-scale desktop 3D printing device and a drawing data output device; the interactive model described in step S4 is used to select the printing range and printing angle, and the selected three-dimensional texture model of the city is integrated using a data integration and transfer device, including two types of plan view and three-dimensional model. The selected model is 3D printed through a large-scale desktop 3D printing device, and the model scale is 1:10000. The printed white model is colored by coloring software; the plan view of the model is input through the drawing data output device, and the printing scale is 1:10000.

[0030] Beneficial effects of the present invention:

[0031] 1. Collection efficiency: The present invention constructs an air-ground dual-dimensional urban data collection device equipped with a millimeter-wave radar, and utilizes the strong penetration and high resolution of the millimeter-wave radar to achieve high-precision and high-effective data collection at the urban scale, solving the problems of urban data collection being greatly affected by weather and low resolution of collected data in the past. The air-ground dual-dimensional data collection device can efficiently collect urban building and open space data at one time, effectively avoiding the problems of data loss and repeated collection and supplementation of data.

[0032] 2. Objectivity of the process: The present invention divides and calculates the three-dimensional information of the space by spatial triangular surfaces, generates the three-dimensional texture of the building for the building entity, constructs spatial triangular surfaces for the open space virtual body, and generates the three-dimensional texture of the open space. In this process, the data obtained by millimeter-wave radar scanning and the height obtained by intelligent measurement together constitute the three-dimensional texture data of the city. Each step of data acquisition and result measurement is objective and reliable.

[0033] 3. Interactive visibility: The present invention constructs a three-dimensional digital sand table and cooperates with a handheld controller to display the three-dimensional texture model of the city in a virtual scene, and uses VR glasses and virtual reality gloves to enable users to interact with the model in a scene-based manner, thereby realizing the visualization and interactivity of the three-dimensional texture model of the city. BRIEF DESCRIPTION OF THE DRAWINGS

[0034] Figure 1 is a flow chart of the method of the present invention;

[0035] Figure 2 It is a flowchart for automatic recognition of urban three-dimensional texture. DETAILED DESCRIPTION

[0036] 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.

[0037] like Figure 1 and 2 As shown, a method for automatic recognition and modeling of urban three-dimensional texture based on millimeter wave radar includes the following steps:

[0038] S1 Intelligent collection of urban three-dimensional texture data: Build an air-ground dual-dimensional data intelligent collection device equipped with millimeter-wave radar to collect two types of real scenes of building entities and open spaces in the target city. The collection dimensions include two types: plane and three-dimensional; and translate the air and ground data and store them together in a unified data set to construct the basic data set of the target city;

[0039] In the step S1, an air-ground dual-dimensional data intelligent collection device equipped with a millimeter-wave radar with a transmission frequency of 77 Hz, a ranging range of 50 m, and a ranging angle of 60° is built. The device is mounted on a drone during aerial collection, and is mounted around vehicles and buildings during ground collection to collect real scenes of the target city. The collected data types include two types: building entities and open spaces, and the collection dimensions include two types: plane and three-dimensional. The air and ground data are translated and stored together in a unified data set NAS device to construct a basic data set of the target city. Among them, the plane data includes the perimeter, area, spatial coordinates, and fifth facade photos of the building entity; the length, width, spatial coordinates, and plane views of the open space; the three-dimensional data includes the height of the building entity and the facade photos of the building.

[0040] In step S1, the aerial equipment uses a millimeter-wave radar drone, and the ground equipment uses a millimeter-wave radar scanner fixed on a vehicle and a millimeter-wave radar sensor installed around buildings (structures). The specific functions of the millimeter-wave radar drone are to collect point cloud data of urban high-rise and super-high-rise buildings and take photos of building facades, the millimeter-wave radar scanner fixed on a vehicle is to collect point cloud data of urban single-story and multi-story buildings and structures of 3m and above, and the millimeter-wave radar sensors installed around buildings (structures) are to collect point cloud data of structures below 3m, plants and decorations in open space virtual bodies.

