3D Game Scene Adaptation Method and Device Based on Curved Screen

Abstract

The present application relates to the technical field of curved screens, and discloses a 3D game scene adaptation method and device based on a curved screen. The method includes: performing three-dimensional coordinate sampling on the surface of the curved screen, constructing a curved screen space model, and performing dynamic tracking in the 3D game scene according to the curved screen space model to obtain scene mapping data; constructing a spatial distortion vector and performing compensation transformation analysis based on the scene mapping data to obtain target scene data; constructing control grid nodes by using a surface feature tensor, applying deformation constraint conditions to the control grid nodes, and solving a deformation energy equation to obtain scene deformation control data; creating a rendering control instruction based on the scene deformation control data, and performing scene state acquisition and spatial adaptation calculation according to the rendering control instruction to obtain scene display data, thereby realizing precise correction of scene display, significantly improving the picture quality, and ensuring the stability and continuity of scene display.

Description

Technical Field

[0001] The present application relates to the field of curved screen technology, and in particular to a 3D game scene adaptation method and device based on a curved screen. Background Art

[0002] With the rapid development of display technology, curved screens have been widely used in the gaming field due to their unique immersive visual experience. Compared with traditional flat displays, curved screens can provide a wider field of view and better spatial perception, and are particularly suitable for the presentation of 3D game scenes. However, due to the non-planar curved surface characteristics of curved screens, geometric distortion and visual distortion problems will occur when displaying 3D game scenes. Traditional scene rendering methods are difficult to ensure the realism and continuity of the picture.

[0003] At present, most rendering technologies for 3D game scenes are designed based on flat display. Directly applying these technologies to curved screens will lead to problems such as picture distortion, edge deformation, and visual discontinuity. Although some studies have proposed display methods based on geometric correction, these methods often only consider the adaptation of static scenes, which makes it difficult to cope with dynamic changes during the game, and have high computational complexity and poor real-time performance. In addition, during the display of curved screen games, due to the continuous change of viewing angle, scene rendering needs to consider both geometric deformation and visual perception. Existing technical solutions often separate these two aspects, lack a unified theoretical framework and systematic solutions, resulting in less than ideal rendering effects, especially when dealing with large scenes and high dynamic range game content, often resulting in technical problems such as screen tearing and unstable frame rate. Summary of the invention

[0004] The present application provides a 3D game scene adaptation method and device based on a curved screen, thereby achieving accurate correction of the scene display, significantly improving the picture quality, and ensuring the stability and continuity of the scene display.

[0005] In a first aspect, the present application provides a 3D game scene adaptation method based on a curved screen, and the 3D game scene adaptation method based on a curved screen includes:

[0006] Sampling the three-dimensional coordinates of the curved screen surface, constructing a curved screen space model, and dynamically tracking the curved screen in a 3D game scene according to the curved screen space model to obtain scene mapping data;

[0007] Based on the scene mapping data, a spatial distortion vector is constructed and a compensation transformation analysis is performed to obtain target scene data;

[0008] Calculate the surface feature tensor according to the target scene data, construct control grid nodes by using the surface feature tensor, apply deformation constraint conditions to the control grid nodes and solve the deformation energy equation to obtain scene deformation control data;

[0009] Create a rendering control instruction based on the scene deformation control data, and perform scene state acquisition and spatial adaptation calculation according to the rendering control instruction to obtain scene display data.

[0010] The second aspect of the present application provides a 3D game scene adaptation device based on a curved screen. The 3D game scene adaptation device based on a curved screen includes:

[0011] A sampling module for performing three-dimensional coordinate sampling on the surface of the curved screen, constructing a curved screen space model, and dynamically tracking in the 3D game scene according to the curved screen space model to obtain scene mapping data;

[0012] An analysis module for constructing a spatial distortion vector and performing compensation transformation analysis based on the scene mapping data to obtain target scene data;

[0013] A construction module for calculating the surface feature tensor according to the target scene data, constructing control grid nodes by using the surface feature tensor, applying deformation constraint conditions to the control grid nodes and solving the deformation energy equation to obtain scene deformation control data;

[0014] A creation module for creating a rendering control instruction based on the scene deformation control data, and performing scene state acquisition and spatial adaptation calculation according to the rendering control instruction to obtain scene display data.

[0015] Compared with the prior art, the present application has the following beneficial effects: By performing three-dimensional coordinate sampling and spatial model construction on the surface of the curved screen, combined with the dynamic particle swarm tracking technology, the scene mapping data can be accurately obtained, effectively solving the problem of inaccurate scene space positioning in traditional methods. Using the spatial distortion vector construction and compensation transformation analysis method, geometric distortion and visual distortion are processed separately, and through bilinear interpolation and boundary constraint optimization, accurate correction of scene display is achieved, significantly improving the picture quality. Introducing the surface feature tensor analysis and control grid node construction technology, accurate scene deformation control is realized by solving the deformation energy equation, ensuring the continuity and smoothness of the scene deformation process. Based on the rendering resource allocation strategy of priority distribution, combined with the frustum model and shader optimization technology, efficient utilization of rendering resources is achieved, improving the real-time performance of the system. Through the distributed state fusion and robustness control technology, the problems of system delay and noise interference are effectively handled, ensuring the stability and continuity of scene display. Description of the Drawings

[0016] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the following will briefly introduce the accompanying drawings required for the description of the embodiments or the prior art. Obviously, the accompanying drawings in the following description are only some embodiments of the present invention. For those of ordinary skill in the art, without creative efforts, other drawings can be obtained based on these drawings.

[0017] The structures, ratios, sizes, etc. shown in the accompanying drawings of this specification are only used to cooperate with the content disclosed in the specification for those who are familiar with this technology to understand and read, and are not used to limit the limiting conditions under which the present invention can be implemented. Therefore, they do not have technical substantive significance. Any modification of the structure, change of the proportional relationship, or adjustment of the size, without affecting the effects that the present invention can produce and the purposes that can be achieved, should still fall within the scope that can be covered by the technical content disclosed in the present invention.

[0018] Figure 1 is a schematic flowchart of a 3D game scene adaptation method based on an arc screen provided by an embodiment of the present invention;

[0019] Figure 2 is a schematic block diagram of the structure of a 3D game scene adaptation device based on an arc screen provided by an embodiment of the present invention. Detailed implementation manners

[0020] The following will clearly and completely describe the technical solutions in the embodiments of the present invention with reference to the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are some, but not all, of the embodiments of the present invention. All other embodiments obtained by those of ordinary skill in the art based on the embodiments of the present invention without creative efforts fall within the scope of protection of the present invention.

[0021] The flowchart shown in the accompanying drawings is only an example illustration, and does not necessarily include all the content and operations / steps, nor does it necessarily need to be executed in the described order. For example, some operations / steps can also be decomposed, combined, or partially merged. Therefore, the actual execution order may change according to the actual situation.

[0022] It should also be understood that the terms used in this application specification are only for the purpose of describing specific embodiments and are not intended to limit this application. As used in this application specification and the appended claims, unless the context clearly indicates otherwise, the singular forms "a", "an", and "the" are intended to include the plural forms.

[0023] It should be further understood that the term "and / or" used in this application specification and the appended claims refers to any combination and all possible combinations of one or more of the associated listed items, and includes these combinations. Please refer toFigure 1 , an embodiment of the 3D game scene adaptation method based on a curved screen in the embodiments of the present application includes:

[0024] Step 100: Perform three-dimensional coordinate sampling on the surface of the curved screen, construct a curved screen space model, and perform dynamic tracking in the 3D game scene according to the curved screen space model to obtain scene mapping data;

[0025] It can be understood that the execution subject of the present application can be a 3D game scene adaptation device based on a curved screen, or a terminal or a server. Specifically, it is not limited here. The embodiments of the present application will be described by taking the server as the execution subject as an example.

