Urban live-action three-dimensional model modeling method and system

Abstract

The invention provides an urban live-action three-dimensional model modeling method and system, and relates to the technical field of three-dimensional modeling. The method comprises the following steps: acquiring point cloud data of a city; acquiring meteorological data of a plurality of regions of the city at the current moment; determining a surface reflectance dynamic correction coefficient according to the meteorological data; radar detectors are arranged in a plurality of road areas of a city, and traffic flow data at the current moment are obtained; determining the dynamic texture density of the vehicle according to the traffic flow data; determining a rendering distribution factor according to the surface reflectance dynamic correction coefficient and the vehicle dynamic texture density; and according to the rendering distribution factor and the point cloud data, constructing an urban live-action three-dimensional model. According to the method, the rendering resources of the urban live-action three-dimensional model can be reasonably distributed according to the meteorological data and the traffic flow data, the surface reflectance dynamic correction coefficient and the vehicle dynamic texture density are dynamically adjusted, the visual reality sense of the model is improved, and the dynamic performance capability of the model is enhanced.

Description

Technical Field

[0001] The present invention relates to the technical field of three-dimensional modeling, and in particular to a method and system for modeling a three-dimensional model of a real urban scene. Background Art

[0002] Although current related technologies can construct three-dimensional models, they do not consider the impact of meteorological conditions and traffic conditions on the allocation of rendering resources. In other words, it is impossible to reasonably allocate rendering resources for real-life three-dimensional models of cities based on meteorological data and traffic flow data. Summary of the Invention

[0003] The present invention provides a method and system for modeling a real-scene three-dimensional model of a city, which can solve the technical problem in related technologies that it is impossible to reasonably allocate rendering resources for the real-scene three-dimensional model of the city based on meteorological data and traffic flow data.

[0004] According to a first aspect of the present invention, a method for modeling a three-dimensional model of a real city is provided, comprising: acquiring point cloud data of a city through laser radar scanning; acquiring meteorological data of multiple areas in the city at the current moment, wherein the meteorological data include light intensity, precipitation intensity and wind speed; determining a dynamic correction coefficient of surface reflectivity based on the meteorological data; setting up radar detectors in multiple road areas in the city to acquire traffic flow data at the current moment, wherein the traffic flow data includes vehicle density and vehicle speed; determining vehicle dynamic texture density based on the traffic flow data; determining a rendering allocation factor based on the dynamic correction coefficient of surface reflectivity and the vehicle dynamic texture density; and constructing a three-dimensional model of the real city based on the rendering allocation factor and the point cloud data.

[0005] Furthermore, based on the meteorological data, a dynamic correction coefficient of the surface reflectivity is determined, including: setting a benchmark light intensity; determining the precipitation level based on the precipitation intensity; and determining the dynamic correction coefficient of the surface reflectivity based on the light intensity, the wind speed, the precipitation level and the benchmark light intensity.

[0006] Furthermore, determining the surface reflectivity dynamic correction coefficient according to the light intensity, the wind speed, the precipitation level and the reference light intensity includes: according to the formula Determine the dynamic correction coefficient of the surface reflectivity of the i-th region at the current moment ,in, is the light intensity of the i-th region at the current moment, is the baseline light intensity, is the precipitation level of the i-th region at the current moment, is the wind speed of the ith region at the current moment, is the preset weight, min is the minimum function, and i is a positive integer.

[0007] Furthermore, based on the traffic flow data, determining the vehicle dynamic texture density includes: obtaining the number of lanes and road speed limit data of multiple road areas; averaging the vehicle speeds of multiple vehicles in each road area at the current moment based on the traffic flow data to obtain an average vehicle speed; and determining the vehicle dynamic texture density based on the number of lanes, the road speed limit data, the average vehicle speed and the vehicle density.

[0008] Further, according to the number of lanes, the road speed limit data, the average vehicle speed and the vehicle density, determining the vehicle dynamic texture density includes: according to the formula Determine the vehicle dynamic texture density of the jth road area at the current moment ,in, is the vehicle density of the jth road area at the current moment, is the number of lanes in the jth road area, is the average vehicle speed of the jth road area at the current moment, is the road speed limit data of the j-th road area, and j is a positive integer.