[0041] S2 Automatic recognition of urban 3D texture: Call the target city basic data set constructed in step S1, generate urban building 3D texture for building entities, rasterize the open space virtual body and construct the spatial triangular surface of each grid, calculate the height of each grid with the millimeter wave radar intelligent ranging equipment, generate urban open space 3D texture, and store the generated urban building 3D texture and urban open space 3D texture into the target city 3D texture data set;

[0042] The step S2 specifically includes:

[0043] Step S2-1: Retrieve the perimeter and height information of the target city basic data set constructed in step S1, and divide the three-dimensional urban texture into building entities and open space virtual bodies; wherein the building entity must simultaneously meet the requirements of being a quadrilateral, having a shape completeness greater than 50%, and having a height greater than 3m;

[0044] Step S2-2: for the building entity, call the building height information in the target city basic data set in step S1, generate the three-dimensional texture of the city building, and store it in the target city three-dimensional texture data set;

[0045] Step S2-3: For the open space virtual body, divide the open space plane into 10m×10m grids, rename each grid in sequence as "open space-n"; where "n" represents the number sequence, and delete the grids with an area less than 10m×10m;

[0046] Step S2-4: Use an intelligent laser ranging device equipped with a millimeter-wave radar with a transmission frequency of 77 GHz, a ranging range of 40 m, and a side angle range of 150° to scan the vertices and midpoints of the four sides of the grid generated in step S2-3, and construct spatial triangular faces of 8 points respectively, and name them in sequence as "n spatial triangular face-m", where "n" is consistent with the grid number, and "m" represents the number sequence number;

[0047] Step S2-5: Retrieve the target city building plan and height data obtained in step S1, calculate the open space height in combination with the grid space triangle surface constructed in step S2-4, generate the three-dimensional texture of the urban open space, and store it in the target city three-dimensional texture dataset.

[0048] The spatial triangles of each grid are constructed by connecting the midpoint of the grid, the top edge of the roof of the adjacent building, and the vertex of the bottom edge of the ground of the adjacent building to obtain 8 "spatial triangles"; wherein the adjacent building refers to a building entity with a width of less than or equal to 40m between the bottom edge of the building and the bottom edge of the open space; the building edge refers to the line connecting the top edge of the roof of the adjacent building and the vertex of the bottom edge of the ground, that is, the edge of the "spatial triangle" coincides with a certain facade of the building.

[0049] The method of calculating the height of each grid by combining the millimeter wave radar intelligent ranging device means taking the line connecting the four corners of the grid as the midpoint of the grid, and calculating the height of each grid at the midpoint S M Use an intelligent outdoor laser ranging device equipped with a millimeter-wave radar with a transmission frequency of 77GHz, a ranging range of 40m, and a side angle range of 150° to draw a vertical line to the surrounding buildings, and record the vertical point as the building point S A , measure the distance from the grid midpoint to the building point; then draw a vertical line from the building point to the building facade, and record the intersection of the vertical line and the top edge of the building as the building top edge point S B ; Measure building point S A To the top edge point S of the building B The distance is recorded as the building point height H AB , grid height Where m∈(1,8).

[0050] S3 Automatic modeling of urban 3D texture: Call the urban 3D texture data set calculated in step S2, uniformly translate different types of data into 3D space coordinates, call the image data in the urban basic data set in step S1, use the target detection algorithm to automatically identify the 3D texture plane and facade element information, and unify the coordinates of all data, and generate a 3D real scene of the urban 3D texture identified in step S2 through a laser VR scanner;

[0051] The step S3 specifically includes:

[0052] Step S3-1 calls the urban three-dimensional texture data set calculated in step S2 to collect the building entity height of the urban building three-dimensional texture and the open space height of the urban open space three-dimensional texture, and uniformly translates the building entity height data and the open space height data into three-dimensional space coordinates;

