Method and system for modeling urban real scene three-dimensional model

Abstract

The application provides a kind of urban real scene three-dimensional model modeling method and system, it is related to three-dimensional modeling technical field.The method comprises: obtaining the point cloud data of city;Obtain the weather data of multiple regions of city at current time;According to weather data, determine the dynamic correction coefficient of ground reflectivity;In multiple road regions of city, set up radar detector, obtain the traffic flow data of current time;According to traffic flow data, determine the dynamic texture density of vehicle;According to the dynamic correction coefficient of ground reflectivity and the dynamic texture density of vehicle, determine rendering allocation factor;According to the rendering allocation factor and the point cloud data, construct urban real scene three-dimensional model.According to the present application, the rendering resource of urban real scene three-dimensional model can be reasonably allocated according to weather data and traffic flow data, the dynamic correction coefficient of ground reflectivity and the dynamic texture density of vehicle are dynamically adjusted, the visual reality of model is improved, and the dynamic performance capability of model is enhanced.

Description

Technical Field

[0001] This invention relates to the field of 3D modeling technology, and in particular to a method and system for modeling 3D models of urban real scenes. Background Technology

[0002] While current technologies can construct 3D models, they do not consider the impact of weather conditions and traffic conditions on the allocation of rendering resources. In other words, they cannot reasonably allocate rendering resources for urban 3D models based on weather data and traffic flow data. Summary of the Invention

[0003] This invention provides a method and system for modeling 3D urban real-scene models, which can solve the technical problem in related technologies that it is impossible to reasonably allocate rendering resources for 3D urban real-scene models 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 urban real-scene model is provided, comprising: acquiring point cloud data of the city through lidar scanning; acquiring meteorological data of 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 for surface reflectance based on the meteorological data; setting up radar detectors in multiple road areas of the city to acquire traffic flow data at the current moment, wherein the traffic flow data includes vehicle density and vehicle speed; determining a dynamic texture density of vehicles based on the traffic flow data; determining a rendering allocation factor based on the dynamic correction coefficient for surface reflectance and the dynamic texture density of vehicles; and constructing a three-dimensional urban real-scene model based on the rendering allocation factor and the point cloud data.

[0005] Further, based on the meteorological data, the dynamic correction coefficient for surface reflectance is determined, including: setting a reference light intensity; determining the precipitation level based on the precipitation intensity; and determining the dynamic correction coefficient for surface reflectance based on the light intensity, the wind speed, the precipitation level, and the reference light intensity.

[0006] Further, based on the light intensity, the wind speed, the precipitation level, and the reference light intensity, a dynamic correction coefficient for surface reflectance is determined, including: according to the formula... Determine the dynamic correction coefficient for the surface reflectance of the i-th region at the current time. ,in, Let be the light intensity of the i-th region at the current moment. As a reference light intensity, Let be the precipitation level of the i-th region at the current moment. Let be the wind speed in the i-th region at the current moment. The preset weights are defined, min is the minimum value function, and i is a positive integer.

[0007] Further, determining the vehicle dynamic texture density based on the traffic flow data includes: acquiring the number of lanes and road speed limit data for 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 the 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, based on the number of lanes, the road speed limit data, the average vehicle speed, and the vehicle density, the vehicle dynamic texture density is determined, including: according to the formula... Determine the vehicle dynamic texture density of the j-th road region at the current time. ,in, Let J be the vehicle density of the j-th road region at the current time. Let j be the number of lanes in the j-th road region. Let be the average vehicle speed of the j-th road region at the current time. Let j be the speed limit data for the j-th road area, where j is a positive integer.

[0009] Further, the rendering allocation factor is determined based on the dynamic correction coefficient of surface reflectance and the dynamic texture density of vehicles, including: obtaining the dynamic feature coverage area of ​​the city and the total scene area; and determining the rendering allocation factor based on the dynamic feature coverage area, the total scene area, the dynamic correction coefficient of surface reflectance, and the dynamic texture density of vehicles.

[0010] Further, based on the dynamic element coverage area, the total scene area, the dynamic correction coefficient for surface reflectance, and the vehicle dynamic texture density, a rendering allocation factor is determined, including: according to the formula... Determine the rendering allocation factor F at the current moment, where, The coverage area of ​​dynamic elements in the city. The total area of ​​the city scene. Let be the dynamic correction coefficient for the surface reflectance of the i-th region at the current time. Let be the vehicle dynamic texture density of the j-th road region at the current time, M be the number of urban areas, N be the number of urban road regions, i≤M, j≤N, and i, j, M and N are all positive integers.