[0009] Furthermore, a rendering allocation factor is determined based on the dynamic correction coefficient of the surface reflectivity and the dynamic texture density of the vehicle, including: obtaining the dynamic element coverage area and the total scene area of ​​the city; and determining the rendering allocation factor based on the dynamic element coverage area, the total scene area, the dynamic correction coefficient of the surface reflectivity and the dynamic texture density of the vehicle.

[0010] Furthermore, determining the rendering allocation factor according to the dynamic element coverage area, the total scene area, the surface reflectivity dynamic correction coefficient and the vehicle dynamic texture density includes: according to the formula Determine the rendering allocation factor F at the current moment, where is the coverage area of ​​the city’s dynamic elements, is the total area of ​​the city scene, is the dynamic correction coefficient of the surface reflectivity of the i-th region at the current moment, is the vehicle dynamic texture density of the jth road area at the current moment, M is the number of urban areas, N is the number of urban road areas, i≤M, j≤N, and i, j, M and N are all positive integers.

[0011] Furthermore, a three-dimensional model of a real city scene is constructed based on the rendering allocation factor and the point cloud data, including: obtaining three-dimensional data of the real city scene based on the point cloud data; allocating rendering resources to the dynamic real city scene based on the rendering allocation factor to construct a three-dimensional model of the real city scene.

[0012] According to a second aspect of the present invention, a system for modeling a city real-scene three-dimensional model is provided, comprising: a point cloud data module for acquiring point cloud data of a city through laser radar scanning; a meteorological data module for acquiring meteorological data of multiple areas of the city at the current moment, wherein the meteorological data include light intensity, precipitation intensity and wind speed; a surface reflectivity dynamic correction coefficient module for determining the surface reflectivity dynamic correction coefficient based on the meteorological data; a traffic flow data module for setting radar detectors in multiple road areas of the city to acquire traffic flow data at the current moment, wherein the traffic flow data include vehicle density and vehicle speed; a vehicle dynamic texture density module for determining the vehicle dynamic texture density based on the traffic flow data; a rendering allocation factor module for determining the rendering allocation factor based on the surface reflectivity dynamic correction coefficient and the vehicle dynamic texture density; and a three-dimensional model module for constructing a city real-scene three-dimensional model based on the rendering allocation factor and the point cloud data.

[0013] Technical effect: According to the present invention, the dynamic correction coefficient of surface reflectivity can make the model present the appearance of the city more realistically under different meteorological scenarios, thereby enhancing the visual realism of the model. Through the dynamic texture density of vehicles, the model can reflect the actual traffic conditions of different roads in the city, thereby enhancing the dynamic performance ability of the model. The rendering allocation factor can reasonably allocate rendering resources according to the changes in surface reflectivity and traffic conditions. The point cloud data can accurately record the spatial position and shape information of urban objects. Combined with the rendering allocation factor, a real-life three-dimensional model of the city with high-precision geometric shapes and realistic visual effects can be constructed. When determining the dynamic correction coefficient of surface reflectivity, the relationship between light intensity, precipitation level, wind speed and the dynamic correction coefficient of surface reflectivity can be used to determine the dynamic correction coefficient of surface reflectivity, which can more accurately describe the actual changes in surface reflectivity in different regions. By introducing the nonlinear coupling effect of precipitation level and wind speed and quantifying the influence of wind speed on precipitation attachment, the optical properties of urban wet surfaces in different weather conditions can be more accurately simulated. When determining vehicle dynamic texture density, the speed weighting factor can be used to automatically adjust the vehicle dynamic texture density under different traffic conditions, reducing texture overload in low-speed congestion scenarios, saving computing resources, enabling the model to better reflect the dynamic changes in urban traffic, and enhancing the model's dynamic performance capabilities. The rendering allocation factor can be determined by considering the dynamic feature coverage area, the total scene area, the dynamic surface reflectivity correction coefficient, and the vehicle dynamic texture density. This adjusts rendering resource allocation based on the impact of weather changes and traffic conditions on rendering load, optimizing rendering priority in dynamic areas, and improving rendering efficiency in complex urban scenes, thereby enhancing the accuracy and comprehensiveness of the rendering allocation factor. BRIEF DESCRIPTION OF THE DRAWINGS