[0053] Step S3-2 calls the fifth facade image of the building entity, the open space plane photo, and the building (structure) facade photo data in the urban basic data set of step S1, and uses the R-FCN target detection algorithm to automatically identify the image data; the recognition process includes, first preprocessing the photo to eliminate irrelevant information; importing the preprocessed image into the trained ResNet-101 classification network; there are 3 cognitions on the feature map obtained by the last convolutional layer of the pre-trained network, the first branch performs RPN operation on the feature map to obtain the corresponding ROI, the second branch obtains the K*K*(C+1)-dimensional position sensitive score map for classification on the feature map, and the third branch obtains the 4*K*K-dimensional position sensitive score map for regression on the feature map; position-sensitive average pooling operations are performed on the K*K*(C+1)-dimensional position sensitive score map and the 4*K*K-dimensional position sensitive score map to obtain corresponding categories and position information; the recognition content includes three-dimensional texture plane and facade element information; the recognition result is the coordinate information and spatial relationship information of the image;

[0054] Step S3-3 unifies the coordinate data of steps S3-1 and S3-2 into the WGS 84 coordinate system;

[0055] In step S3-4, the spatial coordinates after the unified coordinate system in step S3-3 and the point cloud data obtained by the millimeter wave radar scanning in step S1 are processed to remove overlapping points and contradictory points, and a laser VR scanner is used to generate a three-dimensional real scene of the three-dimensional texture of the city.

[0056] S4: Achievement interaction: Using a handheld controller to display a virtual scene of the three-dimensional urban texture model in step S3, and using VR glasses and virtual reality gloves to achieve scene interaction with the model;

[0057] The result display in step S4 uses a projector with a resolution of 4K or above, a depth sensor, a motion sensor, a touch sensor, and a sand table to construct a three-dimensional digital sand table, and cooperates with a handheld controller to perform a virtual scene display of the three-dimensional texture model of the city in step S3, and uses VR glasses and virtual reality gloves to achieve scene interaction with the model; wherein, an in-depth experience of the model is achieved through the handheld controller; the selection, scaling, and modification of the model are achieved through the virtual reality gloves; the interactive display of the model is achieved through the VR glasses, and the model can be operated together with the virtual reality gloves.

[0058] S5 Results printing: Print the three-dimensional texture model of the target city generated in step S4 through large-scale desktop 3D printing equipment and drawing data output equipment; the printing includes two types: plan view and three-dimensional model, and the three-dimensional model is colored using coloring software.

[0059] The result printing in step S5 is to print the three-dimensional texture model of the target city generated in S4 through a large-scale desktop 3D printing device and a drawing data output device; the interactive model described in step S4 is used to select the printing range and printing angle, and the selected three-dimensional texture model of the city is integrated using a data integration and transfer device, including two types of plan view and three-dimensional model. The selected model is 3D printed through a large-scale desktop 3D printing device, and the model scale is 1:10000. The printed white model is colored by coloring software; the plan view of the model is output through the drawing data output device, and the printing scale is 1:10000.

Claims

1. A method for automatic recognition and modeling of urban three-dimensional texture based on millimeter wave radar, characterized in that: The following steps are involved: S1 Intelligent collection of urban three-dimensional texture data: Build an air-ground dual-dimensional data intelligent collection device equipped with millimeter-wave radar to collect two types of real scenes of building entities and open spaces in the target city. The collection dimensions include two types: plane and three-dimensional; and translate the air and ground data and store them together in a unified data set to construct the basic data set of the target city; S2 Automatic recognition of urban 3D texture: Call the target city basic data set constructed in step S1, generate urban building 3D texture for building entities, rasterize the open space virtual body and construct the spatial triangular surface of each grid, calculate the height of each grid with the millimeter wave radar intelligent ranging equipment, generate urban open space 3D texture, and store the generated urban building 3D texture and urban open space 3D texture into the target city 3D texture data set; S3 Automatic modeling of urban 3D texture: Call the urban 3D texture data set calculated in step S2, uniformly translate different types of data into 3D space coordinates, call the image data in the urban basic data set in step S1, use the target detection algorithm to automatically identify the 3D texture plane and facade element information, and unify the coordinates of all data, and generate a 3D real scene of the urban 3D texture identified in step S2 through a laser VR scanner; S4: Using a handheld controller to display a virtual scene of the three-dimensional urban texture model in step S3, and using VR glasses and virtual reality gloves to achieve scene interaction with the model; S5 Results printing: Print the three-dimensional texture model of the target city generated in step S4 through large-scale desktop 3D printing equipment and drawing data output equipment; the printing includes two types: plan view and three-dimensional model, and the three-dimensional model is colored using coloring software.