[0026] Specifically, the x-axis and y-axis are calibrated for the horizontal and vertical directions of the curved screen respectively, and the z-axis is calibrated for the direction perpendicular to the screen reference plane to establish the reference points for three-dimensional coordinate acquisition. These reference points are used to define the basic space range and reference coordinate system of the curved screen. According to the three-dimensional coordinate acquisition reference points, uniform grid points are divided on the surface of the curved screen according to certain rules to form a plurality of regularly arranged grid points. At each grid point, the corresponding spatial position coordinates are measured and recorded to obtain discrete spatial point data. The discrete spatial point data is divided into multiple data subsets according to the spatial distribution law, so that each subset can centrally describe the spatial characteristics of the local surface. After the data division is completed, the curvature is calculated for the adjacent point data of each data subset to extract the geometric characteristics of the local curved surface of the curved screen and obtain local curvature data. Using the local curvature data and discrete spatial point data, a surface mathematical model is constructed by fitting with a cubic spline function to continuously describe the geometric shape of the curved screen. Based on the surface mathematical model, the specific distributions of the normal vector and tangent vector are calculated at each spatial position to generate a curvature tensor matrix reflecting the shape change of the surface. The eigenvalue decomposition operation is performed on the curvature tensor matrix to obtain a curvature distribution vector, which reflects the curvature change characteristics of different regions on the surface of the curved screen. Based on the curvature distribution vector, the surface of the curved screen is divided into sub-regions, so that each sub-region can more accurately describe the local characteristics of the curved screen, and at the same time ensure good continuity and coordination between different sub-regions to obtain region segmentation data. An independent coordinate system is established for each sub-region in the region segmentation data, and the spatial transformation relationship between the sub-regions is calculated. The spatial transformation relationship defines the geometric mapping rules between the parts of the curved screen, thereby constructing an overall space model of the curved screen to describe the geometric characteristics and spatial structure of the curved screen. Using the constructed curved screen space model, an initial particle swarm is generated in the 3D game scene. These particle swarms, as the core of dynamic tracking, capture the dynamic changes in the scene in real time through interaction with the game scene content, and finally obtain scene mapping data.

[0027] Based on the arc screen space model, the 3D game scene is evenly divided. The scene space is logically partitioned based on the geometric characteristics of the arc screen to ensure that each partition can be reasonably mapped to the display area of the arc screen. After completing the space division, the particle swarm is initialized to generate the initial particle distribution. The generation of the initial particle swarm needs to follow the surface characteristics and spatial distribution rules of the arc screen model to ensure that the distribution density and position of the particle swarm match the geometric structure of the arc screen. Set the basic attributes for each initial particle, including the position vector, velocity vector, and acceleration vector, to form the basic state matrix of the particle, which describes the dynamic characteristics of each particle in 3D space. Based on the basic state matrix, configure the scene depth value, visual weight, and deformation coefficient for each particle to expand the description dimension of the particle and generate the enhanced state data of the particle. The depth value reflects the distance between the particle and the viewpoint and is used to handle the far and near relationships in space; the visual weight is used to measure the importance of the particle in the arc screen display; while the deformation coefficient is used to adapt to the surface characteristics of the arc screen to ensure that the particle can be mapped to the appropriate position. Based on the enhanced state data, construct the motion equation function of the particle to describe the dynamic changes of the particle in the scene space. To accurately reflect the surface characteristics of the arc screen and its influence on the particle motion, the motion equation function is coupled with the curvature influence factor to generate the state transition function. According to the state transition function, by quantitatively analyzing the rendering parameters, construct the observation model of the particle to generate the observation correlation data. This observation model is used to describe the correlation relationship between the display state of the particle swarm and the actual scene and can provide the necessary feedback data to correct the state of the particle. Based on the observation correlation data, the predicted value and the observed value of the particle are optimized by data fusion using the Kalman filter method to obtain the optimized result of the particle state. The Kalman filter can effectively combine the prior model of the particle motion with the actual observation data, eliminate noise, and optimize the state of the particle, making the motion trajectory of the particle smoother and meeting the requirements of the arc screen display. Calculate the interaction relationship of the local particle swarm according to the optimized result of the particle state to construct the group behavior model. The interaction relationship of the local particle swarm is established by analyzing the distance, direction, and velocity difference between the particles, reflecting the coordination and overall behavior of the particle group in the scene space. Based on the group behavior model, dynamically update the motion parameters of the particle swarm to achieve the dynamic adjustment of the particle swarm in the scene space. Perform mapping transformation calculation on the optimized motion parameters of the particle swarm and the scene coordinate system to complete the final adaptation of the particle on the arc screen surface and generate the scene mapping data that meets the requirements of the arc screen surface display.

[0028] Step 200: Based on the scene mapping data, construct the spatial distortion vector and perform compensation transformation analysis to obtain the target scene data;

[0029] Specifically, the surface projection deformation amount of each rendering object in the scene mapping data is calculated. By matching the curved surface characteristics of the curved screen with each rendering object in the mapping data, the deformation of each object when projected onto the curved screen is quantified. The quantification of the deformation is achieved through spatial coordinate transformation, where the displacement between the source space points and the target space points of each object constitutes a spatial distortion vector, describing the spatial distortion characteristics of the rendering object when mapped onto the surface of the curved screen. A geometric feature decomposition operation is performed on the spatial distortion vector, and it is decomposed into geometric distortion components and visual distortion components according to the surface projection rules. The geometric distortion components reflect the physical space offset caused by the curvature change of the curved screen, while the visual distortion components capture the influence of the viewing angle, screen curvature, and human eye line-of-sight differences on the visual effect. After the decomposition is completed, a compensation transformation matrix is constructed based on the geometric distortion components and visual distortion components. This compensation transformation matrix maps the source space points to the target space to correct the distortion of the scene objects on the curved screen display. The construction of the compensation transformation matrix needs to combine the position, size, shape of the objects in the scene, and the curvature characteristics of the curved screen to ensure that the display effect of each object after mapping meets the requirements of realism and immersion. Through the mapping process, preliminary spatial transformation data is obtained. An inverse projection transformation is performed on the geometric distortion components in the spatial transformation data. By analyzing the curvature parameters of the curved screen, the projection compensation amount of each mapping point is calculated to obtain geometric correction data, which precisely eliminates the deformation problem caused by the surface projection, making the position and shape of the rendering object on the curved screen more conform to the actual situation. The rendering parameters of the visual distortion components in the spatial transformation data are adjusted. According to the law of viewing angle change, the compensation parameters of each pixel point are calculated to generate visual optimization data. These compensation parameters can dynamically adjust the texture, lighting, and other characteristics of the rendering object, making the final display effect more in line with the visual perception law of the audience. The geometric correction data and the visual optimization data are combined and calculated to generate compensation optimization data. In order to make the data distribution smoother and continuous, a bilinear interpolation operation is performed on the compensation optimization data. By dividing the data according to the grid and smoothing the adjacent pixel points, the boundary discontinuity problem caused by discrete calculation is eliminated, and continuous compensation data is obtained. The continuous compensation data is input into the boundary constraint processing unit to perform boundary calibration and gradient optimization. The boundary calibration is used to eliminate the boundary distortion of the scene display area, ensuring that the rendering object can maintain the same display effect as the central area at the edge position of the curved screen. The gradient optimization improves the naturalness and consistency of the display by adjusting the color and brightness transition of adjacent areas. Through the above steps, the target scene data is finally obtained.

[0030] Step 300: Calculate the surface feature tensor according to the target scene data, construct the control grid nodes using the surface feature tensor, apply deformation constraint conditions to the control grid nodes, and solve the deformation energy equation to obtain the scene deformation control data;

[0031] It should be noted that the coordinate system of the target scene data is standardized, and the coordinate system of the scene is unified into the spatial model of the curved screen to ensure that the spatial relationships of all elements in the scene can adapt to the curvature characteristics of the curved screen. On the basis of standardization, a metric tensor is constructed according to the metric relationship of the local space. Through the geometric feature analysis of the metric tensor, the key surface information in the scene is extracted, and a surface feature tensor describing the geometric properties of the surface is obtained, including the local curvature, direction field and other geometric properties of the surface. A feature vector field is constructed based on the surface feature tensor. The spatial distribution laws of the principal direction, curvature line and geodesic of the surface are quantitatively calculated to obtain the surface feature vectors. The surface feature vectors are input into the mesh generation algorithm, and the spatial region is adaptively meshed according to the topological structure of the scene to generate adaptable mesh node data. In the mesh generation, under the guidance of the feature vectors, the complex scene geometry is effectively divided into refined mesh elements, so that each mesh element can better reflect the local characteristics of the surface. For each generated mesh node, a local coordinate frame is established to analyze the geometric relationship between the nodes in the local coordinate system. By calculating the associated transformation matrix between each node and its adjacent nodes, node transformation data is generated to describe the spatial relationship and transformation rules between the mesh nodes. A scene deformation model is established according to the node transformation data. By optimizing the node position parameters, the deformation amount between the nodes is calculated to obtain the spatial deformation data. The spatial deformation data directly reflects the geometric adjustment requirements of the scene when adapting to the curved screen. On this basis, geometric constraints and visual constraints are introduced for the scene deformation. By constructing a geometric constraint function, the degree of preservation of the geometric shape after the scene deformation is calculated to ensure that the basic structural characteristics of the scene are not damaged during the deformation process. At the same time, by constructing a visual constraint function, the continuity of the visual effect after the deformation is quantified to ensure that there will be no split or unnatural visual perception in the display on the curved screen. Combining the geometric constraints and visual constraints, a deformation energy function is constructed. Based on the deformation energy function, the optimal deformation parameters are obtained by numerical optimization methods. These parameters can ensure that the scene deformation not only meets the spatial adaptation requirements of the curved screen, but also retains the original visual effect of the scene. After obtaining the optimal deformation parameters, these parameters are applied to the actual scene through the scene deformation mapping operation to generate the final scene deformation control data.

