Game image generation method and system based on 3D rendering engine

Abstract

The invention relates to the technical field of game image generation, in particular to a game image generation method and system based on a 3D rendering engine. The method comprises the following steps: acquiring an intersection object which has an intersection relationship with the surface of a visual cone in a 3D model of a game engine; suspicious objects are screened according to the coincidence condition of the bounding box of the intersecting objects and the visual cone, the change condition of the physical engine information and the distance between the intersecting objects on the same plane of the visual cone; determining a user view angle following time difference according to the consistency between the space vector of the historical intersection object and the corresponding camera view angle vector and the matching relationship between the change of the space vector of the historical intersection object and the change of the corresponding camera view angle vector; and calculating the attention of the suspicious object by integrating the projection distribution of the suspicious object and the matched object relative to the plane aligned with the camera lens and the change of the space vector when the state of the suspicious object changes, thereby generating a game image. The rendering effect of the game image is ensured.

Description

Technical Field

[0001] The present invention relates to the technical field of game image generation, and in particular to a game image generation method and system based on a 3D rendering engine. Background Art

[0002] As the core component of game image generation, 3D rendering engines carry the important task of transforming the virtual world into a real experience. By finely managing scenes, models, lighting, and materials, 3D rendering engines are able to generate impressive game images in real time. There are currently two types of rendering technologies used in the industry: real-time rendering and offline rendering. When an offline renderer renders a scene, each change needs to be recompiled, even if the change may be trivial, such as changing the color of the light, changing the camera angle, moving the character, etc. The real-time rendering engine can see the final result almost immediately. However, real-time rendering needs to work under limited computing resources, which limits the complexity and image quality. Among them, view distance clipping and level of detail are two commonly used optimization techniques in real-time rendering engines. Their main purpose is to reduce the number of objects and geometric complexity that the rendering engine needs to process, thereby improving rendering efficiency. The conventional LOD (Level of Detail) level is matched according to the distance between the object and the camera's perspective, but it is only suitable for static images. When there are dynamic elements in the picture and when the camera's perspective is constantly switching, this LOD matching strategy wastes rendering resources, and causes problems such as abrupt pictures and torn details, affecting the 3D rendering effect of the game image. Summary of the invention

[0003] In order to solve the problems of abrupt images and detail tearing when the existing 3D rendering method generates game images, the purpose of the present invention is to provide a method and system for generating game images based on a 3D rendering engine. The technical solutions adopted are as follows:

[0004] In a first aspect, the present invention provides a method for generating a game image based on a 3D rendering engine, the method comprising the following steps:

[0005] Get the intersecting objects in the 3D model of the current game engine that intersect with the surface of the viewing frustum;

[0006] The eye-attracting degree of each intersecting object is obtained based on the overlap between the bounding box of each intersecting object and the visual frustum, the change of the physical engine information of each intersecting object, and the relative distance between the intersecting objects on the same surface of the visual frustum; the eye-attracting degree is used to filter suspicious objects;

[0007] According to the consistency between the spatial vector of the historical intersection object in the historical log data and the corresponding camera view vector, the consistency index corresponding to the historical intersection object is obtained; according to the matching relationship between the change of the spatial vector of the historical intersection object and the change of the corresponding camera view vector and the consistency index, the user view following time difference is determined; based on the difference between the relative distance between each suspicious object and the camera and the relative distance between other objects other than the suspicious object and the camera, the matching object of each suspicious object is screened;

[0008] The visual relationship factor between the suspicious object and each of its matching objects is obtained by combining the projection distribution of the suspicious object and its matching object relative to the plane aligned with the camera lens and the change of the spatial vector when the state of the suspicious object changes; the attention degree of each suspicious object is calculated by combining the spatial distance between the suspicious object and its matching object, the visual relationship factor and the user's perspective following time difference;

[0009] The LOD level model is determined based on the attention level to generate game images.

[0010] Preferably, obtaining the eye-catching degree of each intersecting object according to the overlap between the bounding box of each intersecting object and the visual frustum, the change of the physical engine information of each intersecting object, and the relative distance between the intersecting objects on the same surface of the visual frustum includes:

[0011] Obtaining the overlap volume of the bounding box of the intersecting object to be analyzed and the visual frustum, and determining the ratio of the overlap volume to the volume of the bounding box of the intersecting object to be analyzed as the overlap ratio of the object to be analyzed;

[0012] Obtaining the projection area of ​​the intersecting object to be analyzed in the plane to which the camera lens is pointed, and the area of ​​the surface with the largest intersection area among all the intersecting planes of the intersecting object to be analyzed and the viewing cone; determining the ratio between the projection area and the area of ​​the surface with the largest intersection area as the projection ratio;

[0013] The ratio between the projection ratio and the overlap ratio is used as the overlap factor of the intersecting object to be analyzed;

[0014] According to each type of physical engine information of the intersecting object to be analyzed in the previous frame image and the current frame image, the normalized result of the change rate corresponding to each type of physical engine information of the intersecting object to be analyzed is calculated; the physical engine information corresponding to the maximum normalized result of the change rate is determined as the motion parameter of the intersecting object to be analyzed;

[0015] Normalizing the inverse proportionality of the spatial distance between the bounding box of the intersecting object to be analyzed and each other intersecting object on the surface of the visual frustum intersecting with the object as a first weight corresponding to each other intersecting object; performing weighted averaging on the motion parameters of each other intersecting object using the first weight to obtain a dynamic reference volume of the intersecting object to be analyzed;

[0016] The ratio of the value of the motion parameter to the dynamic reference value of the intersecting object to be analyzed is determined as the eye-catching degree of the intersecting object to be analyzed;

[0017] The intersecting object to be analyzed is any intersecting object in the 3D model of the current game engine that has an intersecting relationship with the surface of the viewing frustum; wherein the intersecting object is an object whose bounding box has an intersecting relationship with any surface of the viewing frustum.

[0018] Preferably, the method of screening suspicious objects by using eye-attractiveness includes: determining intersecting objects whose normalized eye-attractiveness result is greater than a preset eye-attractiveness threshold as suspicious objects.