[0014] Figure 1 A schematic diagram exemplarily illustrates a flow chart of a method for modeling a city real scene 3D model according to an embodiment of the present invention; Figure 2 A flowchart of calculating a dynamic correction coefficient of surface reflectivity according to an embodiment of the present invention is exemplarily shown; Figure 3 A flowchart of calculating vehicle dynamic texture density according to an embodiment of the present invention is exemplarily shown; Figure 4 A flowchart of calculating a rendering allocation factor according to an embodiment of the present invention is exemplarily shown; Figure 5 The following is a flowchart of constructing a real-scene 3D model of a city according to an embodiment of the present invention; Figure 6 A block diagram of a system for building a city real scene 3D model according to an embodiment of the present invention is exemplarily shown. DETAILED DESCRIPTION

[0015] To make the objectives, technical solutions, and advantages of the embodiments of the present invention more clear, the technical solutions in the embodiments of the present invention will be clearly and completely described below in conjunction with the accompanying drawings 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 making creative efforts shall fall within the scope of protection of the present invention.

[0016] The following specific embodiments are used to describe the technical solution of the present invention in detail. The following specific embodiments can be combined with each other, and the same or similar concepts or processes may not be described in detail in some embodiments.

[0017] Figure 1 The following is a flow chart of a method for modeling a real-scene 3D model of a city according to an embodiment of the present invention, wherein the method includes: Step S1, obtaining point cloud data of the city through laser radar scanning; Step S2, obtaining meteorological data of multiple areas in the city at the current moment, wherein the meteorological data includes light intensity, precipitation intensity and wind speed; Step S3, determining a dynamic correction coefficient of surface reflectivity based on the meteorological data; Step S4: installing radar detectors in multiple road areas in the city to obtain traffic flow data at the current moment, wherein the traffic flow data includes vehicle density and vehicle speed; Step S5, determining the vehicle dynamic texture density based on the traffic flow data; Step S6, determining a rendering allocation factor according to the surface reflectivity dynamic correction coefficient and the vehicle dynamic texture density; Step S7: constructing a city real scene three-dimensional model according to the rendering allocation factor and the point cloud data.

[0018] The method for modeling a realistic 3D urban model according to an embodiment of the present invention utilizes a dynamic surface reflectivity correction coefficient to more realistically represent the city's appearance in different weather scenarios, enhancing the model's visual realism. The dynamic vehicle texture density allows the model to reflect actual traffic conditions on different city roads, thereby enhancing the model's dynamic performance. The rendering allocation factor rationally allocates rendering resources based on changes in surface reflectivity and traffic conditions. Point cloud data accurately records the spatial position and shape information of urban objects. Combined with the rendering allocation factor, a realistic 3D urban model with high-precision geometry and realistic visual effects can be constructed.

[0019] According to one embodiment of the present invention, in step S1, the appropriate type of LiDAR equipment is selected based on the city modeling requirements and scenario characteristics. For example, for scanning a large urban area, an airborne LiDAR might be chosen, as it can quickly cover a large area. For detailed scanning of specific buildings or complex terrain within a city, a terrestrial LiDAR or vehicle-mounted LiDAR might be used. Point cloud data can accurately reflect the characteristics of urban topography and buildings.

[0020] According to one embodiment of the present invention, in step S2, there are multiple areas in the city, and a meteorological monitoring station is deployed in each area to accurately reflect the meteorological conditions of the area, that is, the light intensity is monitored by a light sensor, the precipitation intensity is monitored by a tipping bucket rain sensor, and the wind speed is monitored by an ultrasonic wind speed sensor.

[0021] According to one embodiment of the present invention, in step S3, a dynamic correction coefficient of surface reflectivity is determined based on the meteorological data.

[0022] Figure 2 A flow chart for calculating a dynamic correction coefficient of surface reflectivity according to an embodiment of the present invention is exemplarily shown.

[0023] According to one embodiment of the present invention, step S3 includes: step S31, setting a benchmark light intensity; step S32, determining a precipitation level based on the precipitation intensity; step S33, determining a dynamic correction coefficient of surface reflectivity based on the light intensity, the wind speed, the precipitation level and the benchmark light intensity.