[0032] Step 400: Create a rendering control instruction based on the scene deformation control data, and perform scene state acquisition and spatial adaptation calculation according to the rendering control instruction to obtain the scene display data.

[0033] Specifically, score calculations are performed on each scene element in the scene deformation control data, including evaluating the scene importance, dynamic characteristics, and visual saliency of each element. By integrating these evaluation metrics, quantify the importance of each scene element in the current scene and generate priority distribution data. Input the priority distribution data into the rendering resource allocation model, and allocate computing resources according to the priorities of each region in the scene. Calculate the resource level for different priority scene regions to generate a resource allocation weight map. The resource allocation weight map can guide the resource allocation strategy during the rendering process, enabling computing resources to be concentrated on higher-priority scene regions. At the same time, based on the resource allocation weight map, perform multi-level control on the rendering precision of scene elements. For regions with high priority, use a higher rendering precision to ensure the presentation of picture details; while for regions with low priority, appropriately reduce the rendering precision, thereby saving computing resources and improving the overall rendering efficiency. Through this step, generate detail control data to refine the rendering precision requirements for different regions. Based on the detail control data, construct a frustum model to perform visibility analysis and spatial occlusion determination on scene elements. The frustum model simulates the player's viewing range and can efficiently filter out visible scene objects in the current view, generating a list of visible objects. By removing scene elements outside the view or those that are occluded, reduce the rendering workload and improve the rendering performance. Input the list of visible objects into the shader control unit to optimize the shader parameters and texture sampling methods related to the view. By dynamically adjusting the shader parameters and texture processing strategies, more efficient rendering can be achieved while ensuring that the display effect conforms to the curvature characteristics of the arc screen, generating rendering state data. For the image content in the rendering state data, perform post-processing to improve the visual quality. For the image edges, perform anti-aliasing processing to eliminate the jaggedness caused by resolution limitations. Generate image processing parameters through pixel-level smooth interpolation operations to improve the smoothness and detail expressiveness of the image. Based on the generated image processing parameters, establish a rendering cache mechanism. The core of this mechanism is to store and build an index for the rendering results of static scene content, so as to reuse these rendering results and avoid rendering the unchanged content multiple times, improving the rendering efficiency. Through this step, generate cache management data. Integrate the cache management data with dynamic scene elements and organize them into a rendering scheduling sequence. The rendering scheduling sequence is the final rendering control instruction, containing the rendering priorities, precision control, and status management information for all scene elements. When executing the rendering control instruction, collect the status of the scene according to the rendering scheduling sequence and perform spatial adaptation calculations in combination with the geometric characteristics of the arc screen to generate the final scene display data. The scene display data contains all the necessary information for adapting to the arc screen display, including surface mapping, precision optimization, and visual enhancement processing, etc., thus achieving high-quality 3D game scene rendering.

[0034] Collect the camera position, perspective change, and dynamic object status in the rendering control instructions in real time. Through the collaborative action of sensors, rendering engines, and real-time monitoring modules, obtain the dynamic changes of these elements in the scene, and input the real-time acquisition results into the status monitoring system for integration and processing. Through data cleaning, classification, and integration, generate scene change data containing the real-time changes of the scene. Input the scene change data into the time-varying delay analysis unit to analyze the dynamic delay characteristics during network transmission and processing. Through the analysis of these delay characteristics, construct a delay distribution model and generate delay characteristic data. The construction of the delay distribution model needs to combine the actual network conditions and device performance. Especially in a multi-node rendering environment, the delay characteristics change over time, so the model needs to have a high degree of dynamic adaptability. To improve the reliability of the system, perform composite noise analysis on the delay characteristic data. During this process, generate noise distribution data through the superposition calculation of the signal interference model and the environmental noise model to quantify the impact degree of different noise sources on the system. Based on the noise distribution data, construct a distributed consistency controller, and thus generate a state synchronization strategy through distributed computing in a multi-node environment. The state synchronization strategy coordinates the working states of each node to ensure that the entire system can maintain consistency under the influence of noise interference and delay, and obtain consistency control data. After inputting the consistency control data into the distributed state fusion unit, perform state fusion operations through multi-node collaborative computing, integrate the state data from different nodes, and generate state optimization data. To make the system maintain stability under uncertainty and disturbances, input the state optimization data into the robustness controller for calculation. The robustness controller generates steady-state control data through the suppression calculation of system disturbances and external uncertainties to ensure the operating stability of the system in a complex environment. Input the steady-state control data into the spatial mapping module to perform spatial mapping transformation. Combining the geometric characteristics of the curved screen, perform curved screen adaptation operations on the scene so that it can be presented on the curved screen without distortion. During the scene adaptation operation, consider the accuracy of spatial mapping, and combine the perspective change, dynamic object status, and depth information of the scene to ensure that the final display effect has a high degree of immersion and visual consistency. Through the above steps, finally obtain the scene display data adapted to the curved screen.

[0035] In the embodiments of the present application, by performing three-dimensional coordinate sampling and spatial model construction on the surface of the curved screen, combined with the dynamic particle swarm tracking technology, the scene mapping data can be accurately obtained, effectively solving the problem of inaccurate scene space positioning in traditional methods. By using the spatial distortion vector construction and compensation transformation analysis method, geometric distortion and visual distortion are processed separately, and through bilinear interpolation and boundary constraint optimization, accurate correction of scene display is achieved, significantly improving the picture quality. The surface feature tensor analysis and control grid node construction technology are introduced, and accurate scene deformation control is achieved by solving the deformation energy equation, ensuring the continuity and smoothness of the scene deformation process. Based on the rendering resource allocation strategy based on priority distribution, combined with the frustum model and shader optimization technology, efficient utilization of rendering resources is achieved, improving the real-time performance of the system. Through the distributed state fusion and robustness control technology, the problems of system delay and noise interference are effectively handled, ensuring the stability and continuity of scene display.

[0036] In a specific embodiment, the process of executing step 100 may specifically include the following steps:

[0037] Calibrate the x-axis and y-axis respectively in the horizontal and vertical directions of the curved screen, and calibrate the z-axis in the direction perpendicular to the screen reference plane to obtain the three-dimensional coordinate acquisition reference points;

[0038] According to the three-dimensional coordinate acquisition reference points, evenly divide the curved surface of the curved screen into grid points, obtain a plurality of grid points, and record the spatial position coordinates of each grid point to obtain discrete spatial point data;

[0039] Divide the discrete spatial point data into multiple data subsets according to the spatial distribution law, and calculate the curvature of the adjacent point data of the data subsets to obtain local curvature data;

[0040] Perform cubic spline function fitting on the local curvature data and the discrete spatial point data to obtain a surface mathematical model, and based on the surface mathematical model, calculate the spatial distribution of the normal vector and the tangent vector for each spatial position to obtain a curvature tensor matrix;

[0041] Perform eigenvalue decomposition operation on the curvature tensor matrix to obtain a curvature distribution vector, and divide the surface of the curved screen into sub-regions according to the curvature distribution vector to obtain region segmentation data;

[0042] Establish an independent coordinate system for each sub-region in the region segmentation data, calculate the spatial transformation relationship between the sub-regions to obtain a curved screen spatial model;

[0043] Generate an initial particle swarm in the 3D game scene according to the curved screen spatial model, and perform dynamic tracking on the initial particle swarm to obtain scene mapping data.