[0019] Preferably, obtaining the consistency index corresponding to the historical intersection object according to the consistency between the spatial vector of the historical intersection object in the historical log data and the corresponding camera view vector includes:

[0020] Calculate the vector difference between the spatial vector of each historical intersection object and the corresponding camera view vector respectively; average and normalize all the vector differences corresponding to each historical intersection object in the historical log data, and use the difference between the constant 1 and the normalization result as the consistency index corresponding to each historical intersection object;

[0021] Among them, the spatial vector of the historical intersection object is composed of the translation parameters, up and down tilt parameters, rotation parameters and speed of the historical intersection object; the camera viewing angle vector is composed of the camera's translation parameters, up and down tilt parameters, rotation parameters and the FOV used to characterize the camera's zooming in and out.

[0022] Preferably, the determining of the user perspective following time difference according to the matching relationship between the change of the space vector of the historical intersecting object and the change of the corresponding camera perspective vector and the consistency index comprises:

[0023] Calculate the difference between two adjacent space vectors of the candidate object in the historical log data, record it as the change vector of the candidate object, and all the change vectors of the candidate object constitute a first change vector sequence;

[0024] Calculate the difference between two adjacent viewing angle vectors of the camera in the historical log data, record it as the change vector of the camera, and all the change vectors of the camera constitute a second change vector sequence;

[0025] Perform DTW matching on the first change vector sequence and the second change vector sequence to obtain matching pairs; calculate the time difference corresponding to the two elements in each matching pair, wherein the time difference is the difference obtained by subtracting the element in the first change vector sequence from the element in the second change vector sequence in the matching pair; the candidate object is any historical intersection object;

[0026] The normalized result of the consistent index corresponding to each historical intersection object is used as a weight, and the time differences corresponding to all historical intersection objects are weighted and averaged to obtain the user perspective following time difference.

[0027] Preferably, the step of screening the matching object of each suspicious object based on the difference between the relative distance between each suspicious object and the camera and the relative distance between other objects other than the suspicious object and the camera comprises:

[0028] Recording the relative distance between the suspicious object to be evaluated and the camera as a first distance, and recording the relative distance between each object except the suspicious object to be evaluated and the camera as a second distance corresponding to each object except the suspicious object to be evaluated;

[0029] Determine the objects other than the suspicious object to be evaluated that correspond to the second distance being greater than the first distance as matching objects of the suspicious object to be evaluated;

[0030] The suspicious object to be evaluated is any suspicious object.

[0031] Preferably, the step of synthesizing the projection distribution of the suspicious object and its matching object relative to the plane aligned with the camera lens and the change of the space vector when the state of the suspicious object changes to obtain the visual relationship factor of the suspicious object and each of its matching objects includes:

[0032] For the suspicious object to be evaluated and any of its matching objects:

[0033] If the projection area of ​​the suspicious object to be evaluated relative to the plane to which the camera lens is pointed is greater than or equal to the projection area of ​​any matching object relative to the plane to which the camera lens is pointed, then the spatial vector corresponding to the minimum degree of spatial vector transformation when the suspicious object to be evaluated is transformed from the current state to the maximum projection area of ​​the suspicious object to be evaluated relative to the plane to which the camera lens is pointed is determined as the minimum transformation vector of any matching object;

[0034] Calculating the Euclidean norm of the minimum conversion vector to obtain a first conversion value;

[0035] The sum of the spatial distance between the suspicious object to be evaluated and any matching object and the preset adjustment parameter, and the normalized result of the ratio between the first conversion value and the sum are determined as the visual relationship factor between the suspicious object to be evaluated and any matching object; wherein the preset adjustment parameter is a value greater than 0.

[0036] Preferably, the step of calculating the attention level of each suspicious object by combining the spatial distance between the suspicious object and its matching object, the visual relationship factor, and the user's perspective following time difference comprises:

[0037] The attention degree of the suspicious object to be evaluated is calculated according to the visual relationship factor between the suspicious object to be evaluated and all its matching objects, the spatial distance value between the suspicious object to be evaluated and all its matching objects, and the user perspective following time difference. The visual relationship factor and the spatial distance value are both positively correlated with the attention degree, and the user perspective following time difference is negatively correlated with the attention degree.

[0038] Preferably, the method of determining the LOD level model based on the attention level to generate the game image includes:

[0039] All LOD models are divided into a preset number of levels, and all attention levels are also divided into a preset number of levels. The corresponding LOD levels are matched according to the numerical values ​​of the attention levels of different suspicious objects, and the corresponding rendering resources are allocated according to the LOD levels to obtain game images.

[0040] In a second aspect, the present invention provides a game image generation system based on a 3D rendering engine, the system comprising:

[0041] An intersecting object acquisition module is used to obtain intersecting objects in the 3D model of the current game engine that have an intersecting relationship with the surface of the viewing frustum;

[0042] A suspicious object screening module is used to obtain the eye-attracting degree of each intersecting object based on the overlap between the bounding box of each intersecting object and the visual frustum, the change of the physical engine information of each intersecting object, and the relative distance between the intersecting objects on the same surface of the visual frustum; and screen suspicious objects using the eye-attracting degree;

[0043] A matching object screening module is used to obtain a consistency index corresponding to the historical intersection object according to the consistency between the spatial vector of the historical intersection object in the historical log data and the corresponding camera view vector; determine the user view following time difference according to the matching relationship between the change of the spatial vector of the historical intersection object and the change of the corresponding camera view vector and the consistency index; and screen the matching object of each suspicious object based on the difference between the relative distance between each suspicious object and the camera and the relative distance between other objects other than the suspicious object and the camera;

[0044] The attention calculation module is used to obtain the visual relationship factor between the suspicious object and each of its matching objects by comprehensively analyzing the projection distribution of the suspicious object and its matching object relative to the plane aligned with the camera lens and the change of the space vector when the state of the suspicious object changes; and calculate the attention degree of each suspicious object by combining the spatial distance between the suspicious object and its matching object, the visual relationship factor and the user's perspective following time difference;

[0045] The game image generation module is used to generate game images based on the LOD level model determined by the attention level.

