A point cloud reflection intensity simulation method, medium and device

Abstract

The present application discloses a method, medium and device for simulating the reflection intensity of a point cloud, including: in response to the intersection of a virtual ray and a virtual object, sampling a map texture at the intersection to obtain a sampling result; obtaining the incident angle of the virtual ray at the intersection; calculating the initial reflection intensity of the virtual ray at the intersection based on the sampling result at the intersection and the incident angle of the virtual ray at the intersection; correcting the initial reflection intensity by an empirical value coefficient corresponding to the virtual ray to obtain the virtual reflection intensity of the virtual ray at the intersection. In the above manner, the present application can accurately simulate the reflection intensity of the laser radar in the virtual point cloud and improve the accuracy of the laser radar sensor in the autonomous driving simulation.

Description

Technical Field

[0001] The present application relates to the field of sensor simulation, and in particular to a point cloud reflection intensity simulation method, medium and device. Background Art

[0002] In a simulation environment, a simulated LiDAR protocol is used to generate a point cloud in a virtual environment. The method usually adopted is to use ray tracing to simulate and select the vertices of the point cloud near the first intersecting surface. This method can usually simulate the position information of the point cloud more accurately. However, for a real LiDAR sensor, the point cloud has a reflection intensity item. The reflection intensity is related to the reflectivity of the object in the real environment, the angle at which the wire bundle intersects with the object, etc. However, in the virtual scene, since the objects are represented by three-dimensional models, their precision and reflection calculations are quite different from the actual situation. The reflection intensity of the resulting simulated point cloud information will be quite different, which will affect the accuracy of the LiDAR sensor in the autonomous driving simulation. Summary of the invention

[0003] This application mainly provides a point cloud reflection intensity simulation method, storage medium and computer equipment to solve the problem of low accuracy of lidar sensors in autonomous driving simulation.

[0004] In order to solve the above technical problems, a technical solution adopted in the present application is: to provide a point cloud reflection intensity simulation method in a virtual environment, including: in response to the intersection of a virtual ray and a virtual object, performing map texture sampling at the intersection to obtain a sampling result; obtaining the incident angle of the virtual ray at the intersection; based on the sampling result at the intersection and the incident angle of the virtual ray at the intersection, calculating the initial reflection intensity of the virtual ray at the intersection; correcting the initial reflection intensity by an empirical value coefficient corresponding to the virtual ray to obtain the virtual reflection intensity of the virtual ray at the intersection.

[0005] By simulating the material of the surface of the virtual object through texture mapping, the reflection intensity of the virtual ray at the intersection of the virtual ray and the object is closer to the real scene. The virtual ray emitted by the lidar more accurately reflects the distance information, thereby generating accurate three-dimensional point cloud data.

[0006] In some embodiments, the correcting the reflection intensity by using the empirical value coefficient corresponding to the virtual ray includes: multiplying the empirical value coefficient by the initial reflection intensity to obtain the virtual reflection intensity of the virtual ray at the intersection.

[0007] By correcting the reflection intensity through the empirical value coefficient, the reflection intensity value of each point can be linearly adjusted in a global range, thereby integrating different types of radar reflection intensities to adapt to different actual needs.

[0008] In some embodiments, before multiplying the empirical value coefficient by the initial reflection intensity, it also includes: obtaining the virtual reflection intensity and the real reflection intensity of the virtual ray at each point in the existing point cloud data; based on the mean square error between the virtual reflection intensity and the real reflection intensity at each point, optimizing the empirical value coefficient by the least squares method until the mean square error is less than a preset threshold.

[0009] By continuously collecting real reflection intensity data under different temperature and humidity conditions, the empirical value coefficient is optimized.

[0010] In some embodiments, the formula for calculating the mean square error between the virtual reflection intensity and the real reflection intensity is: Wherein, MSE(T) is the mean square error of the empirical coefficient of the virtual ray, N is the total number of points in the current point cloud, I is the initial reflection intensity, and I real is the real reflection intensity, and T is the empirical coefficient of the virtual ray.