[0011] Further, constructing a 3D model of the city's real scene based on the rendering allocation factor and the point cloud data includes: obtaining 3D data of the city's real scene based on the point cloud data; allocating rendering resources for the dynamic city scene based on the rendering allocation factor; and constructing a 3D model of the city's real scene.

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

[0013] Technical Effects: According to this invention, by using a dynamic surface reflectance correction coefficient, the model can more realistically present the appearance of the city under different weather scenarios, enhancing the model's visual realism. By using vehicle dynamic texture density, the model can reflect the actual traffic conditions on different city roads, thereby enhancing the model's dynamic performance. The rendering allocation factor can rationally allocate rendering resources based on changes in surface reflectance and traffic conditions. Point cloud data can accurately record the spatial location and shape information of urban objects. Combined with the rendering allocation factor, a high-precision geometric shape and realistic visual effect can be constructed to create a realistic 3D urban scene model. When determining the dynamic surface reflectance correction coefficient, the relationship between light intensity, precipitation level, wind speed, and the dynamic surface reflectance correction coefficient can be used to determine the coefficient, allowing for a more accurate description of the actual changes in surface reflectance in different areas. By introducing the nonlinear coupling effect between precipitation level and wind speed, the influence of wind speed on precipitation adhesion can be quantified, allowing for a more accurate simulation of the optical characteristics of a wet urban surface under different weather conditions. When determining vehicle dynamic texture density, a 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 computational resources, and enabling the model to better reflect the dynamic changes in urban traffic, thus enhancing the model's dynamic performance. Rendering allocation factors can be determined using dynamic feature coverage area, total scene area, surface reflectance dynamic correction coefficient, and vehicle dynamic texture density. By considering the impact of weather changes and traffic conditions on rendering load, rendering resource allocation can be adjusted, optimizing the rendering priority of dynamic areas, improving the rendering efficiency of complex urban scenes, and thereby enhancing the accuracy and comprehensiveness of rendering allocation factors. Attached Figure Description

[0014] Figure 1 A flowchart illustrating a method for modeling a three-dimensional urban real-scene model according to an embodiment of the present invention is shown exemplarily.

[0015] Figure 2 A flowchart illustrating the calculation of dynamic correction coefficients for surface reflectance according to an embodiment of the present invention is shown.

[0016] Figure 3 An exemplary flowchart illustrating the calculation of vehicle dynamic texture density according to an embodiment of the present invention is shown;

[0017] Figure 4 A flowchart illustrating the calculation of the rendering allocation factor according to an embodiment of the present invention is shown exemplarily;

[0018] Figure 5 An exemplary flowchart for constructing a 3D model of a city scene is shown according to an embodiment of the present invention;

[0019] Figure 6 A block diagram of a city real scene 3D modeling system according to an embodiment of the present invention is shown as an example. Detailed Implementation

[0020] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, the technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0021] The technical solution of the present invention will be described in detail below with reference to specific embodiments. These specific embodiments can be combined with each other, and the same or similar concepts or processes may not be described again in some embodiments.

[0022] Figure 1 An exemplary flowchart illustrates a method for modeling a 3D urban real-scene model according to an embodiment of the present invention, the method comprising:

[0023] Step S1: Obtain point cloud data of the city through LiDAR scanning;

[0024] Step S2: Obtain meteorological data for multiple areas of the city at the current moment, wherein the meteorological data includes light intensity, precipitation intensity and wind speed;

[0025] Step S3: Determine the dynamic correction coefficient for surface reflectance based on the meteorological data;

[0026] Step S4: Set up radar detectors in multiple road areas of the city to obtain traffic flow data at the current moment, wherein the traffic flow data includes vehicle density and vehicle speed;

[0027] Step S5: Determine the vehicle dynamic texture density based on the traffic flow data;

[0028] Step S6: Determine the rendering allocation factor based on the dynamic correction coefficient of the surface reflectance and the dynamic texture density of the vehicle;

[0029] Step S7: Construct a 3D model of the city scene based on the rendering allocation factor and the point cloud data.