[0024] According to one embodiment of the present invention, the reference light intensity can be set to 1000W / m². If the precipitation intensity is above 8mm / h, the precipitation level is 1; if the precipitation intensity is greater than 6mm / h and less than or equal to 8mm / h, the precipitation level is 0.8; if the precipitation intensity is greater than 4mm / h and less than or equal to 6mm / h, the precipitation level is 0.6; if the precipitation intensity is greater than 2mm / h and less than or equal to 4mm / h, the precipitation level is 0.4; if the precipitation intensity is greater than 0.1mm / h and less than or equal to 2mm / h, the precipitation level is 0.2; if the precipitation intensity is greater than 0mm / h and less than or equal to 0.1mm / h, the precipitation level is 0. Based on meteorological data, the dynamic correction coefficient of surface reflectivity under different weather conditions is calculated to simulate the light scattering characteristics of a wet surface. The greater the light intensity, the higher the surface reflectivity; the greater the precipitation level and the greater the surface wetness, the more significant the reflectivity correction. Strong winds accelerate the evaporation of surface water and disturb the distribution of water film, resulting in enhanced specular reflection. For example, when heavy rain is accompanied by strong winds, the water film on the ground is uneven and the reflectivity requires a higher correction.

[0025] According to one embodiment of the present invention, the dynamic correction coefficient of the surface reflectivity is determined according to the light intensity, the wind speed, the precipitation level and the reference light intensity, including: determining the dynamic correction coefficient of the surface reflectivity of the i-th region at the current moment according to formula (1): , (1), in, is the light intensity of the i-th region at the current moment, is the baseline light intensity, is the precipitation level of the i-th region at the current moment, is the wind speed of the ith region at the current moment, is the preset weight, min is the minimum function, and i is a positive integer.

[0026] According to one embodiment of the present invention, in formula (1), is the ratio of the current light intensity of the ith region to the reference light intensity, reflecting the basic influence of direct sunlight on the dynamic correction coefficient of surface reflectivity, that is, the light intensity influence term. For example, when the light is strong (such as at noon), the ratio approaches 1 or even exceeds 1, and when the light is weak (such as on a cloudy day), the ratio decreases. The greater the precipitation level in the ith region at the current moment, the greater the surface moisture, and the greater the need for surface reflectivity correction. It represents the nonlinear saturation effect of wind speed on precipitation, that is, the attenuation factor of wind speed. The greater the wind speed, the closer the impact on precipitation is to 1. Wind speed affects the state of precipitation on the surface. For example, wind speed accelerates evaporation and blows the surface water film during precipitation to form ripples, increasing scattering. On sunny days, that is, , the surface reflectivity is determined only by the light intensity. When it rains, the surface reflectivity correction term It increases with the precipitation level and wind speed, simulating the light scattering enhancement effect of the wet surface, that is, the precipitation-wind speed coupling effect, such as the specular reflection of the water surface and the diffuse reflection of the asphalt road surface. (e.g. 0.3) is the preset weight for regulating the coupling effect between precipitation and wind speed. Represents the influence of light intensity plus the precipitation-wind speed coupling influence. Indicates taking 1 and The smaller value of the two ensures that the dynamic correction coefficient of the surface reflectivity does not exceed the physical upper limit of 1 (that is, complete reflection and no absorption).

[0027] This approach allows the dynamic surface reflectivity correction coefficient to be determined based on the relationship between light intensity, precipitation level, wind speed, and the dynamic surface reflectivity correction coefficient, providing a more accurate description of the actual variations in surface reflectivity across different regions. By incorporating the nonlinear coupling effect of precipitation level and wind speed and quantifying the impact of wind speed on precipitation attachment, the optical properties of wet urban surfaces under varying weather conditions can be more accurately simulated.

[0028] According to one embodiment of the present invention, in step S4, radar detectors are installed in multiple road areas in the city (for example, main roads, secondary roads, and branch roads), which can emit microwave signals and receive reflected waves. The vehicle speed is calculated through the Doppler effect, and the vehicle density can be calculated by counting the number of vehicles passing through the radar detector per unit time and combining it with the road section length.