2. The method for automatic recognition and modeling of urban three-dimensional texture based on millimeter wave radar according to claim 1 is characterized in that: In the step S1, an air-ground dual-dimensional data intelligent collection device equipped with a millimeter-wave radar with a transmission frequency of 77 Hz, a ranging range of 50 m, and a ranging angle of 60° is built. The device is mounted on a drone during aerial collection, and is mounted around vehicles and buildings during ground collection to collect real scenes of the target city. The collected data types include two types: building entities and open spaces, and the collection dimensions include two types: plane and three-dimensional. The air and ground data are translated and stored together in a unified data set NAS device to construct a basic data set of the target city. Among them, the plane data includes the perimeter, area, spatial coordinates, and fifth facade photos of the building entity; the length, width, spatial coordinates, and plane views of the open space; the three-dimensional data includes the height of the building entity and the facade photos of the building.

3. The method for automatic recognition and modeling of urban three-dimensional texture based on millimeter wave radar according to claim 2 is characterized in that: In step S1, the aerial equipment uses a millimeter-wave radar drone, and the ground equipment uses a millimeter-wave radar scanner fixed on a vehicle and a millimeter-wave radar sensor installed around a building. The specific functions are that the millimeter-wave radar drone is used to collect point cloud data of high-rise and super-high-rise buildings in the city and take photos of the facades of the buildings. The millimeter-wave radar scanner fixed on the vehicle is used to collect point cloud data of single-story and multi-story buildings in the city and structures of 3m and above. The millimeter-wave radar sensors installed around the buildings are used to collect point cloud data of structures below 3m, plants and decorations in the virtual body of open spaces.

4. The method for automatic recognition and modeling of urban three-dimensional texture based on millimeter wave radar according to claim 3 is characterized in that: The step S2 specifically includes: Step S2-1: Retrieve the perimeter and height information of the target city basic data set constructed in step S1, and divide the three-dimensional urban texture into building entities and open space virtual bodies; wherein the building entity must simultaneously meet the requirements of being a quadrilateral, having a shape completeness greater than 50%, and having a height greater than 3m; Step S2-2: for the building entity, call the building height information in the target city basic data set in step S1, generate the three-dimensional texture of the city building, and store it in the target city three-dimensional texture data set; Step S2-3: For the open space virtual body, divide the open space plane into 10m×10m grids, rename each grid in sequence as "open space-n"; "n" represents the number sequence, and delete the grids with an area less than 10m×10m; Step S2-4: Use an intelligent laser ranging device equipped with a millimeter-wave radar with a transmission frequency of 77 GHz, a ranging range of 40 m, and a side angle range of 150° to scan the vertices and midpoints of the four sides of the grid generated in step S2-3, and construct spatial triangular faces of 8 points respectively, and name them in sequence as "n spatial triangular face-m"; where "n" is consistent with the grid number, and "m" represents the number sequence number; Step S2-5: Retrieve the target city building plan and height data obtained in step S1, calculate the open space height in combination with the grid space triangle surface constructed in step S2-4, generate the three-dimensional texture of the urban open space, and store it in the target city three-dimensional texture dataset.

5. The method for automatic recognition and modeling of urban three-dimensional texture based on millimeter wave radar according to claim 4 is characterized in that: The spatial triangle facets of each grid are constructed by connecting the grid midpoint, the top edge of the roof of the adjacent building, and the vertex of the bottom edge of the ground of the adjacent building to obtain 8 "spatial triangle faces"; wherein the adjacent building refers to a building entity whose width between the bottom edge of the building and the bottom edge of the open space is less than or equal to 40m; the building edge refers to the line connecting the top edge of the roof of the adjacent building and the vertex of the bottom edge of the ground, that is, the edge of the "spatial triangle face" coincides with a certain facade of the building.