[0044] Specifically, to calibrate the three-dimensional coordinate system of the curved screen, the screen is evenly divided in the horizontal direction axis) and the vertical direction axis), and at the same time, the direction perpendicular to the reference plane of the screen ([[]] axis) is calibrated. The physical width of the curved screen is , and the physical height is , and its curvature is represented by , defined as the curvature of the screen surface ([[]] , where is the radius of the curved surface). By dividing the screen into segments in the horizontal direction and segments in the vertical direction, the size of each divided unit is calculated:

[0045] ;

[0046] where, is the width of each grid cell in the horizontal direction, and is the height of each grid cell in the vertical direction. For each point on the screen surface, its three-dimensional coordinates are defined as , where, , represents the coordinate of the th grid point in the horizontal direction; , represents the coordinate of the th grid point in the vertical direction; , represents the coordinate of the th grid point in the direction perpendicular to the screen plane. After calibrating the grid points, discrete space point data is obtained, which describes the spatial geometric characteristics of the curved screen surface. According to the discrete space point data, it is divided into multiple data subsets for calculating the local curvature. For any two adjacent points and , the curvature between them is calculated by the following formula:

[0047] ;

[0048] where, represents the modulus of the difference between the tangent vectors of the two points; represents the Euclidean distance between the two points; , is the tangent vector of point , representing the rate of change of the surface direction at that point. These local curvature data reflect the geometric changes in different regions of the curved screen surface and provide a basis for surface fitting. Based on the local curvature data and discrete space point data, a cubic spline function is used for surface fitting to construct an overall surface mathematical model. Assume the fitted surface is , where and is a parameter variable (representing the normalized coordinates on the grid). The cubic spline function model is expressed as:

[0049] ;

[0050] where is the weight coefficient of the control point, representing the influence of each control point on the surface shape; , are the cubic spline basis functions, used to define the smoothness of the surface; is the normalized coordinate, usually in the range of [0, 1]. Through the surface mathematical model, the normal vector and the tangent vector at each position are calculated, representing the normal direction and the tangent direction of the surface at that point respectively:

[0051] ;

[0052] By further analyzing the surface mathematical model, the geometric characteristics of the surface are described by constructing the curvature tensor matrix :

[0053] ;

[0054] where is the principal curvature, representing the maximum curvature of the surface along the first principal direction; is the secondary curvature, representing the minimum curvature of the surface along the second principal direction. After performing eigenvalue decomposition on the tensor matrix , the curvature distribution vector is obtained, which is used to divide the screen surface into sub-regions. By establishing an independent coordinate system for each sub-region and calculating the spatial transformation relationship (such as rotation and translation) between adjacent regions, the overall spatial model of the curved screen is constructed. Based on this model, an initial particle swarm is generated in the 3D game scene. For example, assuming the particle position is , and the velocity is , through the dynamic tracking formula:

[0055] ;

[0056] the dynamic changes of the particles in the scene are simulated. Combining geometric analysis and particle dynamics, the scene mapping data adapted to the curved screen is finally generated, and the distortion-free scene display on the curved screen is realized.

[0057] In a specific embodiment, the process of generating the initial particle swarm in the 3D game scene according to the curved screen spatial model and dynamically tracking the initial particle swarm to obtain the scene mapping data may specifically include the following steps:

[0058] Uniformly divide the 3D game scene based on the arc screen space model and initialize the particle swarm to generate the initial particle swarm;

[0059] Set the position vector, velocity vector, and acceleration vector for each particle in the initial particle swarm to obtain the particle basic state matrix, and configure the scene depth value, visual weight, and deformation coefficient for each particle according to the particle basic state matrix to obtain the particle enhanced state data;

[0060] Construct the motion equation function based on the particle enhanced state data, and perform coupled calculation with the curvature influence factor to obtain the state transfer function;

[0061] Based on the state transfer function, construct the particle observation model through the quantitative analysis of the rendering parameters to obtain the observation correlation data, and perform the Kalman filter update operation based on the observation correlation data to optimize the data fusion of the predicted value and the observed value to obtain the particle state optimization result;

[0062] Calculate the interaction relationship of the local particle swarm according to the particle state optimization result, construct the group behavior model, and perform dynamic update to obtain the particle swarm motion parameters;

[0063] Perform mapping transformation calculation on the particle swarm motion parameters and the scene coordinate system to obtain the scene mapping data.

[0064] Specifically, uniformly divide the 3D game scene based on the space model of the arc screen. Based on the geometric characteristics of the arc screen, such as the width of the screen 、height 、radius of curvature (inversely proportional to the curvature ), perform regular grid division on the scene space. Assume that it is divided into units in the horizontal direction and units in the vertical direction, then the resolution of the grid is:

[0065] ;

[0066] For the center point of each grid cell, its three-dimensional coordinates are expressed as , where:

[0067] ;

[0068] These points form the spatial distribution of the initial particle swarm. Assume that the initial particle swarm contains particles, and the position vector of each particle is expressed as . Initialize the basic dynamic state for each particle, including the position vector , velocity vector , acceleration vector These states combine to form the basic state matrix of the particle :

[0069] ;

[0070] Among them, is the current position coordinate of the particle; is the velocity component of the particle; is the acceleration component of the particle. Based on the basic state matrix, enhanced attributes are configured for each particle, including the scene depth value , visual weight and deformation coefficient . Among them, represents the depth of the particle from the observation point (or viewpoint), and the calculation formula is:

[0071] ;

[0072] Among them is the position of the observation point; represents the contribution of the particle to the visual effect, determined by the curvature region weight of the particle; is used to describe the degree of deformation of the particle when adapting to the curved screen, obtained through curvature calculation. Combining these enhanced attributes, the enhanced state data of the particle is formed . Based on the enhanced state data, the motion equation function of the particle is constructed . In 3D space, the motion of the particle is jointly affected by position, velocity and acceleration, and its position update formula is:

[0073] ;

[0074] Among them is the time step. At the same time, the velocity of the particle is updated to:

[0075] ;

[0076] In order to reflect the influence of the curvature of the curved screen on the motion of the particle, the motion equation is coupled with the curvature influence factor , and the acceleration is updated to:

[0077] ;

[0078] Among them is the normal vector of the point where the particle is located. Based on the motion equation, combined with rendering parameters (such as viewing angle, lighting and resolution) for quantitative analysis, a particle observation model is constructed. The observation model describes the observation state of the particle through the formula , where is a non - linear projection function used to map enhanced state data to screen display. To optimize the motion state of particles, data fusion is performed based on the Kalman filtering method. The state update formula of the Kalman filter is:

[0079] ;

[0080] where is the optimized enhanced state data; is the Kalman gain, used to balance the predicted value and the observed value; is the observation matrix, representing the influence of the particle state on the observation result. The interaction relationship of the local particle swarm is calculated from the particle state data obtained by filtering optimization, and a group behavior model is constructed. The interaction force is calculated by introducing a Boolean attraction - repulsion model:

[0081] ;

[0082] where are the attraction and repulsion coefficients, is the distance threshold for attraction and repulsion. The motion parameters of the particle swarm are mapped and transformed with the scene coordinate system, and the particle state is associated with the scene space through the coordinate transformation formula Finally, the scene mapping data adapted to the curved screen is generated for dynamic rendering and display.

[0083] In a specific embodiment, the process of executing step 200 may specifically include the following steps:

[0084] Calculate the surface projection deformation amount for each rendering object in the scene mapping data, and construct a spatial distortion vector through spatial coordinate transformation;

[0085] Perform geometric feature decomposition operation on the spatial distortion vector, and decompose the spatial distortion vector into geometric distortion components and visual distortion components according to the surface projection rules;

[0086] Construct a compensation transformation matrix based on the geometric distortion components and visual distortion components, map the source space points to the target space, and obtain spatial transformation data;

[0087] Perform inverse projection transformation on the geometric distortion components in the spatial transformation data, calculate the projection compensation amount according to the curvature parameters of the curved screen, obtain geometric correction data, and adjust the rendering parameters of the visual distortion components in the spatial transformation data, calculate the compensation parameters according to the perspective change rule, and obtain visual optimization data;

[0088] Combine the geometric correction data and the visual optimization data for combined calculation to obtain compensated optimization data, and perform bilinear interpolation operations on the compensated optimization data, and smooth the adjacent pixel points according to the grid division to obtain continuous compensation data;

[0089] Input the continuous compensation data into the boundary constraint processing unit for boundary calibration and gradient optimization to obtain the target scene data.