[0011] In some embodiments, the initial reflection intensity of the virtual ray at the intersection is calculated based on the map texture sampling at the intersection and the incident angle of the virtual ray at the intersection, including: obtaining the metalness, roughness and basic color of the material at the intersection through map texture sampling; and calculating the initial reflection intensity of the virtual ray at the intersection based on the metalness, roughness, basic color and incident angle of the material at the intersection.

[0012] Through precise material properties, the reflection intensity formula can accurately calculate the reflection intensity of the virtual ray at the intersection point. This helps simulate more realistic lighting effects and improve the quality of rendered images.

[0013] In some embodiments, the formula for calculating the initial reflection intensity is: I = M × A + (1-M) × R × (1-cosθ) 5 Among them, I is the initial reflection intensity, M is the metalness, A is the base color, R is the roughness, and θ is the incident angle.

[0014] The reflection intensity of the virtual ray at the intersection is calculated by taking into account the surface material of the object through the reflection intensity formula, which is conducive to obtaining a more accurate lidar point cloud.

[0015] In some embodiments, obtaining the incident angle of the virtual ray at the intersection includes: obtaining the incident direction of the virtual ray and the normal direction of the virtual object at the intersection, and taking the angle between the incident direction and the normal as the incident angle of the virtual ray at the intersection.

[0016] By calculating the angle between the incident direction of the virtual ray and the normal direction of the object at the intersection, it is applicable to various complex virtual environments and can accurately determine the incident angle. By calculating the incident angle and normal direction, it can be adjusted in real time according to the changes of the object to maintain the real-time and accuracy of the data.

[0017] In some embodiments, obtaining the normal direction of the virtual object at the intersection includes: performing texture sampling of a normal map at the intersection, or calculating the average normal value of a plane around the intersection to determine the normal direction at the intersection.

[0018] Normal maps provide normal information on each pixel, so that even models with a low number of polygons can show high-detail surface effects. Without increasing the geometric complexity of the model, the rendered image can be more realistic, and more complex lighting and reflection effects can be simulated, significantly improving visual details. By calculating the average normal value of multiple planes around the intersection, a smoother normal direction can be obtained. This method helps to reduce visual noise caused by sudden changes in the normal direction of a single plane, making the lighting and shadow effects of the entire surface more natural and continuous.

[0019] In order to solve the above technical problems, another technical solution adopted by the present application is: providing a storage medium on which program data is stored, and when the program data is executed by a processor, the steps of the point cloud reflection intensity simulation method as described above are implemented.

[0020] The beneficial effects of the storage medium refer to the beneficial effects of the above-mentioned point cloud reflection intensity simulation method, which will not be repeated here.

[0021] The present application also provides a computer device, including a processor and a memory connected to each other, wherein the memory stores a computer program, and when the processor executes the computer program, the steps of the point cloud reflection intensity simulation method as described above are implemented.

[0022] The beneficial effects of the computer device refer to the beneficial effects of the above-mentioned point cloud reflection intensity simulation method, which will not be repeated here.

[0023] The beneficial effects of the present application are as follows: Different from the prior art, the present application discloses a point cloud reflection intensity simulation method, storage medium and computer device, which simulates the material of the surface of the virtual object by sampling the texture at the intersection in response to the intersection of the virtual ray and the virtual object. The incident angle of the virtual ray at the intersection is obtained, and the reflection intensity of the virtual ray at the intersection is calculated based on the texture sampling result at the intersection and the incident angle of the virtual ray at the intersection, and the initial reflection intensity is corrected by the empirical value coefficient corresponding to the virtual ray, so that the reflection intensity of the virtual ray at the intersection of the virtual ray and the object is closer to the real scene, and the virtual ray emitted by the laser radar more accurately reflects the distance information, thereby generating accurate three-dimensional point cloud data. BRIEF DESCRIPTION OF THE DRAWINGS

[0024] In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, the drawings required for use in the embodiments or the prior art descriptions are briefly introduced below. Obviously, the drawings described below are only some embodiments of the present application. For those of ordinary skill in the art, other drawings can be obtained based on these drawings without creative work, among which:

[0025] Figure 1 It is a flow chart of an embodiment of a point cloud reflection intensity simulation method provided by the present application;

[0026] Figure 2 Yes Figure 1 The flowchart of step 30 of the point cloud reflection intensity simulation method shown in FIG.