[0046] The present invention has at least the following beneficial effects:

[0047] The present invention first obtains all intersecting objects that have an intersecting relationship with the surface of the viewing frustum in the 3D model of the current game engine, and then calculates the eye-catching degree of each intersecting object according to the overlap of the bounding box of the intersecting object with the viewing frustum, the change of the physical engine information of the intersecting object and the relative distance between the intersecting objects on the same surface of the viewing frustum, screens out all suspicious objects, and estimates the user's perspective following time difference according to the relative changes of historical intersecting objects and cameras in the user's historical log data; considering that different objects may interact during movement, the matching objects of each suspicious object are screened out based on the difference between the relative distance between each suspicious object and the camera and the relative distance between other objects except the suspicious object and the camera, and the alignment of the suspicious object with its matching object relative to the camera lens is calculated. The projection distribution of the plane, the change of the space vector when the state of the suspicious object changes, and the time difference of the user's perspective following are used to obtain the attention degree of each suspicious object. The LOD level model is further determined based on the attention degree to generate the game image. The method provided by the present invention takes into account the characteristics of low observation frequency when following dynamic objects during the actual observation process of the user's camera perspective, and the guiding effect of the camera perspective when the object at the edge of the picture moves. By calculating the attention degree of the intersecting object as the LOD level matching standard, its rendering resources are reasonably allocated, avoiding the sudden switching of the picture, tearing, delay and other problems when the object moves and guides the user's camera perspective to follow, optimizing the resource allocation strategy, and obtaining the LOD level matching method that best suits the user's observation habits, which not only ensures the rendering effect of the game image, but also saves rendering resources. BRIEF DESCRIPTION OF THE DRAWINGS

[0048] In order to more clearly illustrate the technical solutions and advantages in the embodiments of the present invention or the prior art, the drawings required for use in the embodiments or the prior art descriptions are briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0049] Figure 1 A flowchart of a method for generating a game image based on a 3D rendering engine provided by an embodiment of the present invention;

[0050] Figure 2 A schematic diagram of a visual cone provided by an embodiment of the present invention;

[0051] Figure 3 Switch the previous picture to the camera perspective;

[0052] Figure 4 This is the picture after the camera perspective is switched;

[0053] Figure 5 A structural block diagram of a game image generation system based on a 3D rendering engine provided in an embodiment of the present invention. DETAILED DESCRIPTION

[0054] In order to further explain the technical means and effects adopted by the present invention to achieve the predetermined purpose of the invention, the game image generation method and system based on the 3D rendering engine proposed by the present invention are described in detail as follows in combination with the accompanying drawings and preferred embodiments.

[0055] Unless defined otherwise, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this invention belongs.

[0056] The specific scheme of the game image generation method and system based on the 3D rendering engine provided by the present invention is described in detail below with reference to the accompanying drawings.

[0057] Game image generation method based on 3D rendering engine embodiment:

[0058] The specific scenario targeted by this embodiment is: in the process of 3D rendering of game images, in order to improve the generation effect of game images, combined with the low observation frequency of the user's camera perspective when following dynamic objects during actual observation, and the guiding effect of the camera perspective when objects at the edge of the screen move, the resource allocation strategy is optimized to avoid sudden switching of the screen and the occurrence of problems such as tearing and delay, thereby ensuring the rendering effect of the game image.

[0059] This embodiment proposes a method for generating game images based on a 3D rendering engine. Figure 1 As shown, the game image generation method based on the 3D rendering engine of this embodiment includes the following steps:

[0060] Step S1, obtaining an intersecting object in a 3D model of the current game engine that has an intersecting relationship with a surface of a viewing frustum.

[0061] Create a 3D scene in the game engine, including models, cameras, lights, etc.; the first perspective in the game screen is the camera perspective, and the game space is a 3D model. Graphics rendering refers to the process of converting 3D models, textures, lighting and other materials into two-dimensional images through a series of calculations and displaying them on the screen to obtain the game image screen within the camera perspective; define a view cone based on the position and direction of the camera. This view cone consists of six planes, namely the near clipping plane, the far clipping plane and four side planes, such as Figure 2 As shown, this figure is a schematic diagram of the visual cone.

[0062] The objects in the 3D model of the current game engine are segmented according to the spatial position, using an octree (3D) to obtain the segmentation results; it is similar to a binary tree, but each node can have up to 8 child nodes, because the three-dimensional space can be divided into 8 equal-sized parts, each node represents a cubic space, the root node is a 3D model, each cubic space can be further divided into 8 sub-cubes, these sub-cubes become the child nodes of the current node, until the number of objects in the node is insufficient for further segmentation, or when the preset maximum depth is reached, it stops, and several child nodes are obtained, which can provide indexing to make the spatial query of the 3D model (such as range query, nearest neighbor query) faster. The maximum depth is set by the implementer according to the specific situation.

[0063] The 3D model space is composed of several elements, each of which is an object, such as a tree, a building, or a character. Each element should have a unique identifier, such as an ID or a name. For each element object in the 3D space, the bounding box of each object is obtained. If the bounding box of the object is completely within the viewing cone, the object is visible; if the bounding box is completely outside the viewing cone, the object is invisible; if the bounding box intersects with the viewing cone, further refinement of the test is required to determine its visibility and optimize the rendering effect of the object. Therefore, in this embodiment, the object whose bounding box and any face of the viewing cone in the 3D model of the current game engine have an intersection relationship is recorded as an intersection object, wherein any face of the viewing cone is the near clipping plane, the far clipping plane, and any one of the four side faces of the viewing cone.

[0064] So far, this embodiment has obtained multiple intersecting objects in the 3D model of the current game engine.

[0065] Step S2, obtaining the eye-attracting degree of each intersecting object according to the overlap between the bounding box of each intersecting object and the visual frustum, the change of the physical engine information of each intersecting object, and the relative distance between the intersecting objects on the same surface of the visual frustum; and screening suspicious objects by using the eye-attracting degree.

[0066] For each object, the developer created multiple different versions (LOD levels) of the model. Each version has the same basic shape and appearance, but with a different level of detail. LOD is a method of reducing the number of polygons based on the distance of a 3D object from the camera, and each LOD level has a different polygon count. Typically, objects closer to the camera use high-detail models, while objects farther away use low-detail models. Based on the calculated distance, the appropriate LOD level is selected for rendering.

[0067] The change of camera perspective may be partly determined by the user's attention to different objects. For example, if the camera perspective suddenly moves in a certain direction, it may be because the object of interest appears at the boundary of the viewing cone, and the user adjusts the perspective for better observation. Therefore, when analyzing the relationship between the camera perspective and the object LOD level, the guiding effect of the intersecting objects at the edge of the viewing cone on the camera perspective should be considered.