[0090] Specifically, the initial spatial position of each rendering object is represented by three-dimensional coordinates denoted as is the position of the object in the plane, and is its depth information. Due to the curved surface characteristics of the curved screen, all rendering objects are subjected to curved surface projection to adapt to the screen surface. By calculating the curved surface projection deformation amount of the object, the position offset amount of each object in the mapping from the plane to the curved surface is quantified . For the case where the curvature radius of the screen is , the calculation formula for the curved surface projection deformation amount

[0091] is:

[0092] where are the coordinates in the source space; are the coordinates in the target curved surface space. Through this transformation, the spatial distortion vector of each rendering object is obtained, where are the offsets of the object in the three dimensions respectively. Perform geometric feature decomposition operations on the spatial distortion vector. Each distortion vector D is decomposed into a geometric distortion component and a visual distortion component according to the curved surface projection rule. The geometric distortion component reflects the physical offset of the object under the geometric shape change of the curved screen, and the visual distortion component describes the distortion caused by the change of the viewing angle or the perception characteristics of the human eye. The decomposition formula is:

[0093] ;

[0094] where is the normal vector at the position of the object, defined as projected along the normal vector direction, representing the geometric change; is the remaining component, representing the visual change. Based on the decomposed geometric distortion component and visual distortion component, a compensation transformation matrix is constructed for mapping the source space point to the target space point . The general expression form of the compensation transformation matrix is:

[0095] ;

[0096] where is the identity matrix, and are the geometric compensation term and the visual compensation term respectively. For the geometric distortion component in the constructed spatial transformation data, perform an inverse projection transformation to calculate the projection compensation amount. By analyzing the curvature parameter of the curved screen to correct the geometric offset of the object so that it fits better with the screen surface. The inverse projection transformation formula is:

[0097] ;

[0098] where is the geometric position after projection compensation, is the curvature parameter, is the normal vector. At the same time, for the visual distortion component in the spatial transformation data, adjust the rendering parameters according to the viewing angle change rule, such as lighting, texture, and resolution. Assuming the viewing angle parameter is (the angle between the viewing direction and the normal), then the visual compensation parameter is calculated by the following formula:

[0099] ;

[0100] where is the visual weight of the object. Apply to the rendering pipeline to optimize the visual display effect and generate visual optimization data. Combine and calculate the geometric correction data and the visual optimization data to generate compensation optimization data. Perform bilinear interpolation operation on the compensation optimization data to smooth it and eliminate the pixel-level jump caused by the discrete grid. Input the generated continuous compensation data into the boundary constraint processing unit for boundary calibration and gradient optimization. In the boundary calibration, ensure that the rendered object fully fits the curved screen display area by restricting the pixel values of the object beyond the screen area. And in the gradient optimization, make the display effect more natural and smooth by minimizing the pixel gradient change. Generate the target scene data adapted to the curved screen.

[0101] In a specific embodiment, the process of executing step 300 may specifically include the following steps:

[0102] Perform coordinate system standardization operation on the target scene data, construct a metric tensor according to the local space metric relationship, and perform geometric feature analysis on the metric tensor to obtain a surface feature tensor;

[0103] Based on the surface feature tensor, construct a feature vector field, perform quantization calculation on the spatial distribution laws of the surface principal direction, curvature line, and geodesic line to obtain a surface feature vector;

[0104] Input the surface feature vector into the mesh generation algorithm, adaptively divide the spatial region according to the scene topology structure to obtain mesh node data, establish a local coordinate frame for each node in the mesh node data, calculate the associated transformation matrix between adjacent nodes, and obtain node transformation data;

[0105] Establish a scene deformation model based on the node transformation data, calculate the deformation amount through the optimization adjustment of the node position parameters, and obtain spatial deformation data;

[0106] Construct a geometric constraint function for the spatial deformation data, calculate the degree of geometric shape preservation, and construct a visual constraint function to calculate the continuity of the visual effect to obtain a deformation energy function;

[0107] Perform numerical optimization and solution based on the deformation energy function to obtain the optimal deformation parameters, and perform a scene deformation mapping operation on the optimal deformation parameters to obtain scene deformation control data.

[0108] Specifically, the target scene data includes all objects to be rendered and their position, shape, and texture information in the three-dimensional space. These data are initially defined in the global coordinate system, but for adapting to the geometric characteristics of the curved screen, a coordinate system standardization operation is performed. Assume that the point position in the global coordinate system is , through the standardization transformation, map it to the unitized local coordinate system, so that the coordinate range is normalized to [0,1]. The standardization formula is:

[0109] ;

[0110] where is the standardized coordinate; are the minimum and maximum boundary points of the scene data respectively. Based on the coordinate standardization, construct a metric tensor . The metric tensor describes the geometric properties of the local surface, and its definition is:

[0111] ;

[0112] where , representing the change along the direction; , representing the and coupling change; , representing the change along the direction; is the dot product operation. Perform geometric feature analysis on the constructed metric tensor, and obtain the surface feature tensor . The eigenvalue The principal curvatures and eigenvectors of the corresponding surface respectively represent the principal directions of the surface. Based on the surface feature tensor, an eigenvector field is constructed to quantitatively calculate the spatial distribution laws of the principal directions, curvature lines, and geodesics of the surface. Assume the eigenvector field is and is expressed by the following formula:

[0113] ;

[0114] where is the weight coefficient used to adjust the contribution of different eigenvectors to the field. The constructed eigenvector field is input into the mesh generation algorithm, and adaptive meshing is performed according to the topological structure of the scene to generate mesh node data. Assume the set of mesh nodes is and the position of each node is . A local coordinate frame is established for each node, and the associated transformation matrix between adjacent nodes is defined as:

[0115] ;

[0116] where is the rotation matrix describing the angular change between nodes; is the translation vector describing the displacement between nodes. Based on the node transformation data, a scene deformation model is established. Assume the initial position of the node is , and the deformation amount is calculated by optimizing the node position parameters to obtain the deformed node position:

[0117] ;

[0118] The deformation amount is determined by optimization calculations to minimize the deformation energy function. During the calculation process, a geometric constraint function is constructed to measure the degree of geometric shape preservation:

[0119] ;

[0120] where and are the transformation matrices after and before deformation, respectively. At the same time, a visual constraint function is constructed to evaluate the continuity of the visual effect:

[0121] ;

[0122] where is the visual weight, is the current visual state, is the target visual state. The total deformation energy function is:

[0123] ;

[0124] where is a trade-off coefficient. Numerically optimize and solve the deformation energy function, and use the gradient descent or finite element method to obtain the optimal deformation parameters . Through the scene deformation mapping operation, apply these parameters to the grid nodes to generate scene deformation control data.

[0125] In a specific embodiment, the process of executing the steps of creating a rendering control instruction based on the scene deformation control data and performing scene state acquisition and spatial adaptation calculation according to the rendering control instruction to obtain scene display data may specifically include the following steps:

[0126] Calculate the scores of the importance, dynamic characteristics, and visual salience of the scene elements in the scene deformation control data to obtain the priority distribution data;

[0127] Input the priority distribution data into the rendering resource allocation model, divide the computing resource levels for different priority scene areas, obtain the resource allocation weight map, and perform multi-level control on the rendering precision of the scene elements according to the resource allocation weight map to obtain the detail control data;

[0128] Build a view frustum model based on the detail control data, perform visibility analysis and spatial occlusion determination on the scene elements to obtain a list of visible objects, and input the list of visible objects into the shader control unit to optimize the calculation of the shading parameters and texture sampling methods related to the viewing angle to obtain the rendering state data;

[0129] Perform anti-aliasing processing on the image edges in the rendering state data, and obtain the image processing parameters through pixel-level smooth interpolation operation;

[0130] Establish a rendering cache mechanism based on the image processing parameters, store the rendering results and build indexes for the static scene content to obtain the cache management data, and organize the cache management data and dynamic scene elements into a rendering scheduling sequence to obtain the rendering control instruction;

[0131] Perform scene state acquisition and spatial adaptation calculation according to the rendering control instruction to obtain scene display data.