[0027] Figure 3 Yes Figure 1 The flowchart of step 40 of the point cloud reflection intensity simulation method is shown as an embodiment;

[0028] Figure 4 It is a structural schematic diagram of an embodiment of a storage medium provided by the present application;

[0029] Figure 5 It is a structural diagram of an embodiment of a computer device provided by the present application. DETAILED DESCRIPTION

[0030] The following will be combined with the drawings in the embodiments of the present application to clearly and completely describe the technical solutions in the embodiments of the present application. Obviously, the described embodiments are only part of the embodiments of the present application, not all of the embodiments. Based on the embodiments in the present application, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of this application.

[0031] The terms "first", "second", "third" in the embodiments of the present application are only used for descriptive purposes, and cannot be understood as indicating or implying relative importance or implicitly indicating the number of indicated technical features. Thus, the features defined as "first", "second", "third" can expressly or implicitly include at least one of the features. In the description of the present application, the meaning of "multiple" is at least two, such as two, three, etc., unless otherwise clearly and specifically defined. In addition, the terms "including" and "having" and any of their variations are intended to cover non-exclusive inclusions. For example, a process, method, system, product or device comprising a series of steps or units is not limited to the listed steps or units, but optionally also includes steps or units that are not listed, or optionally also includes other steps or units inherent to these processes, methods, products or devices.

[0032] Reference to "embodiments" herein means that a particular feature, structure, or characteristic described in conjunction with the embodiments may be included in at least one embodiment of the present application. The appearance of the phrase in various locations in the specification does not necessarily refer to the same embodiment, nor is it an independent or alternative embodiment that is mutually exclusive with other embodiments. It is explicitly and implicitly understood by those skilled in the art that the embodiments described herein may be combined with other embodiments.

[0033] See also Figure 1 , Figure 1 : is a flow chart of an embodiment of a point cloud reflection intensity simulation method provided by the present application. The point cloud reflection intensity simulation method in a virtual environment includes:

[0034] Step 10: In response to the intersection of the virtual ray and the virtual object, map texture sampling is performed at the intersection point to obtain a sampling result.

[0035] During the point cloud simulation process, a number of virtual rays equal to the actual laser radar scanning frequency are emitted, and each virtual ray has its own direction and initial position. When a virtual ray is projected onto the surface of a virtual object in a virtual environment, the virtual ray and the virtual object intersect. In the spatial coordinate system, the intersection position is represented by the coordinates p(x1, y1, z1). The virtual object surface is texture sampled at the intersection position, and the color, bumps, light reflection and absorption information of the virtual object surface at that point is collected as the sampling result.

[0036] Among them, virtual objects must use physically based rendering (PBR) materials for surface material description.

[0037] Specifically, physically based rendering texture maps usually include multiple layers, each of which represents different physical properties of the material surface. Common physically based rendering texture maps include base color (Albedo) map, normal map, metalness map, roughness map, ambient occlusion (AO) map, height map, etc. These maps combined can provide a more realistic and natural appearance for 3D models, simulating the performance of different materials under different lighting conditions.

[0038] LiDAR (Light Detection and Ranging) is a technology that measures distance and images by emitting laser beams and measuring their reflection time. The LiDAR system calculates the distance by rapidly emitting laser pulses and recording the time difference between the laser pulses returning to the sensor, thereby generating accurate 3D point cloud data. In the point cloud generated by LiDAR, each point contains at least coordinate information and intensity value. This information can be combined to generate accurate environmental perception data.

[0039] Step 20: Get the incident angle of the virtual ray at the intersection point.

[0040] The incident angle formed by the incident direction of the virtual ray and the surface of the virtual object at the intersection point p is obtained, which is used to calculate the reflection or absorption of the virtual ray on the surface of the virtual object.

[0041] By calculating the angle between the incident direction of the virtual ray and the normal direction of the object at the intersection, it is applicable to various complex virtual environments and can accurately determine the incident angle. By calculating the incident angle and normal direction, it can be adjusted in real time according to the changes of the object to maintain the real-time and accuracy of the data.

[0042] Furthermore, step 20 also includes: obtaining the incident direction of the virtual ray and the normal direction of the virtual object at the intersection, and taking the angle between the incident direction and the normal as the incident angle of the virtual ray at the intersection.