6. The method for automatic recognition and modeling of urban three-dimensional texture based on millimeter wave radar according to claim 5 is characterized in that: The method of calculating the height of each grid by combining the millimeter wave radar intelligent ranging device means taking the line connecting the four corners of the grid as the midpoint of the grid, and calculating the height of each grid at the midpoint S M Use an intelligent outdoor laser ranging device equipped with a millimeter-wave radar with a transmission frequency of 77GHz, a ranging range of 40m, and a side angle range of 150° to draw a vertical line to the surrounding buildings, and record the vertical point as the building point S A , measure the distance from the grid midpoint to the building point; then draw a vertical line from the building point to the building facade, and record the intersection of the vertical line and the top edge of the building as the building top edge point S B ; Measure building point S A To the top edge point S of the building B The distance is recorded as the building point height H AB , grid height Where m∈(1,8).

7. The method for automatic recognition and modeling of urban three-dimensional texture based on millimeter wave radar according to claim 6 is characterized in that: The step S3 specifically includes: Step S3-1 calls the urban three-dimensional texture data set calculated in step S2 to collect the building entity height of the urban building three-dimensional texture and the open space height of the urban open space three-dimensional texture, and uniformly translates the building entity height data and the open space height data into three-dimensional space coordinates; Step S3-2 calls the fifth facade image of the building entity, the open space plane photo, and the building facade photo data in the city basic data set of step S1, and uses the R-FCN target detection algorithm to automatically identify the image data; the recognition process includes, first preprocessing the photo to eliminate irrelevant information; importing the preprocessed image into the trained ResNet-101 classification network; there are 3 cognitions on the feature map obtained by the last convolutional layer of the pre-trained network, the first branch performs RPN operation on the feature map to obtain the corresponding ROI, the second branch obtains the K*K*(C+1)-dimensional position sensitive score map for classification on the feature map, and the third branch obtains the 4*K*K-dimensional position sensitive score map for regression on the feature map; position-sensitive average pooling operations are performed on the K*K*(C+1)-dimensional position sensitive score map and the 4*K*K-dimensional position sensitive score map to obtain corresponding categories and position information; the recognition content includes three-dimensional texture plane and facade element information; the recognition result is the coordinate information and spatial relationship information of the image; Step S3-3 unifies the coordinate data of steps S3-1 and S3-2 into the WGS 84 coordinate system; In step S3-4, the spatial coordinates after the unified coordinate system in step S3-3 and the point cloud data obtained by the millimeter wave radar scanning in step S1 are processed to remove overlapping points and contradictory points, and a laser VR scanner is used to generate a three-dimensional real scene of the three-dimensional texture of the city.

8. The method for automatic recognition and modeling of urban three-dimensional texture based on millimeter wave radar according to claim 7, characterized in that: The result display in step S4 uses a projector with a resolution of 4K or above, a depth sensor, a motion sensor, a touch sensor, and a sand table to construct a three-dimensional digital sand table, and cooperates with a handheld controller to perform a virtual scene display of the three-dimensional texture model of the city in step S3, and uses VR glasses and virtual reality gloves to achieve scene interaction with the model; wherein, an in-depth experience of the model is achieved through the handheld controller; the selection, scaling, and modification of the model are achieved through the virtual reality gloves; the interactive display of the model is achieved through the VR glasses, and the model can be operated together with the virtual reality gloves.

9. The method for automatic recognition and modeling of urban three-dimensional texture based on millimeter wave radar according to claim 8, characterized in that: The result printing in step S5 is to print the three-dimensional texture model of the target city generated in S4 through a large-scale desktop 3D printing device and a drawing data output device; the interactive model described in step S4 is used to select the printing range and printing angle, and the selected three-dimensional texture model of the city is integrated using a data integration and transfer device, including two types of plan view and three-dimensional model. The selected model is 3D printed through a large-scale desktop 3D printing device, and the model scale is 1:10000. The printed white model is colored by coloring software; the plan view of the model is output through the drawing data output device, and the printing scale is 1:10000.

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