Large-scale texture model loading method based on Unreal scene

Abstract

The invention relates to the technical field of model loading, and discloses a Unreal scene-based large-scale textured model loading method, which comprises the following steps of: obtaining position coordinates of a user in a simulation scene, position coordinates of a task in the simulation scene and an abstract path in the simulation scene, and associating the position coordinates of the user and the task with the abstract path; predicting the user according to the position coordinate of the user, the position coordinate of the task and the abstract path to obtain a weighted path set; distributing first computing power to the path with the maximum weight coefficient in the weighted path set; distributing second computing power to a path with a non-maximum weight coefficient in the weighted path set; obtaining weather types in the simulation scene, and dividing the weather types into short-looking weather and long-looking weather; and according to the paths in the weighted path set selected by the user, weather is set for the corresponding paths, and computing power is redistributed.

Description

Technical Field

[0001] The present invention relates to the field of model loading, and more specifically, to a large-scale texture model loading method based on Unreal scene. Background Art

[0002] With the development of digital twin technology, modern virtual engines continue to pursue higher visual effects and more realistic immersive experiences. Especially in the open virtual world of digital twin technology, developers use a large number of high-definition textures, detailed physical simulations, and complex scene rendering. This causes users to experience problems such as screen freezes and loading delays.

[0003] In existing technologies, simulation engines usually load a large range of high-resolution textures at once to ensure that the picture remains clear at all times. This leads to performance bottlenecks such as video memory exhaustion and frame rate drop. Since open virtual worlds do not have fixed paths, a large number of high-definition resources need to be loaded in multiple directions, which makes it difficult for low-configuration hardware to withstand such pressure and causes obvious lag. The existing loading system loads all objects in the user's field of view as high-definition textures indiscriminately on the display, which increases the load on the device. Summary of the invention

[0004] The present invention provides a large-scale textured model loading method based on Unreal scene, which solves the technical problems raised in the background technology.

[0005] First, a large-scale textured model loading method based on Unreal scene, including:

[0006] Step 1, obtaining the position coordinates of the user in the simulation scene, the position coordinates of the task in the simulation scene, and the abstract path in the simulation scene, and associating the position coordinates of the user and the task with the abstract path;

[0007] Step 2: predict the user based on the user's location coordinates, the task's location coordinates, and the abstract path to obtain a weighted path set;

[0008] Step 3: Allocate the first computing power to the path with the largest weight coefficient in the weighted path set; allocate the second computing power to the path with a non-largest weight coefficient in the weighted path set;

[0009] Step 4, obtaining the weather type in the simulation scene, and classifying the weather type into short-sighted weather and long-sighted weather;

[0010] Step 5: According to the path in the weighted path set selected by the user, set the weather for the corresponding path and reallocate the computing power.

[0011] Furthermore, the user is predicted based on the user's location coordinates, the task's location coordinates, and the abstract path to obtain a weighted path set, including:

[0012] Step 6, according to the user's position on the abstract path, determine the path adjacent to the user, and use the adjacent path as the first candidate path;

[0013] Step 7, obtaining the user's moving direction, and according to the user's moving direction, eliminating the path in the first candidate path that is opposite to the user's moving direction, to obtain a second candidate path;

[0014] Step 8, for the shortest distance from the end of the second candidate path far from the user to the coordinate position of the task, determine the first path in the second candidate path, and use the remaining path as the second path;

[0015] Step 9: assign a first weight to the first path and assign a second weight to the second path to obtain a weighted path set; wherein the sum of the weights of all the paths in the weighted path set is 1.

[0016] Furthermore, the first computing power is allocated to the path with the largest weight coefficient in the weighted path set; and the second computing power is allocated to the path with a non-largest weight coefficient in the weighted path set, including:

[0017] Step 10: Determine the dispatchable computing power , for the second path, the second computing power is evenly allocated Preload.

[0018] Step 11: Allocate the first computing power to the first path Preload; where ; represents the number of second paths, ;

[0019] Step 12: Determine the total required computing power for preloading on the first path ,judge , then the total required computing power is allocated to the first path Preload and allocate computing power to the second path Preload.