[0030] The urban real-scene 3D modeling method according to embodiments of the present invention, through a dynamic correction coefficient for surface reflectivity, enables the model to more realistically present the appearance of the city under different weather scenarios, enhancing the model's visual realism. Through vehicle dynamic texture density, the model can reflect the actual traffic conditions of different roads in the city, thereby enhancing the model's dynamic performance. The rendering allocation factor can rationally allocate rendering resources according to changes in surface reflectivity and traffic conditions. Point cloud data can accurately record the spatial location and shape information of urban objects. Combined with the rendering allocation factor, an urban real-scene 3D model with high-precision geometry and realistic visual effects can be constructed.

[0031] According to one embodiment of the present invention, in step S1, a suitable type of LiDAR device is selected based on the needs of urban modeling and the characteristics of the scene. For example, for scanning a large urban area, an airborne LiDAR may be selected, which can quickly cover a large area, while for detailed scanning of specific buildings or complex terrain in the city, a ground-based LiDAR or a vehicle-mounted LiDAR is selected. Point cloud data can accurately reflect the urban topography and building features.

[0032] According to one embodiment of the present invention, in step S2, there are multiple areas in the city, and meteorological monitoring stations are set up 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 gauge, and the wind speed is monitored by an ultrasonic wind speed sensor.

[0033] According to one embodiment of the present invention, in step S3, a dynamic correction coefficient for surface reflectance is determined based on the meteorological data.

[0034] Figure 2 A flowchart illustrating the calculation of the dynamic correction coefficient for surface reflectance according to an embodiment of the present invention is shown.

[0035] According to an embodiment of the present invention, step S3 includes: step S31, setting a reference light intensity; step S32, determining a precipitation level based on the precipitation intensity; and step S33, determining a dynamic correction coefficient for surface reflectance based on the light intensity, the wind speed, the precipitation level, and the reference light intensity.

[0036] According to one embodiment of the present invention, the reference light intensity can be set to 1000 W / m². If the precipitation intensity is 8 mm / h or higher, the precipitation level is 1; if the precipitation intensity is greater than 6 mm / h and less than or equal to 8 mm / h, the precipitation level is 0.8; if the precipitation intensity is greater than 4 mm / h and less than or equal to 6 mm / h, the precipitation level is 0.6; if the precipitation intensity is greater than 2 mm / h and less than or equal to 4 mm / h, the precipitation level is 0.4; if the precipitation intensity is greater than 0.1 mm / h and less than or equal to 2 mm / h, the precipitation level is 0.2; and if the precipitation intensity is greater than 0 mm / h and less than or equal to 0.1 mm / h, the precipitation level is 0. Based on meteorological data, dynamic correction coefficients for surface reflectance are calculated under different weather conditions to simulate the light scattering characteristics of moist surfaces. The greater the light intensity, the higher the surface reflectance; the greater the precipitation level, the greater the surface moisture, and the more significant the reflectance correction. Strong winds accelerate surface water evaporation and disturb the water film distribution, leading to enhanced specular reflection. For example, when heavy rain is accompanied by strong winds, the ground water film is uneven, and the reflectance needs to be corrected more.

[0037] According to an embodiment of the present invention, determining the dynamic correction coefficient of surface reflectance based on the light intensity, the wind speed, the precipitation level, and the reference light intensity includes: determining the dynamic correction coefficient of surface reflectance of the i-th region at the current time according to formula (1). ,

[0038] (1),

[0039] in, Let be the light intensity of the i-th region at the current moment. As a reference light intensity, Let be the precipitation level of the i-th region at the current moment. Let be the wind speed in the i-th region at the current moment. The preset weights are defined, min is the minimum value function, and i is a positive integer.