[0029] According to an embodiment of the present invention, in step S5, the vehicle dynamic texture density is determined based on the traffic flow data.

[0030] Figure 3 The flowchart of calculating the vehicle dynamic texture density according to an embodiment of the present invention is exemplarily shown.

[0031] According to one embodiment of the present invention, step S5 includes: step S51, obtaining the number of lanes and road speed limit data of multiple road areas; step S52, averaging the vehicle speeds of multiple vehicles in each road area at the current moment based on the traffic flow data to obtain an average vehicle speed; step S53, determining the vehicle dynamic texture density based on the number of lanes, the road speed limit data, the average vehicle speed and the vehicle density.

[0032] According to one embodiment of the present invention, lane counts and speed limit data for multiple road areas can be obtained using data such as basic databases from traffic management departments. For each road area, the current speeds of multiple vehicles are averaged to obtain an average speed. Vehicle density reflects the number of vehicles per unit length of road, the number of lanes reflects the upper limit of the road's capacity, and vehicle speed reflects the efficiency of traffic flow. Vehicle dynamic texture density is a comprehensive indicator of the dynamic characteristics of traffic flow on a road, dynamically reflecting the congestion level and dynamic changes in traffic flow. For example, during peak hours in the morning and evening, as vehicle density increases and average speed decreases, the vehicle dynamic texture density will increase accordingly, indicating increased road congestion. During off-peak hours, as vehicle density decreases and average speed increases, the vehicle dynamic texture density will decrease, indicating good road conditions. Vehicle dynamic texture density can promptly and accurately reflect real-time changes in road traffic conditions.

[0033] According to one embodiment of the present invention, the vehicle dynamic texture density is determined based on the number of lanes, the road speed limit data, the average vehicle speed and the vehicle density, including: determining the vehicle dynamic texture density of the j-th road area at the current moment according to formula (2): , (2), in, is the vehicle density of the jth road area at the current moment, is the number of lanes in the jth road area, is the average vehicle speed of the jth road area at the current moment, is the road speed limit data of the j-th road area, and j is a positive integer.

[0034] According to one embodiment of the present invention, in formula (2), It is the ratio of the vehicle density of the j-th road area at the current moment to the number of lanes in the j-th road area, indicating the vehicle density of a single lane. The ratio of the average vehicle speed of the jth road area at the current moment to the road speed limit data of the jth road area plus 1 represents the speed weight factor, which is used to adjust the impact of the vehicle density of a single lane on the dynamic texture. When the speed is low (for example, in congestion), Much smaller than ), the speed weight factor decreases, the vehicle dynamic texture density increases, and the road congestion in low-speed congestion scenarios is more accurately reflected, reducing the texture overload problem (i.e., underestimation of congestion). When the vehicle speed is high (e.g., when the vehicle is unblocked), near ), the speed weight factor increases and the vehicle dynamic texture density decreases, reflecting the reduction of visual texture caused by the rapid passage of vehicles, which is more in line with human visual perception (focusing on vehicle details at low speeds and focusing on the overall flow state at high speeds).

[0035] In this way, the speed weight factor can be used to automatically adjust the dynamic texture density of vehicles under different traffic conditions, reduce texture overload in low-speed congestion scenarios, save computing resources, enable the model to better reflect the dynamic changes of urban traffic, and enhance the dynamic performance of the model.

[0036] According to an embodiment of the present invention, in step S6, a rendering allocation factor is determined according to the dynamic correction coefficient of the surface reflectivity and the dynamic texture density of the vehicle.

[0037] Figure 4 The flowchart of calculating the rendering allocation factor according to an embodiment of the present invention is exemplarily shown.

[0038] According to one embodiment of the present invention, step S8 includes: step S61, obtaining the dynamic element coverage area and the total scene area of ​​the city; step S62, determining the rendering allocation factor based on the dynamic element coverage area, the total scene area, the surface reflectivity dynamic correction coefficient and the vehicle dynamic texture density.

[0039] According to one embodiment of the present invention, the coverage area of ​​dynamic elements in a city, such as the area occupied by real-time objects like vehicles and weather particle effects, can be obtained from point cloud data to create 3D data of the actual city scene, thereby generating a 3D model. The total scene area is the spatial projection area of ​​the entire 3D model. GPU computing power can be allocated according to the rendering allocation factor to achieve better rendering performance.