[0020] Furthermore, the weather types in the simulation scene are obtained, and the weather types are divided into short-sighted weather and long-sighted weather, including:

[0021] Step 13, obtaining the weather type of the simulated scene and the visual depth under each weather condition;

[0022] Step 14, setting a depth threshold, classifying the weather with a visual depth greater than the depth threshold as long-view weather, and classifying the weather with a visual depth less than the depth threshold as short-view weather.

[0023] Furthermore, according to the path in the weighted path set selected by the user, the weather is set for the corresponding path and the computing power is reallocated, including:

[0024] Step 15, determining whether the path selected by the user is the first path or the second path;

[0025] Step 16: If the user's selected path is the first path, the second path is stopped from being preloaded, and the schedulable computing power is All are allocated to the first path;

[0026] Step 17: If the user selects the path The second path, then The second path sets short-sighted weather and dispatches computing power All allocated in A second path.

[0027] Further, preloading includes:

[0028] Step 18, start the Unreal engine's LOD2 to load the preloaded path.

[0029] Second, the method for loading a large-scale textured model based on an Unreal scene also includes:

[0030] Step 19, dividing the display image, setting the central area of ​​the display image as the central visual area of ​​the screen, and setting the remaining area of ​​the display image as the peripheral visual area of ​​the screen;

[0031] Step 20, performing a first loading on the central visual area of ​​the screen, the first loading comprising:

[0032] Start loading the Unreal engine's LOD0 for the model in the central visual area of ​​the screen;

[0033] Step 21, performing a second loading on the peripheral visual area of ​​the screen, the second loading comprising:

[0034] Enable the Unreal Engine's LOD1 to load the model in the peripheral visual area of ​​the screen.

[0035] Further, the central area of ​​the display screen is set as the central visual area of ​​the screen, and the remaining area of ​​the display screen is set as the peripheral visual area of ​​the screen, including:

[0036] Step 22, calculate the radius of the central visual area of ​​the human eye, the formula is as follows: ;in, Represents the radius of the central visual area of ​​the human eye, Indicates the distance between the user and the display screen, which is a custom constant. Indicates the central visual angle of the human eye. ;

[0037] Step 23, convert the radius of the central visual area of ​​the human eye into the radius of the central visual area of ​​the screen, the formula is as follows: ;in, Indicates the radius of the central visual area of ​​the screen, Indicates the width of the display screen;

[0038] Step 24: for any point in the display screen ,judge Belongs to the central visual area of ​​the screen. satisfy ,but belongs to the central visual area of ​​the screen, otherwise it belongs to the peripheral visual area of ​​the screen; among them, Indicates the central horizontal coordinate of the display screen. Indicates the center vertical coordinate of the display screen.

[0039] The beneficial effects of the present invention are: efficient loading of textured models is achieved through multiple measures such as path prediction optimization of computing power allocation, dynamic weather control of sight distance, and visual partition management. In an open virtual world, it intelligently allocates computing power resources, prioritizes loading the most likely path for users, and uses short-sighted weather to reduce rendering burden when predictions deviate. Visual partition loading simulates the characteristics of the human eye, ensuring a clear central image and low load in the surrounding area, thereby significantly improving loading efficiency and fluency. BRIEF DESCRIPTION OF THE DRAWINGS

[0040] Figure 1 This is the first flow chart of the method for loading a large-scale textured model based on an Unreal scene of the present invention;

[0041] Figure 2 This is the second flow chart of the method for loading a large-scale textured model based on an Unreal scene of the present invention. DETAILED DESCRIPTION

[0042] The subject matter described herein will now be discussed with reference to example implementations. It should be understood that the discussion of these implementations is only to enable those skilled in the art to better understand and implement the subject matter described herein, and the functions and arrangements of the elements discussed may be changed without departing from the scope of protection of the contents of this specification. Various examples may omit, replace, or add various processes or components as needed. In addition, the features described in some examples may also be combined in other examples.