[0040] According to an embodiment of the present invention, in formula (1), This represents the ratio of the light intensity of region i at the current moment to the baseline light intensity, reflecting the fundamental impact of direct sunlight on the dynamic correction coefficient of surface reflectivity. Specifically, it's the light intensity factor; for example, when sunlight is strong (e.g., at noon), the ratio approaches or even exceeds 1, while when sunlight is weak (e.g., on a cloudy day), the ratio decreases. The higher the precipitation level of region i at the current moment, the greater the surface moisture, and the greater the need for surface reflectivity correction. This represents the nonlinear saturation effect of wind speed on precipitation, i.e., the attenuation factor of wind speed. The higher the wind speed, the closer its impact on precipitation approaches 1. Wind speed affects the state of precipitation on the ground surface; for example, wind speed accelerates evaporation and blows the water film on the surface during precipitation, forming ripples and increasing scattering. On sunny days, i.e. Surface reflectance is determined solely by light intensity; during precipitation, the surface reflectance correction term... This indicates that it increases with increasing precipitation level and wind speed, simulating the enhanced light scattering effect of moist surfaces, i.e., the precipitation-wind speed coupled effect term, such as specular reflection from water surfaces and diffuse reflection from asphalt pavements. (e.g., 0.3) is a preset weight for adjusting the coupling effect of precipitation and wind speed. This represents the sum of the effects of light intensity and the effects of precipitation-wind speed coupling. Indicates taking 1 and The smaller of the two values ​​ensures that the dynamic correction factor for surface reflectance does not exceed the physical upper limit of 1 (i.e., complete reflection with no absorption).

[0041] In this way, the dynamic correction coefficient for surface reflectance can be determined by examining the relationship between light intensity, precipitation level, wind speed, and surface reflectance, allowing for a more accurate description of the actual changes in surface reflectance in different regions. By introducing the nonlinear coupling effect between precipitation level and wind speed, the influence of wind speed on precipitation adhesion can be quantified, enabling a more precise simulation of the optical characteristics of urban moist surfaces under different weather conditions.

[0042] According to one embodiment of the present invention, in step S4, radar detectors are set up in multiple road areas of the city (e.g., main roads, secondary roads, and branch roads), which can emit microwave signals and receive reflected waves, calculate vehicle speed through the Doppler effect, and calculate vehicle density by counting the number of vehicles passing through the radar detectors per unit time and combining the road segment length.

[0043] According to one embodiment of the present invention, in step S5, the vehicle dynamic texture density is determined based on the traffic flow data.

[0044] Figure 3 An exemplary flowchart illustrating the calculation of vehicle dynamic texture density according to an embodiment of the present invention is shown.

[0045] According to an embodiment of the present invention, step S5 includes: step S51, acquiring lane number and road speed limit data for 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 vehicle dynamic texture density based on the lane number, the road speed limit data, the average vehicle speed, and the vehicle density.

[0046] According to one embodiment of the present invention, lane number and speed limit data for multiple road areas can be obtained through basic databases and other data from traffic management departments. For each road area, the average speed of multiple vehicles at the current moment is obtained. Vehicle density reflects the number of vehicles per unit length of road, lane number reflects the upper limit of road capacity, vehicle speed reflects the efficiency of traffic flow, and vehicle dynamic texture density is a comprehensive indicator of the dynamic characteristics of traffic flow on the road. It can dynamically reflect the degree of congestion and dynamic changes of traffic flow on the road. For example, during morning and evening peak hours, vehicle density increases, average speed decreases, and vehicle dynamic texture density increases accordingly, indicating that road congestion is intensified. During off-peak hours, vehicle density decreases, average speed increases, and vehicle dynamic texture density decreases, indicating that road traffic conditions are good. Vehicle dynamic texture density can reflect real-time changes in road traffic conditions in a timely and accurate manner.

[0047] According to one embodiment of the present invention, 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 includes: determining the vehicle dynamic texture density of the j-th road region at the current time according to formula (2). ,

[0048] (2),

[0049] in, Let J be the vehicle density of the j-th road region at the current time. Let j be the number of lanes in the j-th road region. Let be the average vehicle speed of the j-th road region at the current time. Let j be the speed limit data for the j-th road area, where j is a positive integer.

[0050] According to an embodiment of the present invention, in formula (2), Let be the ratio between the vehicle density of the j-th road region at the current time and the number of lanes in the j-th road region, representing the vehicle density per lane. The ratio of the average vehicle speed in the j-th road region at the current time to the road speed limit data for the j-th road region, plus 1, represents the speed weighting factor, used to adjust the impact of single-lane vehicle density on the dynamic texture. This factor is applied when vehicle speed is low (e.g., during congestion). Much smaller than The speed weighting factor is reduced, and the vehicle dynamic texture density is increased, which more accurately reflects the congestion level of the road in low-speed congestion scenarios, reducing texture overload (i.e., underestimation of congestion level). When the vehicle speed is high (e.g., in smooth traffic), near When the speed weighting factor increases, the vehicle dynamic texture density decreases, reflecting the reduction in 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 overall flow at high speeds).