[0132] Specifically, calculate the scores of the importance, dynamic characteristics, and visual salience of the scene elements in the scene deformation control data. Assume there are elements in the scene, and the score of each element consists of three key indicators: importance score , dynamic characteristic score and visual salience score These scores are used to calculate the total priority score through linear combination. :

[0133] ;

[0134] Among them, is the weight coefficient, which is used to adjust the influence of each score on the total priority; reflects the importance of the scene element in the current rendering task, such as the main target or key background that the player focuses on; represents the dynamicity of the scene element, through the speed or acceleration of the element Calculated as:

[0135] ;

[0136] Where is the vector norm, is the acceleration weight; Describes the visual saliency of the element, such as color contrast or texture complexity. Calculate the priority score of each element through the above formula to obtain the priority distribution data . Input the priority distribution data into the rendering resource allocation model to divide the resource levels of different regions in the scene. Assume that the total amount of rendering resources is , and the amount of resources allocated to each element is , then allocate resources proportionally according to the priority score of the element:

[0137] ;

[0138] Through this calculation, a resource allocation weight map is generated to guide the rendering precision control of each scene element. For example, for elements with high importance, allocate higher rendering resources to improve their resolution and detail fidelity. Based on the resource allocation weight map, perform multi-level rendering precision control on each scene element to generate detail control data , where represents the rendering precision level of the element . On the basis of the detail control data, construct a frustum model to perform visibility analysis and spatial occlusion determination on the scene elements. The frustum model represents the visible range of the player's perspective, which is defined by the viewpoint , the line-of-sight direction and the viewing angle range , where and are the horizontal and vertical viewing angles respectively. Determine whether the scene element is within the frustum through the following formula:

[0139] ;

[0140] Among them, is the center point position of the scene element; is the dot product operation. Through frustum analysis, visible objects within the viewing range are filtered out to generate a list of visible objects . The list of visible objects is input into the shader control unit to optimize view-related color viewing parameters (such as light direction, shadow intensity) and texture sampling methods. Assume the optimization parameter for texture sampling is . It is calculated by the following formula:

[0141] ;

[0142] Among them, is the texture weight; is the rendering precision level; is the depth value of the element from the viewpoint. The optimized result generates rendering state data, including texture, lighting, and shadow parameters. To improve the visual quality of the rendered image, anti-aliasing is performed on the image edges in the rendering state data. Through pixel-level smooth interpolation operations, the jagged phenomenon is effectively eliminated to generate smoother edges. Based on the generated image processing parameters, a rendering cache mechanism is established. This mechanism stores the rendering results of static scene content (such as background elements) to reduce the overhead of repeated rendering. Assume the cache index for the static scene is , and the update frequency of dynamic elements is . Then, the rendering priorities of static and dynamic content are coordinated through a rendering scheduling sequence to finally generate rendering control instructions. When executing the rendering control instructions, according to the current state of the scene, dynamic changes are collected and spatial adaptation calculations are performed, and the final scene display data is generated in combination with the rendering scheduling sequence.

[0143] In a specific embodiment, the process of executing step 400 may specifically include the following steps:

[0144] Real-time collect the camera position, view changes, and dynamic object states in the rendering control instructions to obtain real-time collection results, and input the real-time collection results into the state monitoring system for data integration to obtain scene change data;

[0145] Input the scene change data into the time-varying delay analysis unit, construct a delay distribution model according to the time-varying characteristics of network transmission delay and processing delay, and obtain delay characteristic data;

[0146] Perform composite noise analysis on the delay characteristic data. Through the superposition calculation of the signal interference model and the environmental noise model, obtain noise distribution data, and based on the noise distribution data, construct a distributed consistency controller. Generate a state synchronization strategy through multi-node collaborative calculation to obtain consistency control data;

[0147] Perform a distributed state fusion operation on the consistency control data to obtain state-optimized data, and input the state-optimized data into a robustness controller to perform suppression calculations on system disturbances and uncertainties to obtain steady-state control data;

[0148] Perform a spatial mapping transformation on the steady-state control data and perform a scene adaptation operation according to the display characteristics of the curved screen to obtain scene display data.

[0149] Specifically, in the rendering control process, the camera position, view angle change, and the state of dynamic objects are the core data sources for real-time adjustment of the scene display effect. Assume the position of the camera is , the view direction is defined by the unit vector , and the state of the dynamic object is represented by its position , velocity , acceleration , and rotation angle . In the real-time acquisition stage, these data are continuously obtained through sensors and input devices and organized into a real-time acquisition result matrix :

[0150]

[0151] Each row represents the real-time state of a dynamic object or the camera. Input the real-time acquisition results into the state monitoring system, and after data cleaning, classification, and integration, generate scene change data. The scene change data matrix records the dynamic changes of the scene at a specific time point and provides a basis for delay analysis and subsequent optimization. Input the scene change data into the time-varying delay analysis unit for constructing a delay distribution model. The delay distribution model considers the network transmission delay and the processing delay , and the two together constitute the total delay :

[0152] ;

[0153] Assume that the network delay follows a normal distribution , the processing delay follows an exponential distribution , and the probability density function of the total delay is expressed in a convolution form:

[0154] ;

[0155] where and are the probability density functions of the network and processing delays respectively. Through this delay distribution model, obtain the delay characteristic data , including the mean, variance, and their time-varying characteristics of the delay. These data can quantify the delay impact of different scenario elements. Conduct composite noise analysis on the delay characteristic data, considering the superposition effect of signal interference and environmental noise. Assume that the signal interference follows a normal distribution with a mean of zero , the environmental noise is white noise, and its power spectral density is . The composite noise is expressed as:

[0156] ;

[0157] where , is the signal interference; , is the environmental noise. Through superposition calculation, obtain the noise distribution data matrix , and based on this, construct a distributed consensus controller. The consensus controller generates a state synchronization strategy through multi-node collaborative calculation to optimize the dynamic consistency in the scenario. Assume that the state of each node is , and the consensus control law is:

[0158] ;

[0159] where is the set of neighbor nodes of node ; is the connection weight between nodes. By solving this system of differential equations, obtain the consensus control data , which is used to maintain the dynamic consistency of the scenario. Perform a distributed state fusion operation on the consensus control data, combine the local states of each node, and generate globally optimized state data . To improve the stability of the system, input the optimized data into a robust controller, which generates steady-state control data by suppressing disturbances and uncertainties. The design of the robust controller is based on the Lyapunov stability criterion, and its goal is to find a Lyapunov function such that:

[0160] ;

[0161] The meaning of each variable is as follows: is the Lyapunov function, which is a non-negative scalar function used to describe the "energy" or "degree of deviation" of the system state. It is usually defined as a quadratic form, for example:

[0162] ;

[0163] where is the state vector of the system, such as the position, velocity, acceleration, etc. state information of a dynamic object in the scenario, ; is a symmetric positive definite matrix used to weigh the influence of different state variables on the system stability. It is the time derivative of the Lyapunov function, used to judge whether the system state tends to be stable as time changes. If , it means that the system energy does not increase and tends to be stable. is the gradient of the Lyapunov function with respect to the state vector and is defined as:

[0164] ;

[0165] is the time derivative of the state vector, representing the dynamic change of the system state. For example, the position change of a dynamic object is represented in the following form:

[0166] ;

[0167] where is the system dynamic matrix, describing the linear relationship between system states; is the control input matrix, used to describe the influence of external control on the system state; is the control input vector, representing the input applied by the controller, such as instructions for adjusting the speed and position of a dynamic object; is the external disturbance or uncertainty, such as nonlinear effects like network delay and noise. The design goal of a robust controller is to find a control input such that in the presence of an external disturbance , the system can still maintain stability, that is, satisfy:

[0168] ;

[0169] is the control gain, used to control the convergence speed; is the norm of the state vector, representing the degree to which the system deviates from the equilibrium state. The design of the control input is based on the following formula: where:

[0170] ;

[0171] where is the feedback gain matrix, obtained through optimization calculations, used to adjust the response intensity of the controller. By substituting into the dynamic equation , the stability of the system is guaranteed, that is . Perform a spatial mapping transformation on the steady-state control data and perform a scene adaptation operation according to the display characteristics of the curved screen. Assume that the curvature radius of the curved screen is , and the target position of each scene element is . Through the surface mapping formula:

[0172] ;

[0173] Adjust the depth information of the elements to adapt to the surface characteristics of the screen, and generate the final scene display data.