[0068] like Figure 3 and Figure 4 As shown, Figure 3 The picture before the camera perspective switches. Figure 4 This is the picture after the camera perspective is switched. Observe the positions of objects 1, 2, and 3 in the left picture. The right picture has a perspective shifted to the right and the viewing distance is extended relative to the left picture. Object 2 was originally at the edge of the left picture, but appeared in the center of the right picture after the perspective changed. In addition, object 3 (a rock) rolled between objects 1 and 2. Objects 2 and 3 are both intersecting objects at the edge of the viewing cone. It can be inferred that the user noticed the dynamic rolling of object 3 in advance from the left perspective, and then the perspective followed object 3 to move to the right, the viewing distance was extended to observe a more complete picture, and finally the center of the perspective just fell on the position where the rock stopped rolling. In this process, the camera perspective is considered to be guided and influenced by object 3. At this time, it means that the game screen and the user have a deeper interaction.

[0069] When switching LOD, you need to pay attention to objects that may have a guiding role at the edge of the view frustum, and focus on depicting and smoothing the fluidity of the objects.

[0070] Next, this embodiment is described by taking any intersecting object in the 3D model of the current game engine that has an intersecting relationship with the surface of the viewing frustum as an example, and other intersecting objects can be processed by the method provided in this embodiment.

[0071] Specifically, any intersecting object in the 3D model of the current game engine that has an intersection relationship with the surface of the viewing cone is recorded as the intersecting object to be analyzed. The overlapping volume of the bounding box of the intersecting object to be analyzed and the viewing cone is obtained, and the ratio of the overlapping volume to the volume of the bounding box of the intersecting object to be analyzed is determined as the overlap ratio of the object to be analyzed; the projection area of ​​the intersecting object to be analyzed in the plane to which the camera lens is aimed, and the area of ​​the surface with the largest intersection area among all the intersecting planes between the intersecting object to be analyzed and the viewing cone are obtained; the ratio between the projection area and the area of ​​the surface with the largest intersection area is determined as the projection ratio. The ratio between the projection ratio and the overlap ratio is used as the overlap factor of the intersecting object to be analyzed; the larger the projection ratio and the smaller the overlap ratio, the more likely it is that the projection direction is orthogonal to the main direction of the intersecting object, and the full view of the 3D object can be almost observed from a 2D perspective, which may be due to the shape of the object, the focal length of the camera, the perspective effect, and other reasons.

[0072] By querying the physical components of the intersecting objects, the physical engine information of the intersecting objects is obtained. In this embodiment, the physical engine information includes speed translation parameters, up and down tilt parameters, rotation parameters and speed.

[0073] According to each type of physical engine information of the intersecting object to be analyzed in the previous frame image and the current frame image, the normalized result of the change rate corresponding to each type of physical engine information of the intersecting object to be analyzed is calculated; the physical engine information corresponding to the maximum normalized result of the change rate is determined as the motion parameter of the intersecting object to be analyzed; the linear normalization method can be used to process the change rate, which is a prior art and will not be described in detail here. The inverse proportional normalized result of the spatial distance between the bounding box of the intersecting object to be analyzed and each other intersecting object on the surface of the visual cone that has an intersecting relationship with it is recorded as the first weight corresponding to each other intersecting object, and the closer the spatial distance, the greater the first weight; in this embodiment, the exponential function value with a natural constant as the base and a negative spatial distance as the exponent is used as the inverse proportional normalized result of the spatial distance. The motion parameter of each other intersecting object is weighted and averaged using the first weight, and the obtained result is used as the dynamic reference of the intersecting object to be analyzed. The smaller the dynamic reference amount, the more prominent the dynamic change of the corresponding object. The ratio of the numerical value of the motion parameter to the dynamic reference amount of the intersecting object to be analyzed is determined as the eye-catching degree of the intersecting object to be analyzed. The greater the eye-catching degree, the larger the visual area and more dynamic references the corresponding intersecting object can provide in its projection direction, and it is easier to be noticed by the user's peripheral vision.

[0074] By adopting the above method, the eye-attracting degree of each intersecting object can be obtained, and the eye-attracting degree of each intersecting object is normalized respectively, so that the normalized value is in (0, 1), and the intersecting object whose normalized result of eye-attracting degree is greater than the preset eye-attracting threshold is determined as a suspicious object, that is, multiple suspicious objects are screened out from all the intersecting objects that have an intersection relationship with the surface of the visual cone in the 3D model of the current game engine. The suspicious object is the object that may guide the camera's viewing angle in the current frame. When normalizing the eye-attracting degree, the maximum and minimum value method can be used for normalization. This normalization method is a prior art and will not be described in detail here. In this embodiment, the preset eye-attracting threshold is 0.7. In specific applications, the implementer can set it according to the specific situation.

[0075] Step S3, according to the consistency between the spatial vector of the historical intersection object in the historical log data and the corresponding camera view vector, obtain the consistency index corresponding to the historical intersection object; according to the matching relationship between the change of the spatial vector of the historical intersection object and the change of the corresponding camera view vector and the consistency index, determine the user view following time difference; based on the difference between the relative distance between each suspicious object and the camera and the relative distance between other objects except the suspicious object and the camera, screen the matching object of each suspicious object.

[0076] The client uses an API interface call method, and after the identity authentication and authorization process, retrieves and extracts the historical log data of the specific user's game behavior from the server; the historical log data contains the user's screen switching habit information; analogous to the method of obtaining the intersection object in the 3D model of the current game engine in step S1 that has an intersection relationship with the surface of the visual cone, obtain the intersection object in each frame image in the historical log data, and record the intersection object obtained at this time as the historical intersection object. For any historical intersection object, the translation parameter, up and down tilt parameter, rotation parameter and speed of the historical intersection object constitute the space vector of the historical intersection object, wherein the translation parameter, up and down tilt parameter and rotation parameter are expressed by three-dimensional vector, Euler angle and quaternion respectively; using this method, the space vector of each historical intersection object can be obtained. Next, we will construct the camera's viewing angle vector corresponding to each frame of the historical log data. The camera viewing angle vector is used to describe basic camera lens switching actions such as zooming in and out, translation, tilting, and rotation. The translation of the camera's viewing angle is expressed by a three-dimensional vector, the tilt is expressed by Euler angles, the rotation is expressed by quaternions, and the zooming in and out is expressed by a scalar value (FOV). Therefore, the camera's translation parameters, tilt parameters, rotation parameters, and the FOV used to characterize the camera's zooming in and out constitute the camera's viewing angle vector.