[0040] According to one embodiment of the present invention, determining a rendering allocation factor based on the dynamic element coverage area, the total scene area, the surface reflectivity dynamic correction coefficient, and the vehicle dynamic texture density includes: determining the rendering allocation factor F at the current moment according to formula (3), (3), in, is the coverage area of ​​the city’s dynamic elements, is the total area of ​​the city scene, is the dynamic correction coefficient of the surface reflectivity of the i-th region at the current moment, is the vehicle dynamic texture density of the jth road area at the current moment, M is the number of urban areas, N is the number of urban road areas, i≤M, j≤N, and i, j, M and N are all positive integers.

[0041] According to one embodiment of the present invention, in formula (3), It is the ratio of the coverage area of ​​the city's dynamic elements to the total area of ​​the city's scene, that is, the initial rendering allocation ratio. The larger the ratio, the more GPU computing power is allocated. The average result of the square root of the dynamic surface reflectance correction coefficient for multiple regions at the current moment indicates the impact of weather on the rendering load. The larger the result, the greater the impact of weather on the rendering load. Heavy rain and strong winds can increase surface reflectance, and changes in lighting require adjustment of shadows. The average result of the square root of the dynamic surface reflectance correction coefficients for multiple regions at the current moment represents the impact of traffic flow on the rendering load. The larger the result, the greater the impact of traffic flow on the rendering load, and high-density traffic requires more detailed rendering. It is a correction item for weather and traffic flow, which is used to adjust the initial rendering allocation ratio according to the dynamic correction coefficient of surface reflectivity and the dynamic texture density of vehicles. larger) or high traffic density ( The larger the value, the larger the rendering allocation factor will be. More GPU computing power will be allocated to render the dynamic area first. The dynamic area is the area covered by dynamic elements. It is the ratio of the coverage area of ​​the city's dynamic elements to the total area of ​​the city's scenes, multiplied by the correction terms of weather and traffic flow, which represents the rendering allocation factor. The larger the rendering allocation factor, the more rendering resources the dynamic area obtains.

[0042] In this way, the rendering allocation factor can be determined by the coverage area of ​​dynamic elements, the total area of ​​the scene, the dynamic correction coefficient of the surface reflectivity and the dynamic texture density of the vehicle. The rendering resource allocation can be adjusted according to the impact of weather changes and traffic conditions on the rendering load, and the rendering priority of the dynamic area can be optimized, thereby improving the rendering efficiency of complex urban scenes and improving the accuracy and comprehensiveness of the rendering allocation factor.

[0043] According to one embodiment of the present invention, in step S7, a real-scene three-dimensional model of the city is constructed based on the rendering allocation factor and the point cloud data.

[0044] Figure 5 The flowchart of constructing a real-scene 3D model of a city according to an embodiment of the present invention is exemplarily shown.

[0045] According to one embodiment of the present invention, step S7 includes: step S71, obtaining three-dimensional data of the urban real scene based on the point cloud data; step S72, allocating rendering resources to the urban dynamic real scene according to the rendering allocation factor, and constructing a three-dimensional model of the urban real scene.

[0046] According to one embodiment of the present invention, point cloud data can be subjected to feature extraction and classification, thereby being converted into a three-dimensional data model of a real city scene. In the rendering of a three-dimensional model of a real city scene, the performance of the GPU directly affects the speed and quality of the rendering. Therefore, it is necessary to reasonably allocate the computing resources of the GPU according to the rendering allocation factor. The real city scene is dynamically changing, and the distribution and status of dynamic elements at different times may be different. Therefore, it is necessary to update the rendering allocation factor according to real-time data and dynamically adjust the allocation of rendering resources. For example, during rush hour, the number of vehicles on the road increases and the vehicle texture density increases. At this time, it is necessary to increase the allocation of rendering resources in the dynamic area accordingly.