[0174] In this embodiment, after the scene display data is generated, it further includes a display enhancement control step: constructing a feature space mapping network, separating and extracting the spatial distribution features and visual perception features in the scene display data to obtain feature decomposition data; performing extreme value distribution analysis on the spatial distribution features in the feature decomposition data, modeling the feature boundaries through the marginal distribution function to obtain boundary feature data; inputting the boundary feature data into a multivariate extreme value model, constructing a joint distribution function between feature variables, and performing probability density estimation on the feature space to obtain density distribution data; establishing a display area adaptive weight model based on the density distribution data, evaluating the importance and assigning weights to different display areas to obtain area control data; performing fusion optimization calculation on the area control data and visual perception features, generating a display enhancement control instruction, and adjusting the display parameters in real time according to the display enhancement control instruction to obtain parameter optimization data; performing display effect evaluation based on the parameter optimization data, scoring the display result through a visual quality evaluation index to obtain quality evaluation data; inputting the quality evaluation data into a feedback controller, generating a parameter adjustment strategy, and dynamically optimizing the display effect according to the parameter adjustment strategy to obtain display optimization data; performing fine adjustment and enhancement control on the scene display effect according to the display optimization data to continuously improve the scene display quality.

[0175] When performing fine-tuning and enhanced control on the scene display effect according to the display optimization data, it includes: constructing a pre-trained model for the scene rendering stage, extracting the feature information of different display areas of the curved screen in the continuous rendering stage through a deep neural network, adaptively segmenting the display areas, establishing the mapping relationship between the areas, quantitatively analyzing the rendering parameters of each area to obtain area feature data; inputting the area feature data into the instance-level contrastive learning network, selecting anchor samples using the dynamic threshold method, constructing a local rendering quality evaluation matrix, extracting the stable rendering features of each display area, and filtering out the interference features that do not match the current rendering state through the bidirectional attention mechanism to obtain instance feature data; performing category-level contrastive learning operations on the instance feature data, constructing a multi-scale feature pyramid, enhancing the contrast between the scene main body and the background, adopting an adaptive weight strategy to balance the contributions of different scale features, and optimizing the feature expression through the cross-entropy loss function to obtain contrastive learning data; constructing a rendering optimization model based on the contrastive learning data, performing dimensionality reduction analysis on the rendering parameter space, establishing a parameter sensitivity evaluation index, adopting a gradient-guided parameter search strategy to achieve local fine-tuning of the rendering parameters to obtain rendering control data; inputting the rendering control data into the feature fusion network, constructing a spatially adaptive convolutional layer based on the geometric characteristics of the curved screen, adaptively weighted fusing the features of different curvature areas, and maintaining the detail information through residual connections to obtain fusion optimization data; establishing a region adaptive controller according to the fusion optimization data, adopting a multi-task learning framework to optimize the rendering quality and calculation efficiency simultaneously, constructing a quality evaluation index system, realizing real-time monitoring and evaluation of the rendering effect to obtain region control data; performing parameter correction calculation based on the region control data, establishing a mapping model between the parameters and the rendering quality, adopting a reinforcement learning method to optimize the parameter adjustment strategy, and solving the optimal parameter combination through a dynamic programming algorithm to obtain parameter correction data; integrating the parameter correction data into the rendering pipeline, establishing a hierarchical rendering control mechanism, prioritizing the scene elements, realizing the reasonable allocation of rendering resources, ensuring the display quality of the key areas, and completing the precise control of the rendering effect of the curved screen.

[0176] The above describes the 3D game scene adaptation method based on the curved screen in the embodiments of the present application. Next, the 3D game scene adaptation device 10 based on the curved screen in the embodiments of the present application will be described. Please refer to Figure 2 One embodiment of the 3D game scene adaptation device 10 based on the curved screen in the embodiments of the present application includes:

[0177] The sampling module 11 is used to perform three-dimensional coordinate sampling on the surface of the curved screen, construct a curved screen space model, and perform dynamic tracking in the 3D game scene according to the curved screen space model to obtain scene mapping data;

[0178] An analysis module 12, configured to construct a spatial distortion vector and perform compensation transformation analysis based on scene mapping data to obtain target scene data;

[0179] A construction module 13, configured to calculate a surface feature tensor according to the target scene data, construct control grid nodes by using the surface feature tensor, impose deformation constraint conditions on the control grid nodes, and solve a deformation energy equation to obtain scene deformation control data;

[0180] A creation module 14, configured to create a rendering control instruction based on the scene deformation control data, and perform scene state acquisition and spatial adaptation calculation according to the rendering control instruction to obtain scene display data.

[0181] Through the collaborative cooperation of the above-mentioned various components, by performing three-dimensional coordinate sampling and spatial model construction on the surface of the arc screen, and combining with the dynamic particle swarm tracking technology, the scene mapping data can be accurately obtained, effectively solving the problem of inaccurate scene space positioning in the traditional method. By adopting the method of constructing a spatial distortion vector and performing compensation transformation analysis, geometric distortion and visual distortion are processed separately, and through bilinear interpolation and boundary constraint optimization, accurate correction of scene display is realized, significantly improving the picture quality. The introduction of surface feature tensor analysis and control grid node construction technology realizes accurate scene deformation control by solving the deformation energy equation, ensuring the continuity and smoothness of the scene deformation process. Based on the rendering resource allocation strategy of priority distribution, combined with the frustum model and shader optimization technology, efficient utilization of rendering resources is realized, improving the real-time performance of the system. Through distributed state fusion and robustness control technology, the problems of system delay and noise interference are effectively processed, ensuring the stability and continuity of scene display.

[0182] Those skilled in the art can clearly understand that for the convenience and brevity of description, the specific working processes of the systems, devices, and units described above can refer to the corresponding processes in the foregoing method embodiments, and will not be described herein again.

[0183] When the integrated unit is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of this application, in essence, or the part that contributes to the prior art, or all or part of this technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions for causing an electronic device (which may be a personal computer, a server, or a network device, etc.) to execute all or part of the steps of the methods described in various embodiments of this application. The foregoing storage medium includes: various media that can store program codes, such as USB flash drives, mobile hard disks, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical discs.

[0184] As described above, the above embodiments are only used to illustrate the technical solutions of this application, rather than to limit them; although this application has been described in detail with reference to the foregoing embodiments, those of ordinary skill in the art should understand that they can still modify the technical solutions recorded in the foregoing embodiments, or perform equivalent replacements on some of the technical features; and these modifications or replacements do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of various embodiments of this application.

Claims

1. A 3D game scene adaptation method based on a curved screen, characterized in that: The method comprises: Sampling the three-dimensional coordinates of the curved screen surface, constructing a curved screen space model, and dynamically tracking the curved screen in a 3D game scene according to the curved screen space model to obtain scene mapping data; Based on the scene mapping data, a spatial distortion vector is constructed and a compensation transformation analysis is performed to obtain target scene data; specifically, the method comprises: calculating the surface projection deformation of each rendering object in the scene mapping data, and constructing a spatial distortion vector through spatial coordinate transformation; performing a geometric feature decomposition operation on the spatial distortion vector, and decomposing the spatial distortion vector into a geometric distortion component and a visual distortion component according to a surface projection rule; constructing a compensation transformation matrix according to the geometric distortion component and the visual distortion component, mapping a source space point to a target space, and obtaining spatial transformation data; and performing a geometric feature decomposition operation on the spatial distortion vector, and decomposing the spatial distortion vector into a geometric distortion component and a visual distortion component according to a surface projection rule; and constructing a compensation transformation matrix according to the geometric distortion component and the visual distortion component, and mapping a source space point to a target space, and obtaining spatial transformation data; and performing a geometric feature decomposition operation on the spatial transformation data. The method comprises the following steps: performing a reverse projection transformation on the image data, calculating a projection compensation amount according to a curvature parameter of the curved screen, obtaining geometric correction data, adjusting rendering parameters of visual distortion components in the spatial transformation data, calculating compensation parameters according to a law of viewing angle change, and obtaining visual optimization data; combining and calculating the geometric correction data with the visual optimization data to obtain compensation optimization data, performing a bilinear interpolation operation on the compensation optimization data, smoothing adjacent pixel points according to grid division, and obtaining continuous compensation data; inputting the continuous compensation data into a boundary constraint processing unit for boundary calibration and gradient optimization, and obtaining target scene data; Calculating a surface feature tensor according to the target scene data, constructing a control grid node using the surface feature tensor, applying deformation constraints to the control grid nodes and solving a deformation energy equation to obtain scene deformation control data; A rendering control instruction is created based on the scene deformation control data, and scene state acquisition and space adaptation calculation are performed according to the rendering control instruction to obtain scene display data.