[0077] The camera lens zooms in and out with the purpose of enlarging and reducing the picture, which should be to follow the dynamic details of the object. The FOV value is adjusted to zoom out. Generally, when the speed of each object is large, the viewing angle is zoomed out to obtain a wider field of view, which is convenient for observing high-speed moving objects. Therefore, the speed in the spatial vector of the historical intersecting object corresponds to the FOV in the camera's viewing angle vector, and the two are in a directly proportional relationship. The speed in the spatial vector of the intersecting object and the FOV in the camera's viewing angle vector are normalized to eliminate the dimension. It should be noted that the spatial vector and viewing angle vector mentioned later are all spatial vectors and viewing angle vectors after this normalization processing.

[0078] The difference between the spatial vector of each historical intersection object and the corresponding camera view vector is calculated respectively, and the difference is used as the corresponding vector difference; all the vector differences corresponding to each historical intersection object in the historical log data are averaged and normalized using the sigmoid function, and the difference obtained by subtracting the normalization result from the constant 1 is used as the consistency index corresponding to each historical intersection object.

[0079] In fact, the consistency between the camera view vector and the spatial vector of any object is not very high, because the switching speed of the camera view is often slower than the change in the spatial state of the object. For example, the camera view on the user side cannot completely follow the entire process of the rock rolling frame by frame. Therefore, the change in the timing of the camera view vector can be regarded as a low-sampling rate following process.

[0080] This embodiment takes any historical intersection object as an example for explanation. The method provided in this embodiment can be used to process other historical intersection objects. Specifically, any historical intersection object is recorded as a candidate object, the difference between two adjacent spatial vectors of the candidate object in the historical log data is calculated, and the difference is recorded as the change vector of the candidate object. All the change vectors of the candidate object are arranged in chronological order, and the sequence obtained at this time is recorded as the first change vector sequence; the difference between two adjacent perspective vectors of the camera in the historical log data is calculated and recorded as the change vector of the camera; all the change vectors of the camera in the historical log data are arranged in chronological order, and the sequence obtained at this time is recorded as the second change vector sequence. It should be noted that: when calculating the difference between two adjacent spatial vectors, the difference is obtained by subtracting the spatial vector with a later time from the spatial vector with a earlier time; similarly, when calculating the difference between two adjacent perspective vectors, the difference is obtained by subtracting the perspective vector with a later time from the perspective vector with a earlier time.

[0081] Perform DTW (Dynamic Time Warping) matching on the elements in the first change vector sequence and the elements in the second change vector sequence to obtain matching pairs, each matching pair contains two elements, one of which is an element in the first change vector sequence and the other is an element in the second change vector sequence; it should be noted that the matching pairs extracted in this embodiment are all elements with a one-to-one matching relationship. Calculate the time difference corresponding to the two elements in each matching pair respectively, where the time difference is the difference obtained by subtracting the element in the first change vector sequence from the element in the second change vector sequence in the matching pair; each matching pair corresponds to a time difference. In the matching result, if the time difference is positive, it means that the spatial vector of the object changes first, and the camera perspective changes after a short delay, and if the time difference is negative, it means that the spatial change of the object is expected in advance from the user's perspective, and the camera perspective vector is positioned in advance. The normalized result of the consistent index corresponding to each historical intersection object is used as the weight corresponding to each historical intersection object, and the time differences corresponding to all historical intersection objects are weighted and averaged, and the obtained result is used as the user perspective following time difference.

[0082] When a suspicious object moves, if it successfully attracts the user's camera view, the movement of the suspicious object may interact with other objects. In order to smooth the visual effect, the visual relationship between the suspicious object and other objects needs to be considered.

[0083] Next, this embodiment is described by taking any suspicious object as an example, and other suspicious objects can be processed by the method provided in this embodiment.

[0084] Any suspicious object is recorded as a suspicious object to be evaluated, the relative distance between the suspicious object to be evaluated and the camera is recorded as a first distance, and the relative distance between each object except the suspicious object to be evaluated and the camera is recorded as a second distance corresponding to each object except the suspicious object to be evaluated; when the second distance is greater than the first distance, it means that the suspicious object is a foreground relative to the objects except the suspicious object, and the objects except the suspicious object to be evaluated corresponding to the second distance being greater than the first distance are determined as matching objects of the suspicious object to be evaluated, that is, multiple matching objects of the suspicious object to be evaluated are screened.

[0085] By adopting the above method, multiple matching objects for each suspicious object can be screened out.

[0086] Step S4, comprehensively analyzing the projection distribution of the suspicious object and its matching object relative to the plane aligned with the camera lens and the change of the spatial vector when the state of the suspicious object changes, obtains the visual relationship factor of the suspicious object and each of its matching objects; combining the spatial distance between the suspicious object and its matching object, the visual relationship factor and the user's perspective following time difference, calculates the attention degree of each suspicious object.

[0087] By calling the API provided by the graphics library or the physics engine, the posture of the suspicious object is obtained, and the different projection areas it presents in the direction of the camera's viewing angle are obtained.

[0088] Next, the suspicious object to be evaluated is still used as an example for explanation. Specifically, for the suspicious object to be evaluated and any of its matching objects: if the projection area of ​​the suspicious object to be evaluated relative to the plane to which the camera lens is aimed is greater than or equal to the projection area of ​​any of the matching objects relative to the plane to which the camera lens is aimed, the spatial vector corresponding to the minimum degree of spatial vector transformation when the suspicious object to be evaluated is transformed from the current state to the maximum projection area of ​​the plane to which the camera lens is aimed is determined as the minimum transformation vector of any of the matching objects; the smaller the degree of spatial vector transformation, the greater the probability of visual relationship interaction between the two. It should be noted that the degree of spatial vector transformation can be characterized by the change in the size of the spatial vector, and the method for obtaining the change in the size of the vector is a prior art, which will not be described in detail here. The Euclidean norm of the elements in the minimum transformation vector is calculated, and the calculation result is used as the first transformation value; the sum of the spatial distance between the suspicious object to be evaluated and the any of the matching objects and the preset adjustment parameter, the normalized result of the ratio between the first transformation value and the sum value is determined as the visual relationship factor between the suspicious object to be evaluated and any of its matching objects; wherein the preset adjustment parameter is a value greater than 0. The purpose of introducing a preset adjustment parameter in the calculation process of the visual relationship factor is to prevent the denominator from being 0. In this embodiment, the preset adjustment parameter is 0.01. In specific applications, the implementer can set it according to the specific situation.