[0051] In this way, the dynamic texture density of vehicles under different traffic conditions can be automatically adjusted by the speed weight factor, reducing texture overload in low-speed congestion scenarios, saving computing resources, enabling the model to better reflect the dynamic changes of urban traffic, and enhancing the model's dynamic performance.

[0052] According to an embodiment of the present invention, in step S6, a rendering allocation factor is determined based on the dynamic correction coefficient of the surface reflectance and the dynamic texture density of the vehicle.

[0053] Figure 4 A flowchart illustrating the calculation of the rendering allocation factor according to an embodiment of the present invention is shown as an example.

[0054] According to an embodiment of the present invention, step S8 includes: step S61, obtaining the dynamic element coverage area of ​​the city and the total scene area; step S62, determining the rendering allocation factor based on the dynamic element coverage area, the total scene area, the surface reflectance dynamic correction coefficient, and the vehicle dynamic texture density.

[0055] According to one embodiment of the present invention, the dynamic element coverage area of ​​a city, such as the area occupied by real-time changing objects like vehicles and weather particle effects, can be obtained through point cloud data to acquire three-dimensional data of the city scene, thereby obtaining a three-dimensional model. The total area of ​​the scene is the spatial projection area of ​​the entire three-dimensional model. GPU computing power can be allocated according to a rendering allocation factor to achieve better rendering.

[0056] According to one embodiment of the present invention, determining the rendering allocation factor based on the dynamic feature coverage area, the total scene area, the dynamic correction coefficient of the surface reflectance, and the vehicle dynamic texture density includes: determining the rendering allocation factor F at the current moment according to formula (3).

[0057] (3),

[0058] in, The coverage area of ​​dynamic elements in the city. The total area of ​​the city scene. Let be the dynamic correction coefficient for the surface reflectance of the i-th region at the current time. Let be the vehicle dynamic texture density of the j-th road region at the current time, M be the number of urban areas, N be the number of urban road regions, i≤M, j≤N, and i, j, M and N are all positive integers.

[0059] According to one embodiment of the present invention, in formula (3), This is the ratio of the area covered by dynamic elements in the city to the total area of ​​the city scene, i.e., the initial rendering allocation ratio. The larger this ratio is, the more GPU computing power is allocated. The result of averaging the square root of the dynamic correction coefficient of surface reflectance for multiple regions at the current moment represents the impact of weather on rendering load. The larger the result, the greater the impact of weather on rendering load. Heavy rain and strong winds will increase surface reflectance, and changes in lighting will require adjustments to shadows. The result of averaging the square root of the dynamic correction coefficient of surface reflectance for multiple regions at the current moment represents the impact of traffic flow on rendering load. The larger the result, the greater the impact of traffic flow on rendering load, and high-density traffic flow requires more detailed rendering. This is a correction term for weather and traffic flow, used to adjust the initial rendering allocation ratio based on the dynamic correction coefficient of surface reflectance and the dynamic texture density of vehicles, in case of severe weather ( Larger) or high traffic density The larger the value, the greater the rendering allocation factor, prioritizing the allocation of more GPU computing power to render dynamic regions, where dynamic regions are the areas covered by dynamic elements. The ratio of the area covered by dynamic elements in the city to the total area of ​​the city scene, multiplied by the correction terms for weather and traffic flow, represents the rendering allocation factor. The larger the rendering allocation factor, the more rendering resources the dynamic area receives.

[0060] In this way, rendering allocation factors can be determined by the dynamic feature coverage area, total scene area, dynamic surface reflectance correction coefficient, and vehicle dynamic texture density. By adjusting the rendering resource allocation based on the impact of weather changes and traffic conditions on the rendering load, the rendering priority of dynamic areas can be optimized, thereby improving the rendering efficiency of complex urban scenes and enhancing the accuracy and comprehensiveness of rendering allocation factors.

[0061] According to an embodiment of the present invention, in step S7, a three-dimensional model of the city scene is constructed based on the rendering allocation factor and the point cloud data.

[0062] Figure 5 An exemplary flowchart for constructing a 3D model of a city scene according to an embodiment of the present invention is shown.