2. The 3D game scene adaptation method based on a curved screen according to claim 1, characterized in that: The three-dimensional coordinate sampling of the curved screen surface is performed to construct a curved screen space model, and dynamic tracking is performed in a 3D game scene according to the curved screen space model to obtain scene mapping data, including: The x-axis and y-axis are calibrated in the horizontal and vertical directions of the curved screen respectively, and the z-axis is calibrated in the direction perpendicular to the screen reference plane to obtain the three-dimensional coordinate acquisition reference point; The curved screen surface is divided into uniform grid points according to the three-dimensional coordinate acquisition reference points to obtain a plurality of grid points, and the spatial position coordinates of each grid point are recorded to obtain discrete spatial point data; Dividing the discrete spatial point data into multiple data subsets according to the spatial distribution law, and performing curvature calculation on the adjacent point data of the data subsets to obtain local curvature data; Fitting the local curvature data and the discrete space point data with a cubic spline function to obtain a surface mathematical model, and based on the surface mathematical model, calculating the spatial distribution of the normal vector and the tangent vector for each spatial position to obtain a curvature tensor matrix; Performing an eigenvalue decomposition operation on the curvature tensor matrix to obtain a curvature distribution vector, and dividing the curved screen surface into sub-regions according to the curvature distribution vector to obtain region segmentation data; Establishing an independent coordinate system based on each sub-region in the region segmentation data, calculating the spatial transformation relationship between the sub-regions, and obtaining a curved screen space model; An initial particle group is generated in a 3D game scene according to the arc-shaped screen space model, and the initial particle group is dynamically tracked to obtain scene mapping data.

3. The 3D game scene adaptation method based on a curved screen according to claim 2, characterized in that: The generating an initial particle group in the 3D game scene according to the arc screen space model, and dynamically tracking the initial particle group to obtain scene mapping data, includes: Uniformly divide the 3D game scene and initialize the particle swarm based on the arc-shaped screen space model to generate an initial particle swarm; Setting a position vector, a velocity vector and an acceleration vector for each particle in the initial particle group to obtain a particle basic state matrix, and configuring a scene depth value, a visual weight and a deformation coefficient for each particle according to the particle basic state matrix to obtain particle enhancement state data; Constructing a motion equation function based on the particle enhancement state data, and coupling the motion equation function with the curvature influence factor to obtain a state transfer function; Based on the state transfer function, a particle observation model is constructed through quantitative analysis of rendering parameters to obtain observation-related data, and a Kalman filter update operation is performed based on the observation-related data to perform data fusion optimization on the predicted value and the observed value to obtain a particle state optimization result; Calculating the interaction relationship of the local particle group according to the particle state optimization result, constructing a group behavior model, and dynamically updating it to obtain the particle group motion parameters; The particle swarm motion parameters are mapped and transformed into the scene coordinate system to obtain scene mapping data.

4. The 3D game scene adaptation method based on a curved screen according to claim 1, characterized in that: The step of calculating a surface feature tensor according to the target scene data, constructing a control grid node using the surface feature tensor, applying deformation constraints to the control grid node and solving a deformation energy equation to obtain scene deformation control data includes: Performing coordinate system normalization operation on the target scene data, constructing a metric tensor according to a local space metric relationship, and performing geometric feature analysis on the metric tensor to obtain a surface feature tensor; Based on the surface feature tensor, a feature vector field is constructed, and the spatial distribution law of the main direction, curvature line and geodesic line of the surface is quantified and calculated to obtain the surface feature vector; Input the surface feature vector into a meshing algorithm, adaptively mesh the space region according to the scene topology structure, obtain mesh node data, establish a local coordinate frame for each node in the mesh node data, calculate the associated transformation matrix between adjacent nodes, and obtain node transformation data; A scene deformation model is established according to the node transformation data, and the deformation variables are calculated by optimizing and adjusting the node position parameters to obtain the spatial deformation data; Constructing a geometric constraint function for the spatial deformation data, calculating the degree of geometric shape preservation, and constructing a visual constraint function, calculating the continuity of the visual effect, and obtaining a deformation energy function; A numerical optimization solution is performed based on the deformation energy function to obtain an optimal deformation parameter, and a scene deformation mapping operation is performed on the optimal deformation parameter to obtain scene deformation control data.

5. The 3D game scene adaptation method based on a curved screen according to claim 4, characterized in that: The creating a rendering control instruction based on the scene deformation control data, and performing scene state acquisition and space adaptation calculation according to the rendering control instruction to obtain scene display data includes: Score and calculate the importance, dynamic characteristics, and visual significance of scene elements in the scene deformation control data to obtain priority distribution data; Inputting the priority distribution data into a rendering resource allocation model, dividing the scene areas with different priorities into computing resource levels, obtaining a resource allocation weight map, and performing multi-level control on the rendering accuracy of scene elements according to the resource allocation weight map to obtain detail control data; Constructing a view cone model based on the detail control data, performing visibility analysis and spatial occlusion determination on scene elements to obtain a visible object list, and inputting the visible object list into a shader control unit, optimizing and calculating view-related shading parameters and texture sampling methods to obtain rendering state data; Performing anti-aliasing processing on the image edge in the rendering state data, and obtaining image processing parameters through pixel-level smoothing interpolation operation; Establishing a rendering cache mechanism based on the image processing parameters, storing rendering results and indexing static scene content to obtain cache management data, and organizing the cache management data and dynamic scene elements into a rendering scheduling sequence to obtain rendering control instructions; Scene state acquisition and space adaptation calculation are performed according to the rendering control instruction to obtain scene display data.

6. The 3D game scene adaptation method based on a curved screen according to claim 5, characterized in that: The performing scene state acquisition and space adaptation calculation according to the rendering control instruction to obtain scene display data includes: The camera position, viewing angle change and dynamic object state in the rendering control instruction are collected in real time to obtain real-time collection results, and the real-time collection results are input into the state monitoring system for data integration to obtain scene change data; Inputting the scene change data into a time-varying delay analysis unit, constructing a delay distribution model according to the time-varying characteristics of network transmission delay and processing delay, and obtaining delay feature data; Performing composite noise analysis on the delay characteristic data, obtaining noise distribution data by superimposing a signal interference model and an environmental noise model, and constructing a distributed consistency controller based on the noise distribution data, generating a state synchronization strategy through multi-node collaborative calculation, and obtaining consistency control data; Performing distributed state fusion operation on the consistency control data to obtain state optimization data, and inputting the state optimization data into the robustness controller to perform suppression calculation on system disturbance and uncertainty to obtain steady-state control data; A space mapping transformation is performed on the steady-state control data, and a scene adaptation operation is performed according to the display characteristics of the curved screen to obtain scene display data.

7. A 3D game scene adaptation device based on a curved screen, characterized in that: Used to execute the 3D game scene adaptation method based on a curved screen according to any one of claims 1 to 6, the device comprising: A sampling module is used to perform three-dimensional coordinate sampling on the curved screen surface, construct a curved screen space model, and perform dynamic tracking in a 3D game scene according to the curved screen space model to obtain scene mapping data; An analysis module, used for constructing a spatial distortion vector and performing compensation transformation analysis based on the scene mapping data to obtain target scene data; A construction module, used to calculate a surface feature tensor according to the target scene data, construct a control grid node using the surface feature tensor, impose deformation constraints on the control grid nodes and solve the deformation energy equation to obtain scene deformation control data; A creation module is used to create a rendering control instruction based on the scene deformation control data, and perform scene state acquisition and space adaptation calculation according to the rendering control instruction to obtain scene display data.

Images