[0043] Get the incident direction of the virtual ray, expressed as vector i (x2, y2, z2); get the normal direction of the plane at the intersection point p, expressed as vector n (x3, y3, z3); ​​the angle between the directions of vector i and vector n is the incident angle θ of the virtual ray at the intersection point p.

[0044] The method for obtaining the incident direction of the virtual ray and the normal direction of the virtual object at the intersection is: performing texture sampling of the normal map at the intersection, or calculating the average normal value of the plane around the intersection to determine the normal direction at the intersection.

[0045] By performing coordinate mapping on point p on the surface of the virtual object, the corresponding tangent space position p' of point p on the normal map is determined, the normal map is sampled at p', the normal vector n' at position p' is obtained, and n' is converted to the spatial coordinate system to obtain the normal direction vector n (x3, y3, z3) of the point p.

[0046] Among them, the coordinate parameters x, y and z of the spatial coordinate system represent the longitude, latitude and altitude in the space respectively, and the corresponding normal map coordinate parameters represent the tangent (horizontal direction of the model surface), the sub-tangent (the model surface is perpendicular to the tangent) and the normal (perpendicular to the model surface, pointing to the outside of the model).

[0047] Get other points around point p, calculate the vectors from other points to point p, average the vectors from other points to point p, get the average normal direction at point p, and determine the normal direction vector n(x3, y3, z3) at the intersection.

[0048] A normal map is a special texture defined relative to the tangent space of the texture surface, which stores the normal vector of each pixel. Sampling a normal map can obtain the normal direction stored in it.

[0049] Normal maps provide normal information on each pixel, so that even models with a low number of polygons can show high-detail surface effects, making the rendered image more realistic without increasing the geometric complexity of the model, and simulating more complex lighting and reflection effects, significantly improving visual details. By calculating the average normal of multiple planes around the intersection, a smoother normal direction can be obtained. This method helps reduce visual noise caused by sudden changes in the normal direction of a single plane, making the lighting and shadow effects of the entire surface more natural and continuous.

[0050] Step 30: Based on the sampling result at the intersection and the incident angle of the virtual ray at the intersection, the initial reflection intensity of the virtual ray at the intersection is calculated.

[0051] The virtual object surface material and incident angle will affect the reflection intensity of the virtual ray at the intersection. According to the sampling result at the intersection and the incident angle of the virtual ray at the intersection, the ray reflection and absorption of the virtual ray at the intersection are calculated to obtain the initial reflection intensity of the virtual ray at the intersection.

[0052] Further, see Figure 2 , step 30 further includes:

[0053] Step 31: Get the metalness, roughness and base color of the material at the intersection through map texture sampling.

[0054] According to the position p' corresponding to the intersection point p on the texture coordinates, the metalness, roughness and basic color of the surface material of the virtual object in the map texture are sampled.

[0055] Metallicity refers to the property of whether a material has a metallic luster. In the virtual object surface material, the metallicity is controlled by a value or a map. Specifically, when the metallicity is controlled by a value, the metallicity is changed between 0 (completely non-metallic) and 1 (completely metal) by adjusting the value slider, thereby changing the degree of metallic luster of the material. Lower metallicity values ​​(closer to 0) indicate that the reflectivity of the surface is mainly determined by the roughness, while higher metallicity values ​​(closer to 1) indicate that the surface is closer to a smooth metallic luster. The reflectivity is higher.

[0056] Roughness describes how smooth or rough an object's surface is, and can be controlled by a numerical value or a map. Specifically, when metalness is controlled by a numerical value, by adjusting the numerical slider, you can control how much light is scattered on the surface of the object. A lower roughness value (closer to 0) means a smoother surface, with more concentrated light reflections; a higher roughness value (closer to 1) means a rougher surface, with more diffuse light scattering.

[0057] The Base Color is the color of the Material itself, and determines the basic appearance of the object when there is no lighting or reflections. The Base Color can be any color, including a solid color, a gradient, or a pattern. It is usually specified through a color picker or a texture image. While the Base Color determines the basic appearance of the Material, it also interacts with effects such as lighting and reflections. For example, under lighting, the Base Color may become lighter or darker; under reflection effects, the Base Color may interact with its surroundings to form a reflected image.