[0047] According to the method for modeling a real-world 3D urban model, a dynamic surface reflectivity correction coefficient is used to more realistically represent the city's appearance under different weather scenarios, enhancing the model's visual realism. The dynamic vehicle texture density allows the model to reflect actual traffic conditions on different city roads, thereby enhancing the model's dynamic performance. A rendering allocation factor rationally allocates rendering resources based on changes in surface reflectivity and traffic conditions. Point cloud data accurately records the spatial position and shape information of urban objects. Combined with the rendering allocation factor, a real-world 3D urban model with high-precision geometry and realistic visual effects can be constructed. The dynamic surface reflectivity correction coefficient is determined by the relationship between light intensity, precipitation level, wind speed, and the dynamic surface reflectivity correction coefficient, enabling a more accurate description of actual variations in surface reflectivity across different regions. By incorporating the nonlinear coupling effect of precipitation level and wind speed, the impact of wind speed on precipitation adhesion is quantified, enabling a more accurate simulation of the optical properties of wet urban surfaces under different weather conditions. When determining vehicle dynamic texture density, the speed weighting factor can be used to automatically adjust the vehicle dynamic texture density under different traffic conditions, reducing texture overload in low-speed congestion scenarios, saving computing resources, enabling the model to better reflect the dynamic changes in urban traffic, and enhancing the model's dynamic performance capabilities. The rendering allocation factor can be determined by considering the dynamic feature coverage area, the total scene area, the dynamic surface reflectivity correction coefficient, and the vehicle dynamic texture density. This adjusts rendering resource allocation based on the impact of weather changes and traffic conditions on rendering load, optimizing rendering priority in dynamic areas, and improving rendering efficiency in complex urban scenes, thereby enhancing the accuracy and comprehensiveness of the rendering allocation factor.

[0048] Figure 6A block diagram of a city real-scene three-dimensional model modeling system according to an embodiment of the present invention is exemplarily shown, wherein the system includes: a point cloud data module, used to obtain point cloud data of a city through laser radar scanning; a meteorological data module, used to obtain meteorological data of multiple areas in the city at the current moment, wherein the meteorological data includes light intensity, precipitation intensity and wind speed; a surface reflectivity dynamic correction coefficient module, used to determine the surface reflectivity dynamic correction coefficient based on the meteorological data; a traffic flow data module, used to set radar detectors in multiple road areas in the city to obtain traffic flow data at the current moment, wherein the traffic flow data includes vehicle density and vehicle speed; a vehicle dynamic texture density module, used to determine the vehicle dynamic texture density based on the traffic flow data; a rendering allocation factor module, used to determine the rendering allocation factor based on the surface reflectivity dynamic correction coefficient and the vehicle dynamic texture density; and a three-dimensional model module, used to construct a city real-scene three-dimensional model based on the rendering allocation factor and the point cloud data.

[0049] The present invention may be a method, an apparatus, a system and / or a computer program product. The computer program product may include a computer-readable storage medium carrying computer-readable program instructions for executing various aspects of the present invention.

[0050] Those skilled in the art will appreciate that the embodiments of the present invention described above and shown in the accompanying drawings are intended to be illustrative only and are not intended to limit the present invention. The objectives of the present invention have been fully and effectively achieved. The functional and structural principles of the present invention have been demonstrated and illustrated in the embodiments. Any variations or modifications may be made to the embodiments of the present invention without departing from the principles described.

Claims

1. A method for modeling a real-scene three-dimensional model of a city, characterized in that: include: Obtain point cloud data of the city through lidar scanning; Obtaining meteorological data for multiple areas of the city at the current moment, wherein the meteorological data includes light intensity, precipitation intensity, and wind speed; determining a dynamic correction coefficient of surface reflectivity based on the meteorological data; Setting up radar detectors in multiple road areas in the city to obtain traffic flow data at the current moment, wherein the traffic flow data includes vehicle density and vehicle speed; determining a vehicle dynamic texture density based on the traffic flow data; determining a rendering allocation factor according to the surface reflectivity dynamic correction coefficient and the vehicle dynamic texture density; A three-dimensional model of a real city scene is constructed according to the rendering allocation factor and the point cloud data.

2. The method for modeling a city real scene three-dimensional model according to claim 1, characterized in that: Determine the dynamic correction coefficient of surface reflectivity based on the meteorological data, including: Setting a reference light intensity; determining a precipitation level according to the precipitation intensity; A dynamic correction coefficient of surface reflectivity is determined according to the light intensity, the wind speed, the precipitation level and the reference light intensity.