[0063] According to an 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 for the urban dynamic real scene according to the rendering allocation factor, and constructing a three-dimensional model of the urban real scene.

[0064] According to one embodiment of the present invention, point cloud data can be feature extracted and classified to convert it into a 3D data model of a city scene. In the rendering of the 3D city scene model, GPU performance directly affects the rendering speed and quality; therefore, it is necessary to allocate GPU computing resources reasonably according to the rendering allocation factor. City scenes are dynamically changing, and the distribution and state of dynamic elements may differ at different times. Therefore, it is necessary to update the rendering allocation factor based on real-time data and dynamically adjust the allocation of rendering resources. For example, during peak traffic hours, the number of vehicles on the road increases, and the vehicle texture density increases; at this time, it is necessary to correspondingly increase the allocation of rendering resources for dynamic areas.

[0065] The urban real-scene 3D modeling method according to embodiments of the present invention, through a dynamic surface reflectance correction coefficient, enables the model to more realistically present the appearance of the city under different weather scenarios, enhancing the visual realism of the model. Through vehicle dynamic texture density, the model can reflect the actual traffic conditions of different roads in the city, thereby enhancing the model's dynamic performance. The rendering allocation factor can rationally allocate rendering resources according to changes in surface reflectance and traffic conditions. Point cloud data can accurately record the spatial location and shape information of urban objects. Combined with the rendering allocation factor, a high-precision geometric shape and realistic visual effect of the urban real-scene 3D model can be constructed. When determining the dynamic surface reflectance correction coefficient, the relationship between light intensity, precipitation level, wind speed, and the dynamic surface reflectance correction coefficient can be used to determine the coefficient, which can more accurately describe the actual changes in surface reflectance in different areas. By introducing the nonlinear coupling effect between precipitation level and wind speed, the influence of wind speed on precipitation adhesion can be quantified, allowing for a more accurate simulation of the optical characteristics of the urban moist surface under different weather conditions. When determining vehicle dynamic texture density, a 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 computational resources, and enabling the model to better reflect the dynamic changes in urban traffic, thus enhancing the model's dynamic performance. Rendering allocation factors can be determined using dynamic feature coverage area, total scene area, surface reflectance dynamic correction coefficient, and vehicle dynamic texture density. By considering the impact of weather changes and traffic conditions on rendering load, rendering resource allocation can be adjusted, optimizing the rendering priority of dynamic areas, improving the rendering efficiency of complex urban scenes, and thereby enhancing the accuracy and comprehensiveness of rendering allocation factors.

[0066] Figure 6 An exemplary block diagram of a city real-scene 3D modeling system according to an embodiment of the present invention is shown. The system includes: a point cloud data module for acquiring point cloud data of the city through LiDAR scanning; a meteorological data module for acquiring meteorological data of multiple areas of the city at the current moment, wherein the meteorological data includes light intensity, precipitation intensity, and wind speed; a surface reflectance dynamic correction coefficient module for determining a surface reflectance dynamic correction coefficient based on the meteorological data; a traffic flow data module for setting up radar detectors in multiple road areas of the city to acquire traffic flow data at the current moment, wherein the traffic flow data includes vehicle density and vehicle speed; a vehicle dynamic texture density module for determining vehicle dynamic texture density based on the traffic flow data; a rendering allocation factor module for determining a rendering allocation factor based on the surface reflectance dynamic correction coefficient and the vehicle dynamic texture density; and a 3D model module for constructing a city real-scene 3D model based on the rendering allocation factor and the point cloud data.

[0067] This invention can be a method, apparatus, system, and / or computer program product. The computer program product may include a computer-readable storage medium having computer-readable program instructions loaded thereon for performing various aspects of the invention.

[0068] Those skilled in the art should understand that the embodiments of the present invention described above and shown in the accompanying drawings are merely examples and do not limit the present invention. The objectives of the present invention have been fully and effectively achieved. The functions and structural principles of the present invention have been demonstrated and explained in the embodiments, and any variations or modifications may be made to the implementation of the present invention without departing from the stated principles.