[0089] When the motion state of the suspicious object to be evaluated changes, the user's camera perspective may change accordingly according to the user's perspective following time difference. Therefore, the camera's attention to the object to be evaluated is evaluated based on the spatial distance between the suspicious object to be evaluated and its matching object, the visual relationship factor, and the user's perspective following time difference.

[0090] Specifically, the attention degree of the suspicious object to be evaluated is calculated according to the visual relationship factor between the suspicious object to be evaluated and all its matching objects, the spatial distance value between the suspicious object to be evaluated and all its matching objects, and the user perspective following time difference, the visual relationship factor and the spatial distance value are both positively correlated with the attention degree, and the user perspective following time difference is negatively correlated with the attention degree.

[0091] Among them, a positive correlation means that the dependent variable will increase as the independent variable increases, and the dependent variable will decrease as the independent variable decreases. It can be an additive relationship, a multiplicative relationship, etc., which is determined by actual application; a negative correlation means that the dependent variable will decrease as the independent variable increases, and the dependent variable will increase as the independent variable decreases. It can be a subtractive relationship, a division relationship, etc., which is determined by actual application.

[0092] In this embodiment, a specific calculation formula for the attention degree is given, and the attention degree of the suspicious object to be evaluated can be expressed as:

[0093]

[0094] Among them, E represents the attention of the suspicious object to be evaluated, N represents the number of matching objects of the suspicious object to be evaluated, and Z n represents the visual relationship factor between the suspicious object to be evaluated and its nth matching object, C n It represents the spatial distance between the suspicious object to be evaluated and its nth matching object, V represents the user's perspective following time difference, and norm() represents the normalization function.

[0095] It can be regarded as the event observation frequency, and the visual relationship factor between the suspicious object to be evaluated and its nth matching object can be regarded as the probability of event occurrence. The product of the two is the expected value of the visual interaction between the suspicious object to be evaluated and its nth matching object captured when the user's camera angle stays. The expected values ​​of the suspicious object to be evaluated and all matching objects are averaged and normalized to obtain the attention degree of the suspicious object. It should be noted that: when the projection area of ​​the suspicious object to be evaluated relative to the plane pointed by the camera lens is smaller than the projection area of ​​one of its matching objects relative to the plane pointed by the camera lens, the visual relationship factor of the suspicious object to be evaluated and the corresponding matching object at this time is set to 0, and participates in the calculation of the attention degree of the suspicious object to be evaluated.

[0096] By adopting the above method, the attention degree of each suspicious object can be obtained. This embodiment estimates the following speed according to the user's screen switching habits, and then obtains the observation frequency of the user's camera perspective during the following process when the suspicious object may move toward any other object, and then obtains the visual interaction probability of each suspicious object with other objects according to the minimum spatial vector change of each suspicious object; obtains the expected value of each suspicious object interacting with other objects and the event being observed by the user according to the observation frequency and the visual interaction probability, and finally obtains the attention degree of each suspicious object.

[0097] Step S5, determining the LOD level model based on the attention level to generate a game image.

[0098] In step S4, the present embodiment calculates the attention of all suspicious objects in the current picture. Next, the corresponding LOD level model is matched according to the attention of all suspicious objects in the current picture. Specifically, all LOD models are divided into a preset number of levels, and all attention is also divided into a preset number of levels. In the present embodiment, the preset number is 5, that is, all LOD models are divided into 5 levels, and all suspicious objects are also divided into five levels according to the size of the attention of all suspicious objects. Then, the corresponding LOD level is matched according to the attention level of the numerical value of the attention of different suspicious objects, and the corresponding rendering resources are allocated according to the LOD level. The rendering resources include light, texture, shadow, etc.; the vertices of the 3D model are transformed, including model transformation, view transformation and projection transformation, and the vertex coordinates are transformed from the model space to the screen space. The transformed vertex data is converted into pixel data to generate a rasterized image. According to the rendering resource allocation strategy in the above steps, rendering effects are applied to each pixel, including lighting, texture mapping, shadow and other effects, and the final color value is calculated. Finally, multiple layers (such as background, foreground, UI, etc.) are merged and output to the screen for display, thus completing the generation and display of the game image.

[0099] Compared with the shortcomings of the traditional method of obtaining the LOD weight based on the distance and matching the corresponding LOD level, this embodiment takes into account the low observation frequency of the user's camera perspective when following dynamic objects during actual observation and the guiding effect of the camera perspective when objects at the edge of the screen move. By calculating the attention degree of the intersecting objects as the LOD level matching standard, its rendering resources are reasonably allocated, avoiding sudden switching of the screen and tearing, delay and other problems when the objects move and guide the user's camera perspective to follow, optimizing the resource allocation strategy, and obtaining the LOD level matching method that best suits the user's observation habits, which not only saves rendering resources but also improves the smoothness of game operation.

[0100] Example of a game image generation system based on a 3D rendering engine:

[0101] like Figure 5 As shown, the figure shows a structural block diagram of a game image generation system based on a 3D rendering engine, the system includes an intersecting object acquisition module, a suspicious object screening module, a matching object screening module, an attention calculation module and a game image generation module;

[0102] The intersection object acquisition module is used to acquire the intersection objects in the 3D model of the current game engine that have an intersection relationship with the surface of the viewing frustum;

[0103] A suspicious object screening module is used to obtain the eye-attracting degree of each intersecting object based on the overlap between the bounding box of each intersecting object and the visual frustum, the change of the physical engine information of each intersecting object, and the relative distance between the intersecting objects on the same surface of the visual frustum; and screen suspicious objects using the eye-attracting degree;

[0104] A matching object screening module is used to obtain a consistency index corresponding to the historical intersection object according to the consistency between the spatial vector of the historical intersection object in the historical log data and the corresponding camera view vector; determine the user view following time difference according to the matching relationship between the change of the spatial vector of the historical intersection object and the change of the corresponding camera view vector and the consistency index; and screen the matching object of each suspicious object based on the difference between the relative distance between each suspicious object and the camera and the relative distance between other objects other than the suspicious object and the camera;

[0105] The attention calculation module is used to obtain the visual relationship factor between the suspicious object and each of its matching objects by comprehensively analyzing the projection distribution of the suspicious object and its matching object relative to the plane aligned with the camera lens and the change of the space vector when the state of the suspicious object changes; and calculate the attention degree of each suspicious object by combining the spatial distance between the suspicious object and its matching object, the visual relationship factor and the user's perspective following time difference;

[0106] The game image generation module is used to generate game images based on the LOD level model determined by the attention level.