[0043] like Figure 1 As shown, the large-scale textured model loading method based on the Unreal scene includes:

[0044] Step 1, obtaining the position coordinates of the user in the simulation scene, the position coordinates of the task in the simulation scene, and the abstract path in the simulation scene, and associating the position coordinates of the user and the task with the abstract path;

[0045] Step 2: predict the user based on the user's location coordinates, the task's location coordinates, and the abstract path to obtain a weighted path set;

[0046] Step 3: Allocate the first computing power to the path with the largest weight coefficient in the weighted path set; allocate the second computing power to the path with a non-largest weight coefficient in the weighted path set;

[0047] Step 4, obtaining the weather type in the simulation scene, and classifying the weather type into short-sighted weather and long-sighted weather;

[0048] Step 5: According to the path in the weighted path set selected by the user, set the weather for the corresponding path and reallocate the computing power.

[0049] In one embodiment of the present invention, the user is predicted according to the user's location coordinates, the task's location coordinates and the abstract path to obtain a weighted path set, including:

[0050] Step 6, according to the user's position on the abstract path, determine the path adjacent to the user, and use the adjacent path as the first candidate path;

[0051] Step 7, obtaining the user's moving direction, and according to the user's moving direction, eliminating the path in the first candidate path that is opposite to the user's moving direction, to obtain a second candidate path;

[0052] Step 8, for the shortest distance from the end of the second candidate path far from the user to the coordinate position of the task, determine the first path in the second candidate path, and use the remaining path as the second path;

[0053] Step 9: assign a first weight to the first path and assign a second weight to the second path to obtain a weighted path set; wherein the sum of the weights of all the paths in the weighted path set is 1.

[0054] Specifically, the path selection and candidate path determination first determine the user's abstract path location, then identify the paths adjacent to the user's location, and use these adjacent paths as the first candidate paths. This process provides a basis for subsequent path screening and weighting.

[0055] Direction filtering: The system analyzes the user's moving direction and removes the paths that are opposite to the user's moving direction from the first candidate path to generate the second candidate path. This step effectively eliminates the paths with lower probability and focuses on the paths that match the user's current moving direction.

[0056] Shortest path selection and path classification, in the second candidate path, the shortest path is selected as the "first path", that is, the optimal path, and the remaining paths are set as the "second path". The "first path" path will receive a higher weight to ensure that resources are concentrated on the path that users are most likely to choose, and the "second path" will receive a lower weight.

[0057] Weight allocation, assign a higher weight (first weight) to the "first path" and a lower weight (second weight) to the remaining paths. In the weighted path set generated in this way, the sum of the path weights is 1. Through reasonable weight allocation, the distribution of loading resources is optimized, making the computing power allocation more targeted and reducing unnecessary resource consumption.

[0058] In one embodiment of the present invention, a first computing power is allocated to a path with the largest weight coefficient in a weighted path set; and a second computing power is allocated to a path with a non-maximum weight coefficient in the weighted path set, including:

[0059] Step 10: Determine the dispatchable computing power , for the second path, the second computing power is evenly allocated Preload.

[0060] Step 11: Allocate the first computing power to the first path Preload; where ; represents the number of second paths, ;

[0061] Step 12: Determine the total required computing power for preloading on the first path ,judge , then the total required computing power is allocated to the first path Preload and allocate computing power to the second path Preload.

[0062] It should be noted that in the development of professional computer graphics or simulation scenes, computing power usually refers to the computing power of the processor, which is mainly measured by the number of floating-point operations per second, especially in the evaluation of GPU computing power.

[0063] Specifically, the system determines the schedulable computing power and allocates it to the second weighted path. The system first determines the computing power available for allocation, and then allocates part of the computing power to the paths with smaller weight coefficients (second weight) in the weighted path set. These paths have lower priorities, so the computing power allocated is relatively small and preloaded.

[0064] Allocate the first computing power to the first weighted path. The system allocates the main computing power to the path with the highest weight coefficient in the weighted path set (i.e., the first weighted path) to ensure that the path can obtain sufficient resources for efficient loading. The allocated computing power is preloaded according to the needs of the first weighted path to ensure smooth operation of the path that the user is most likely to choose.

[0065] The system calculates the total required computing power and allocates it to the first weighted path and makes a judgment. If the first computing power exceeds the total required computing power, the excess computing power will be evenly allocated to the second weighted path.