[0058] Metalness, Roughness, and Base Color are set to a range of 0-1 because they need to be normalized for ease of processing, and such a range can intuitively reflect the physical characteristics of the material properties.

[0059] For example, the change of metalness from 0 to 1 can smoothly transition from non-metal to metal properties; the change of roughness from 0 to 1 can accurately control the smoothness of the surface; and the base color as the basic intensity of the color, its 0-1 range also conforms to the standard representation of the color channel.

[0060] Through such a design, the graphics rendering engine can more accurately simulate materials in the real world and create more realistic and rich visual effects.

[0061] Step 32: Calculate the initial reflection intensity of the virtual ray at the intersection based on the metalness, roughness, base color and incident angle of the material at the intersection.

[0062] The Schlick approximation, an estimate of the Fresnel equation, can calculate the reflection intensity of the virtual ray at the intersection based on the metalness, roughness, base color and incident angle of the material at the intersection.

[0063] The Schlick approximation is mainly used to calculate the reflectivity of non-metallic surfaces, especially when light enters the surface of an object from air (or vacuum). This approximation method can quickly estimate the reflectivity at a given incident angle through a relatively simple formula, thereby greatly improving rendering efficiency and real-time performance.

[0064] Specifically, the formula for calculating the initial reflection intensity is:

[0065] I=M×A+(1-M)×R×(1-cosθ) 5

[0066] Among them, I is the initial reflection intensity, M is the metalness, A is the base color, R is the roughness, and θ is the incident angle.

[0067] The reflectivity of the virtual object surface material is calculated by metalness, basic color and roughness, and then combined with the size of the incident angle, the initial reflection intensity I generated by the virtual ray emitted at an angle θ to the intersection position under this reflectivity is calculated.

[0068] For example, when M = 0.8, A = 0.6, R = 0.1, θ = 30°, the calculation formula of the initial reflection intensity I of the virtual ray at the intersection is I = 0.8 × 0.6 + (1-0.8) × (1-cos30°) 5 .

[0069] The Schlick approximation avoids directly calculating the complex Fresnel equation through a simple formula, thereby greatly improving the computing efficiency of graphics applications that require real-time rendering, improving rendering quality, and adjusting the reflectivity according to different materials. It is suitable for simulating the surface materials of different virtual objects.

[0070] Through precise material properties, the reflection intensity formula can accurately calculate the reflection intensity of the virtual ray at the intersection point. This helps simulate more realistic lighting effects and improve the quality of rendered images.

[0071] Step 40: Correct the initial reflection intensity by using the empirical value coefficient corresponding to the virtual ray to obtain the virtual reflection intensity of the virtual ray at the intersection point.

[0072] Since different radar models emit different rays at different temperatures and humidity, the initial reflection intensity is corrected by the radar model empirical value coefficient corresponding to the virtual ray, and the overall reflectivity of the laser radar is adjusted within the numerical range.

[0073] Specifically, the method of correcting the initial reflection intensity by the empirical value coefficient corresponding to the virtual ray includes: multiplying the empirical value coefficient by the initial reflection intensity to obtain the virtual reflection intensity of the virtual ray at the intersection.

[0074] For example, if the empirical value coefficient is 1.2, the initial reflection intensity I is multiplied by 1.2, and 1.2I is used as the final virtual reflection intensity.

[0075] When the empirical value coefficient is 1, the virtual reflection intensity is only related to the metalness, basic color, roughness of the virtual object surface and the incident angle of the virtual ray.

[0076] Further, see Figure 3 , step 40 further includes:

[0077] Step 41: obtaining the virtual reflection intensity and the real reflection intensity of the virtual ray at each point in the existing point cloud data;

[0078] The real reflection intensity generated by different models of lidar in different scenes under different temperature and humidity conditions in the point cloud is continuously collected, and the initial reflection intensity of each point cloud is calculated by the method of steps 10 to 30.

[0079] By continuously collecting real reflection intensity data under different temperature and humidity conditions, the empirical value coefficient is optimized.