[0107] It should be understood that Figure 5 The structural block diagram of the game image generation system based on the 3D rendering engine shown and its modules can be implemented in various ways. For example, in some embodiments, the system and its modules can be implemented by hardware, software or a combination of software and hardware. Among them, the hardware part can be implemented using dedicated logic; the software part can be stored in a memory and executed by an appropriate instruction execution system, such as a microprocessor or a dedicated design hardware. Those skilled in the art will understand that the above-mentioned method and system can be implemented using computer executable instructions and / or included in a processor control code, for example, such as a carrier medium such as a disk, CD or DVD-ROM, a programmable memory such as a read-only memory (firmware), or a data carrier such as an optical or electronic signal carrier. Such code is provided on the carrier medium such as a disk, CD or DVD-ROM, a programmable memory such as a read-only memory (firmware), or a data carrier such as an optical or electronic signal carrier. The system and its modules of this specification can not only be implemented by hardware circuits such as ultra-large-scale integrated circuits or gate arrays, semiconductors such as logic chips, transistors, or programmable hardware devices such as field programmable gate arrays, programmable logic devices, etc., but can also be implemented by software such as executed by various types of processors, and can also be implemented by a combination of the above-mentioned hardware circuits and software (for example, firmware).

[0108] For more details about the above modules, please refer to other places in this manual and will not be repeated here.

[0109] In other embodiments, a game image generation device based on a 3D rendering engine is also provided, including a memory and a processor. The memory is used to store executable program code, and the processor is used to call and run the executable program code from the memory, so that the device executes the above-mentioned game image generation method based on the 3D rendering engine. The device can be specifically a chip, a component or a module, and the chip may include a connected processor and a memory; wherein the memory is used to store instructions, and when the processor calls and executes the instructions, the chip can execute the game image generation method based on the 3D rendering engine provided in the above embodiment.

[0110] In other embodiments, a computer program product is also provided. When the computer program product is run on a computer, the computer executes the above-mentioned related steps to implement the game image generation method based on the 3D rendering engine provided in the above embodiment.

[0111] In other embodiments, a medium is also provided, wherein the medium stores at least one program executable by a computer, and when the at least one program is executed by a computer, the computer executes the steps in the method for generating a game image based on a 3D rendering engine in the above-mentioned embodiment, and the medium may be a computer-readable storage medium.

[0112] Among them, the provided systems, devices, products, and media are all used to execute the corresponding methods provided above. Therefore, the beneficial effects that can be achieved can refer to the beneficial effects in the corresponding methods provided above, and will not be repeated here.

[0113] It should be noted that the above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions, improvements, etc. made within the principles of the present invention should be included in the protection scope of the present invention.

Claims

1. A method for generating game images based on a 3D rendering engine, characterized in that: The method comprises the following steps: Get the intersecting objects in the 3D model of the current game engine that intersect with the surface of the viewing frustum; The eye-attracting degree of each intersecting object is obtained based on the overlap between the bounding box of each intersecting object and the visual frustum, the change of the physical engine information of each intersecting object, and the relative distance between the intersecting objects on the same surface of the visual frustum; the eye-attracting degree is used to filter suspicious objects; According to the consistency between the spatial vector of the historical intersection object in the historical log data and the corresponding camera view vector, the consistency index corresponding to the historical intersection object is obtained; according to the matching relationship between the change of the spatial vector of the historical intersection object and the change of the corresponding camera view vector and the consistency index, the user view following time difference is determined; based on the difference between the relative distance between each suspicious object and the camera and the relative distance between other objects other than the suspicious object and the camera, the matching object of each suspicious object is screened; The visual relationship factor between the suspicious object and each of its matching objects is obtained by combining the projection distribution of the suspicious object and its matching object relative to the plane aligned with the camera lens and the change of the spatial vector when the state of the suspicious object changes; the attention degree of each suspicious object is calculated by combining the spatial distance between the suspicious object and its matching object, the visual relationship factor and the user's perspective following time difference; The LOD level model is determined based on the attention level to generate game images.

2. The method for generating game images based on a 3D rendering engine according to claim 1, characterized in that: The eye-catching degree of each intersecting object is obtained according to the overlap between the bounding box of each intersecting object and the viewing frustum, the change of the physical engine information of each intersecting object, and the relative distance between the intersecting objects on the same surface of the viewing frustum, including: Obtaining the overlap volume of the bounding box of the intersecting object to be analyzed and the visual frustum, and determining the ratio of the overlap volume to the volume of the bounding box of the intersecting object to be analyzed as the overlap ratio of the object to be analyzed; Obtaining the projection area of ​​the intersecting object to be analyzed in the plane to which the camera lens is pointed, and the area of ​​the surface with the largest intersection area among all the intersecting planes of the intersecting object to be analyzed and the viewing cone; determining the ratio between the projection area and the area of ​​the surface with the largest intersection area as the projection ratio; The ratio between the projection ratio and the overlap ratio is used as the overlap factor of the intersecting object to be analyzed; According to each type of physical engine information of the intersecting object to be analyzed in the previous frame image and the current frame image, the normalized result of the change rate corresponding to each type of physical engine information of the intersecting object to be analyzed is calculated; the physical engine information corresponding to the maximum normalized result of the change rate is determined as the motion parameter of the intersecting object to be analyzed; Normalizing the inverse proportionality of the spatial distance between the bounding box of the intersecting object to be analyzed and each other intersecting object on the surface of the visual frustum intersecting with the object as a first weight corresponding to each other intersecting object; performing weighted averaging on the motion parameters of each other intersecting object using the first weight to obtain a dynamic reference volume of the intersecting object to be analyzed; The ratio of the value of the motion parameter to the dynamic reference value of the intersecting object to be analyzed is determined as the eye-catching degree of the intersecting object to be analyzed; The intersecting object to be analyzed is any intersecting object in the 3D model of the current game engine that has an intersecting relationship with the surface of the viewing frustum; wherein the intersecting object is an object whose bounding box has an intersecting relationship with any surface of the viewing frustum.