[0066] In one embodiment of the present invention, obtaining weather types in a simulated scene and classifying the weather types into short-sighted weather and long-sighted weather include:

[0067] Step 13, obtaining the weather type of the simulated scene and the visual depth under each weather condition;

[0068] Step 14, setting a depth threshold, classifying the weather with a visual depth greater than the depth threshold as long-view weather, and classifying the weather with a visual depth less than the depth threshold as short-view weather.

[0069] In one embodiment of the present invention, according to a path in a weighted path set selected by a user, setting weather for the corresponding path and reallocating computing power include:

[0070] Step 15, determining whether the path selected by the user is the first path or the second path;

[0071] Step 16: If the user's selected path is the first path, the second path is stopped from being preloaded, and the schedulable computing power is All are allocated to the first path;

[0072] Step 17: If the user selects the path The second path, then The second path sets short-sighted weather and dispatches computing power All allocated in A second path.

[0073] Specifically, to obtain the weather type and visual depth, the system first obtains all weather types in the simulated scene and sets the visual depth for each weather, that is, the user's viewing distance under the weather conditions. Different weather (such as sunny, foggy, etc.) will affect the user's visual range.

[0074] Set depth threshold and classify, by setting the threshold of visual depth, the weather is divided into short-sighted weather and long-sighted weather. Short-sighted weather (such as foggy days and heavy rain) will limit the viewing distance, while long-sighted weather (such as sunny days and clear nights) allows a longer viewing distance.

[0075] The purpose of this grading method is that when a user chooses a low-priority path, due to the small amount of computing power allocated previously, the textured models loaded on the path are limited. Short-sighted weather can be automatically applied to limit the user's field of view, and computing power can be concentrated outside the field of view to load textured models, thereby avoiding being discovered by the user.

[0076] In one embodiment of the present invention, preloading includes:

[0077] Step 18, start the Unreal engine's LOD2 to load the preloaded path.

[0078] Specifically, preloaded scene selection preloads scenes or resources that may not be immediately presented in the user's field of view, ensuring that they can be quickly loaded when the user approaches or turns to these areas, so as to reduce potential loading delays or screen freezes.

[0079] The choice of LOD2, LOD2 represents a lower level of detail version in the LOD system in Unreal Engine, usually containing a lower polygon count and simplified textures. Compared to LOD0 or LOD1, LOD2 is less burdensome to render, but is still enough to give users a basic look of the scene at a longer distance.

[0080] Using LOD2 for preloading helps balance system performance and loading speed, and reduces the use of video memory and computing resources.

[0081] While optimizing loading efficiency, it ensures the smoothness of the display, which is especially suitable for large-scale open world scenes. Users do not need to wait for higher-precision resources to load when approaching certain areas, thereby enhancing the overall immersive experience of the simulation scene.

[0082] like Figure 2 As shown, the large-scale textured model loading method based on the Unreal scene also includes:

[0083] Step 19, dividing the display image, setting the central area of ​​the display image as the central visual area of ​​the screen, and setting the remaining area of ​​the display image as the peripheral visual area of ​​the screen;

[0084] Step 20, performing a first loading on the central visual area of ​​the screen, the first loading comprising:

[0085] Start loading the Unreal engine's LOD0 for the model in the central visual area of ​​the screen;

[0086] Step 21, performing a second loading on the peripheral visual area of ​​the screen, the second loading comprising:

[0087] Enable the Unreal Engine's LOD1 to load the model in the peripheral visual area of ​​the screen.

[0088] Specifically, the screen partition setting divides the display screen into two areas: the central visual area of ​​the screen and the peripheral visual area of ​​the screen. The central visual area corresponds to the central field of vision of the human eye, mainly concentrated in the central area of ​​the user's line of sight; while the peripheral visual area covers the peripheral part of the user's field of vision.

[0089] This partitioning is designed based on the visual characteristics of the human eye to achieve efficient use of resources. The central visual area will load higher-resolution resources, while the peripheral visual area can use lower resolution to reduce the demand for video memory and computing resources.

[0090] Loading in the central visual area, in the central visual area, the highest resolution model is loaded first (using the Unreal engine's LOD0). LOD0 provides complete details and high-definition textures, ensuring that the objects and scenes seen by the user in the centralized field of view have the best visual effects.