Claims

1. A method for modeling a 3D urban real-scene model, characterized in that, include: Point cloud data of the city is obtained through LiDAR scanning; Acquire meteorological data for multiple areas of the city at the current moment, wherein the meteorological data includes light intensity, precipitation intensity and wind speed; Based on the meteorological data, determine the dynamic correction coefficient for surface reflectance; Radar detectors are installed in multiple road areas of the city to obtain traffic flow data at the current moment, wherein the traffic flow data includes vehicle density and vehicle speed; Based on the traffic flow data, determine the vehicle dynamic texture density; The rendering allocation factor is determined based on the dynamic correction coefficient of the surface reflectance and the dynamic texture density of the vehicle. Based on the rendering allocation factor and the point cloud data, construct a 3D model of the city scene; Based on the meteorological data, determine the dynamic correction coefficient for surface reflectance, including: Set a baseline light intensity; determine the precipitation level based on the precipitation intensity; Based on the light intensity, wind speed, precipitation level, and reference light intensity, determine the dynamic correction coefficient for surface reflectivity; Based on the light intensity, wind speed, precipitation level, and reference light intensity, a dynamic correction coefficient for surface reflectance is determined, including: According to the formula ； Determine the dynamic correction coefficient for the surface reflectance of the i-th region at the current time. ,in, Let be the light intensity of the i-th region at the current moment. As a reference light intensity, Let i be the precipitation level of the i-th region at the current moment. Let be the wind speed in the i-th region at the current moment. The preset weights are defined, min is the minimum value function, and i is a positive integer; Based on the traffic flow data, the vehicle dynamic texture density is determined, including: Obtain lane count and speed limit data for multiple road areas; Based on the traffic flow data, the average speed of multiple vehicles in each road area at the current moment is calculated. 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. 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: According to the formula ； Determine the vehicle dynamic texture density of the j-th road region at the current time. ,in, Let J be the vehicle density of the j-th road region at the current time. Let j be the number of lanes in the j-th road region. Let be the average vehicle speed of the j-th road region at the current time. Let j be the speed limit data for the j-th road area, where j is a positive integer.

2. The method for modeling a three-dimensional urban real-scene model according to claim 1, characterized in that, The rendering allocation factor is determined based on the dynamic correction coefficient for surface reflectance and the dynamic texture density of the vehicle, including: Obtain the dynamic element coverage area and total scene area of ​​the city; The rendering allocation factor is determined based on the dynamic element coverage area, the total scene area, the dynamic correction coefficient of the ground reflectivity, and the vehicle dynamic texture density.

3. The method for modeling a three-dimensional urban real-scene model according to claim 2, characterized in that, The rendering allocation factor is determined based on the dynamic element coverage area, the total scene area, the dynamic correction coefficient for surface reflectivity, and the vehicle dynamic texture density, including: According to the formula ； Determine the rendering allocation factor F at the current moment, where, The coverage area of ​​dynamic elements in the city. The total area of ​​the city scene. Let be the dynamic correction coefficient for the surface reflectance of the i-th region at the current time. Let be the vehicle dynamic texture density of the j-th road region at the current time, M be the number of urban areas, N be the number of urban road regions, i≤M, j≤N, and i, j, M and N are all positive integers.

4. The method for modeling a three-dimensional urban real-scene model according to claim 1, characterized in that, Based on the rendering allocation factor and the point cloud data, a 3D model of the city scene is constructed, including: Based on the point cloud data, obtain 3D data of the urban real scene; allocate rendering resources for the urban dynamic real scene according to the rendering allocation factor, and construct a 3D model of the urban real scene.

5. A system for modeling a three-dimensional urban real-scene model, used to execute the urban real-scene three-dimensional model modeling method as described in any one of claims 1-4, characterized in that, include: The point cloud data module is used to acquire point cloud data of the city through LiDAR scanning; The meteorological data module is used to acquire meteorological data for multiple areas of the city at the current moment, wherein the meteorological data includes light intensity, precipitation intensity and wind speed; The surface reflectance dynamic correction coefficient module is used to determine the surface reflectance dynamic correction coefficient based on the meteorological data. The traffic flow data module is used to set up radar detectors in multiple road areas of the city to obtain traffic flow data at the current moment, wherein the traffic flow data includes vehicle density and vehicle speed. The vehicle dynamic texture density module is used to determine the vehicle dynamic texture density based on the traffic flow data. The rendering allocation factor module is used to determine the rendering allocation factor based on the dynamic correction coefficient of the surface reflectance and the dynamic texture density of the vehicle. The 3D model module is used to construct a 3D model of the city scene based on the rendering allocation factor and the point cloud data.

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