3. The method for generating game images based on a 3D rendering engine according to claim 1, characterized in that: The method of screening suspicious objects by using eye-attractiveness includes: determining intersecting objects whose normalized eye-attractiveness is greater than a preset eye-attractiveness threshold as suspicious objects.

4. The method for generating game images based on a 3D rendering engine according to claim 1, characterized in that: The obtaining of the consistency index corresponding to the historical intersection object according to the consistency between the spatial vector of the historical intersection object in the historical log data and the corresponding camera view vector includes: Calculate the vector difference between the spatial vector of each historical intersection object and the corresponding camera view vector respectively; average and normalize all the vector differences corresponding to each historical intersection object in the historical log data, and use the difference between the constant 1 and the normalization result as the consistency index corresponding to each historical intersection object; Among them, the spatial vector of the historical intersection object is composed of the translation parameters, up and down tilt parameters, rotation parameters and speed of the historical intersection object; the camera viewing angle vector is composed of the camera's translation parameters, up and down tilt parameters, rotation parameters and the FOV used to characterize the camera's zooming in and out.

5. The method for generating game images based on a 3D rendering engine according to claim 1, characterized in that: The determining of the user perspective following time difference according to the matching relationship between the change of the space vector of the historical intersecting object and the change of the corresponding camera perspective vector and the consistency index includes: Calculate the difference between two adjacent space vectors of the candidate object in the historical log data, record it as the change vector of the candidate object, and all the change vectors of the candidate object constitute a first change vector sequence; Calculate the difference between two adjacent viewing angle vectors of the camera in the historical log data, record it as the change vector of the camera, and all the change vectors of the camera constitute a second change vector sequence; Perform DTW matching on the first change vector sequence and the second change vector sequence to obtain matching pairs; calculate the time difference corresponding to the two elements in each matching pair, wherein the time difference is the difference obtained by subtracting the element in the first change vector sequence from the element in the second change vector sequence in the matching pair; the candidate object is any historical intersection object; The normalized result of the consistent index corresponding to each historical intersection object is used as a weight, and the time differences corresponding to all historical intersection objects are weighted and averaged to obtain the user perspective following time difference.

6. The method for generating game images based on a 3D rendering engine according to claim 1, characterized in that: The step of screening matching objects for each suspicious object based on the difference between the relative distance between each suspicious object and the camera and the relative distance between other objects other than the suspicious object and the camera comprises: Recording the relative distance between the suspicious object to be evaluated and the camera as a first distance, and recording the relative distance between each object except the suspicious object to be evaluated and the camera as a second distance corresponding to each object except the suspicious object to be evaluated; Determine the objects other than the suspicious object to be evaluated that correspond to the second distance being greater than the first distance as matching objects of the suspicious object to be evaluated; The suspicious object to be evaluated is any suspicious object.

7. The method for generating game images based on a 3D rendering engine according to claim 6, characterized in that: The method of synthesizing the projection distribution of the suspicious object and its matching object relative to the plane aligned with the camera lens and the change of the space vector when the state of the suspicious object changes to obtain the visual relationship factor between the suspicious object and each of its matching objects includes: For the suspicious object to be evaluated and any of its matching objects: If the projection area of ​​the suspicious object to be evaluated relative to the plane to which the camera lens is pointed is greater than or equal to the projection area of ​​any matching object relative to the plane to which the camera lens is pointed, then the spatial vector corresponding to the minimum degree of spatial vector transformation when the suspicious object to be evaluated is transformed from the current state to the maximum projection area of ​​the suspicious object to be evaluated relative to the plane to which the camera lens is pointed is determined as the minimum transformation vector of any matching object; Calculating the Euclidean norm of the minimum conversion vector to obtain a first conversion value; The sum of the spatial distance between the suspicious object to be evaluated and any matching object and the preset adjustment parameter, and the normalized result of the ratio between the first conversion value and the sum are determined as the visual relationship factor between the suspicious object to be evaluated and any matching object; wherein the preset adjustment parameter is a value greater than 0.

8. The method for generating game images based on a 3D rendering engine according to claim 6, characterized in that: The calculation of the attention degree of each suspicious object by combining the spatial distance between the suspicious object and its matching object, the visual relationship factor and the user's perspective following time difference includes: The attention degree of the suspicious object to be evaluated is calculated according to the visual relationship factor between the suspicious object to be evaluated and all its matching objects, the spatial distance value between the suspicious object to be evaluated and all its matching objects, and the user perspective following time difference. The visual relationship factor and the spatial distance value are both positively correlated with the attention degree, and the user perspective following time difference is negatively correlated with the attention degree.

9. The method for generating game images based on a 3D rendering engine according to claim 1, characterized in that: The method of determining the LOD level model based on the attention level to generate the game image includes: All LOD models are divided into a preset number of levels, and all attention levels are also divided into a preset number of levels. The corresponding LOD levels are matched according to the numerical values ​​of the attention levels of different suspicious objects, and the corresponding rendering resources are allocated according to the LOD levels to obtain game images.

10. A game image generation system based on a 3D rendering engine, characterized in that: The system includes: An intersection object acquisition module is used to acquire the intersection objects in the 3D model of the current game engine that have an intersection relationship with the surface of the viewing frustum; A suspicious object screening module is used to obtain the eye-attracting degree of each intersecting object based on the overlap between the bounding box of each intersecting object and the visual frustum, the change of the physical engine information of each intersecting object, and the relative distance between the intersecting objects on the same surface of the visual frustum; and screen suspicious objects using the eye-attracting degree; A matching object screening module is used to obtain a consistency index corresponding to the historical intersection object according to the consistency between the spatial vector of the historical intersection object in the historical log data and the corresponding camera view vector; determine the user view following time difference according to the matching relationship between the change of the spatial vector of the historical intersection object and the change of the corresponding camera view vector and the consistency index; and screen the matching object of each suspicious object based on the difference between the relative distance between each suspicious object and the camera and the relative distance between other objects other than the suspicious object and the camera; The attention calculation module is used to obtain the visual relationship factor between the suspicious object and each of its matching objects by comprehensively analyzing the projection distribution of the suspicious object and its matching object relative to the plane aligned with the camera lens and the change of the space vector when the state of the suspicious object changes; and calculate the attention degree of each suspicious object by combining the spatial distance between the suspicious object and its matching object, the visual relationship factor and the user's perspective following time difference; The game image generation module is used to generate game images based on the LOD level model determined by the attention level.

Images