[0080] Step 42: Based on the mean square error between the virtual reflection intensity and the real reflection intensity at each point, the empirical value coefficient is optimized by the least square method until the mean square error is less than a preset threshold.

[0081] The mean square error between the virtual reflection intensity and the real reflection intensity is calculated to measure the difference between the virtual simulation result and the real result. The numerical value of the empirical value coefficient is then optimized by the least square method until the mean square error reaches the theoretical minimum value (less than the preset threshold). The empirical value coefficient at this time is used as the optimal empirical value parameter for the calculation of the virtual reflection intensity.

[0082] Specifically, the formula for calculating the mean square error between the virtual reflection intensity and the real reflection intensity is:

[0083]

[0084] Where MSE(T) is the mean square error of the virtual ray empirical coefficient, N is the total number of points in the point cloud, I is the initial reflection intensity, and I real is the real reflection intensity, and T is the empirical coefficient of the virtual ray.

[0085] T×I represents the virtual reflection intensity, and the real reflection intensity I is calculated using the mean square error formula realThe mean square error MSE(T) between the virtual reflection intensity T×I is calculated, and the empirical value coefficient T is optimized according to the value of the mean square error until the mean square error MSE(T) is minimized.

[0086] Specifically, optimizing the numerical value of the empirical value coefficient by the least square method includes: finding the minimum value of the mean square error MSE(T) by iteration or numerical optimization algorithm such as gradient descent.

[0087] By correcting the reflection intensity through the empirical value coefficient, the reflection intensity value of each point can be linearly adjusted in a global range, thereby integrating different types of radar reflection intensities to adapt to different actual needs.

[0088] See also Figure 4 , Figure 4 It is a structural diagram of an embodiment of the storage medium provided by the present application.

[0089] The storage medium 300 stores program data 301. When the program data 301 is executed by the processor, the following is achieved: Figure 1 Steps of the point cloud reflection intensity simulation method described.

[0090] The program data 301 is stored in a storage medium 300, and includes a number of instructions for enabling a network device (such as a router, a personal computer, a server, or other network device) or a processor to execute all or part of the steps of the method described in each embodiment of the present application.

[0091] Optionally, the storage medium 300 may be a U disk, a mobile hard disk, a read-only memory (ROM), a random access memory (RAM), a magnetic disk, an optical disk, or other medium that can store program data.

[0092] See also Figure 5 , Figure 5 It is a structural schematic diagram of an embodiment of a computer device provided by the present application.

[0093] The computer device 400 includes a processor 420 and a memory 410 which are connected to each other. The memory 410 stores a computer program. When the processor 420 executes the computer program, the steps of the above-mentioned point cloud reflection intensity simulation method are implemented.

[0094] Different from the prior art, this application samples the texture of the physically based rendering material defined on the surface of the virtual object, obtains the metalness, roughness and basic color related to the reflectivity of the virtual ray, and simulates the reflection ray generated by the virtual ray on the surface of the virtual object in combination with the incident angle of the virtual ray, specifically simulating the reflection intensity of the reflection ray. The real reflection intensity and virtual reflection intensity of a large number of known point clouds are collected to optimize the empirical value coefficient, and the virtual reflection intensity is linearly regulated by the optimized empirical value coefficient, so that the reflection intensity of the virtual ray at the intersection of the virtual ray and the object is closer to the real scene, and the virtual ray emitted by the lidar more accurately reflects the distance information, thereby generating accurate three-dimensional point cloud data.

[0095] Each embodiment in this specification is described in a progressive manner, and the same or similar parts between the embodiments can be referred to each other, and each embodiment focuses on the differences from other embodiments. In particular, for the storage medium embodiment and the computer device embodiment, since they are basically similar to the method embodiment, the description is relatively simple, and the relevant parts can be referred to the partial description of the method embodiment.

[0096] The present application can be used in many general or special computing system environments or configurations, such as personal computers, handheld or portable devices, tablet devices, multiprocessor systems, microprocessor-based systems, network PCs, minicomputers, and distributed computing environments including any of the above systems or devices.

[0097] In the several embodiments provided in this application, it should be understood that the disclosed methods and devices can be implemented in other ways. For example, the device embodiments described above are only illustrative, for example, multiple units or components can be combined or integrated into another system, or some features can be ignored or not performed.