3. The method for modeling a city real scene three-dimensional model according to claim 2, characterized in that: Determining a dynamic correction coefficient of surface reflectivity according to the light intensity, the wind speed, the precipitation level, and the reference light intensity includes: According to the formula ； Determine the dynamic correction coefficient of the surface reflectivity of the i-th region at the current moment ,in, is the light intensity of the i-th region at the current moment, is the baseline light intensity, is the precipitation level of the i-th region at the current moment, is the wind speed of the ith region at the current moment, is the preset weight, min is the minimum function, and i is a positive integer.

4. The method for modeling a city real scene three-dimensional model according to claim 1, characterized in that: Determining a vehicle dynamic texture density according to the traffic flow data includes: Get lane count and road speed limit data for multiple road areas; averaging the vehicle speeds of multiple vehicles in each road area at a current moment based on the traffic flow data to obtain an average vehicle speed; The vehicle dynamic texture density is determined according to the number of lanes, the road speed limit data, the average vehicle speed and the vehicle density.

5. The method for modeling a city real scene three-dimensional model according to claim 4, characterized in that: Determining a vehicle dynamic texture density according to the number of lanes, the road speed limit data, the average vehicle speed, and the vehicle density includes: According to the formula ； Determine the vehicle dynamic texture density of the jth road area at the current moment ,in, is the vehicle density of the jth road area at the current moment, is the number of lanes in the jth road area, is the average vehicle speed of the jth road area at the current moment, is the road speed limit data of the j-th road area, and j is a positive integer.

6. The method for modeling a city real scene three-dimensional model according to claim 1, characterized in that: Determining a rendering allocation factor according to the surface reflectivity dynamic correction coefficient and the vehicle dynamic texture density includes: Get the coverage area of ​​the city's dynamic elements and the total area of ​​the scene; A rendering allocation factor is determined according to the dynamic element coverage area, the total area of ​​the scene, the dynamic correction coefficient of the surface reflectivity and the vehicle dynamic texture density.

7. The method for modeling a city real scene three-dimensional model according to claim 6, characterized in that: Determining a rendering allocation factor according to the dynamic element coverage area, the total area of ​​the scene, the surface reflectivity dynamic correction coefficient, and the vehicle dynamic texture density includes: According to the formula ； Determine the rendering allocation factor F at the current moment, where is the coverage area of ​​the city’s dynamic elements, is the total area of ​​the city scene, is the dynamic correction coefficient of the surface reflectivity of the i-th region at the current moment, is the vehicle dynamic texture density of the jth road area at the current moment, M is the number of urban areas, N is the number of urban road areas, i≤M, j≤N, and i, j, M and N are all positive integers.

8. The method for modeling a city real scene three-dimensional model according to claim 1, characterized in that: Constructing a city real scene three-dimensional model according to the rendering allocation factor and the point cloud data, including: According to the point cloud data, three-dimensional data of the city real scene is obtained; according to the rendering allocation factor, rendering resources are allocated to the city dynamic real scene to construct a three-dimensional model of the city real scene.

9. A city real scene 3D modeling system, used to execute the city real scene 3D modeling method according to any one of claims 1 to 8, characterized in that: include: Point cloud data module, used to obtain point cloud data of the city through lidar scanning; A meteorological data module is used to obtain meteorological data of multiple areas in the city at the current moment, wherein the meteorological data includes light intensity, precipitation intensity and wind speed; A surface reflectivity dynamic correction coefficient module, used to determine the surface reflectivity dynamic correction coefficient based on the meteorological data; A traffic flow data module is used to set up radar detectors in multiple road areas in the city to obtain traffic flow data at the current moment, wherein the traffic flow data includes vehicle density and vehicle speed; A vehicle dynamic texture density module, configured to determine a vehicle dynamic texture density based on the traffic flow data; A rendering allocation factor module, configured to determine a rendering allocation factor based on the surface reflectivity dynamic correction coefficient and the vehicle dynamic texture density; The three-dimensional model module is used to construct a three-dimensional model of the city scene according to the rendering allocation factor and the point cloud data.

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