[0098] The units described as separate components may or may not be physically separated, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed on multiple network units. Some or all of the units may be selected according to actual needs to achieve the purpose of the present embodiment.

[0099] In addition, each functional unit in each embodiment of the present application may be integrated into one processing unit, or each unit may exist physically separately, or two or more units may be integrated into one unit. The above-mentioned integrated unit may be implemented in the form of hardware or in the form of software functional units.

[0100] The above descriptions are merely embodiments of the present application and are not intended to limit the patent scope of the present application. Any equivalent structure or equivalent process transformation made using the contents of the present application specification and drawings, or directly or indirectly applied in other related technical fields, are also included in the patent protection scope of the present application.

Claims

1. A point cloud reflection intensity simulation method in a virtual environment, characterized in that: include: In response to the intersection of the virtual ray and the virtual object, a map texture sampling is performed at the intersection point to obtain a sampling result; Obtaining the incident angle of the virtual ray at the intersection point; Calculating an initial reflection intensity of the virtual ray at the intersection based on the sampling result at the intersection and the incident angle of the virtual ray at the intersection; The initial reflection intensity is corrected by using an empirical value coefficient corresponding to the virtual ray to obtain a virtual reflection intensity of the virtual ray at the intersection.

2. The point cloud reflection intensity simulation method according to claim 1, characterized in that: The correcting the reflection intensity by using the empirical value coefficient corresponding to the virtual ray includes: The initial reflection intensity is multiplied by the empirical value coefficient to obtain the virtual reflection intensity of the virtual ray at the intersection point.

3. The point cloud reflection intensity simulation method according to claim 2, characterized in that: Before multiplying the empirical value coefficient by the initial reflection intensity, the method further includes: Obtaining virtual reflection intensity and real reflection intensity of the virtual ray at each point in the existing point cloud data; Based on the mean square error between the virtual reflection intensity and the real reflection intensity at each point, the empirical value coefficient is optimized by the least square method until the mean square error is less than a preset threshold.

4. The point cloud reflection intensity simulation method according to claim 3, characterized in that: The formula for calculating the mean square error between the virtual reflection intensity and the real reflection intensity is: Wherein, MSE(T) is the mean square error of the empirical coefficient of the virtual ray, N is the total number of points in the point cloud, I is the initial reflection intensity, and I real is the real reflection intensity, and T is the empirical coefficient of the virtual ray.

5. The point cloud reflection intensity simulation method according to claim 1, characterized in that: The calculating the initial reflection intensity of the virtual ray at the intersection based on the map texture sampling at the intersection and the incident angle of the virtual ray at the intersection includes: Obtaining the metalness, roughness and base color of the material at the intersection by mapping texture sampling; Based on the metalness, roughness, base color and incident angle of the material at the intersection, the initial reflection intensity of the virtual ray at the intersection is calculated.

6. The point cloud reflection intensity simulation method according to claim 5, characterized in that: The formula for calculating the initial reflection intensity is: I=M×A+(1-M)×R×(1-cosθ) 5 Among them, I is the initial reflection intensity, M is the metalness, A is the base color, R is the roughness, and θ is the incident angle.

7. The point cloud reflection intensity simulation method according to claim 1, characterized in that: The obtaining the incident angle of the virtual ray at the intersection point includes: The incident direction of the virtual ray and the normal direction of the virtual object at the intersection are obtained, and the angle between the incident direction and the normal is used as the incident angle of the virtual ray at the intersection.

8. The point cloud reflection intensity simulation method according to claim 7, characterized in that: The obtaining the normal direction of the virtual object at the intersection includes: The texture sampling of the normal map is performed at the intersection, or the average normal value of the planes around the intersection is calculated to determine the normal direction at the intersection.

9. A storage medium having program data stored thereon, characterized in that: When the program data is executed by a processor, the steps of the point cloud reflection intensity simulation method as described in any one of claims 1 to 8 are implemented.

10. A computer device, characterized in that: The method comprises a processor and a memory connected to each other, wherein the memory stores a computer program, and when the processor executes the computer program, the steps of the point cloud reflection intensity simulation method according to any one of claims 1 to 8 are implemented.

Images