[0091] This approach ensures the image quality in key areas, allowing users to clearly observe high-detail models in the center of the field of view, enhancing the immersive experience.

[0092] Loading of peripheral vision area, for the peripheral vision area, load lower resolution models (using Unreal Engine's LOD1). LOD1 provides richer polygon counts and details, but is enough to provide basic visual effects.

[0093] This setting can reduce unnecessary resource consumption and enable the engine to use simplified models in the surrounding areas to balance system performance, thereby improving the loading smoothness of the entire simulation scene.

[0094] In one embodiment of the present invention, the central area of ​​the display screen is set as the central visual area of ​​the screen, and the remaining area of ​​the display screen is set as the peripheral visual area of ​​the screen, including:

[0095] Step 22, calculate the radius of the central visual area of ​​the human eye, the formula is as follows: ;in, Represents the radius of the central visual area of ​​the human eye, Indicates the distance between the user and the display screen, which is a custom constant. Indicates the central visual angle of the human eye. ;

[0096] Step 23, convert the radius of the central visual area of ​​the human eye into the radius of the central visual area of ​​the screen, the formula is as follows: ;in, Indicates the radius of the central visual area of ​​the screen, Indicates the width of the display screen;

[0097] Step 24: for any point in the display screen ,judge Belongs to the central visual area of ​​the screen. satisfy ,but belongs to the central visual area of ​​the screen, otherwise it belongs to the peripheral visual area of ​​the screen; among them, Indicates the central horizontal coordinate of the display screen. Indicates the center vertical coordinate of the display screen.

[0098] Specifically, the radius of the central visual area is calculated. This step calculates the physical radius of the visual area according to the characteristics of the central vision of the human eye. The calculation formula involves variables such as the distance between the user and the screen, predefined constants, and the central visual angle, so as to obtain the radius of the central visual area of ​​the human eye to ensure that it meets the natural visual effect.

[0099] The calculated radius of the central visual area is converted to the pixel radius on the screen to adapt to the resolution and display ratio of different devices. This step ensures that the central area of ​​the display accurately corresponds to the central visual range of the human eye, thereby achieving the highest clarity loading in the visual focus area.

[0100] Visual area judgment: Each point in the display image is judged to determine whether it is located in the central visual area of ​​the screen. The qualified area will be divided into the central visual area, and high-definition models and high-resolution textures will be loaded; otherwise, it will be divided into the peripheral visual area and loaded with lower-resolution resources. This partitioning makes resource allocation more accurate and effectively improves loading efficiency.

[0101] The above describes an embodiment of the present embodiment, but the present embodiment is not limited to the above-mentioned specific implementation methods. The above-mentioned specific implementation methods are merely illustrative and not restrictive. Under the guidance of the present embodiment, ordinary technicians in this field can also make many forms, all of which are within the protection of the present embodiment.

Claims

1. A large-scale textured model loading method based on Unreal scene, characterized in that: include: Step 1, obtaining the position coordinates of the user in the simulation scene, the position coordinates of the task in the simulation scene, and the abstract path in the simulation scene, and associating the position coordinates of the user and the task with the abstract path; Step 2: predict the user based on the user's location coordinates, the task's location coordinates, and the abstract path to obtain a weighted path set; Step 3: Allocate the first computing power to the path with the largest weight coefficient in the weighted path set; allocate the second computing power to the path with a non-largest weight coefficient in the weighted path set; Step 4, obtaining the weather type in the simulation scene, and classifying the weather type into short-sighted weather and long-sighted weather; Step 5: According to the path in the weighted path set selected by the user, set the weather for the corresponding path and reallocate the computing power.

2. The method for loading a large-scale textured model based on an Unreal scene according to claim 1, characterized in that: The user is predicted based on the user's location coordinates, the task's location coordinates, and the abstract path to obtain a weighted path set, including: Step 6, according to the user's position on the abstract path, determine the path adjacent to the user, and use the adjacent path as the first candidate path; Step 7, obtaining the user's moving direction, and according to the user's moving direction, eliminating the path in the first candidate path that is opposite to the user's moving direction, to obtain a second candidate path; Step 8, for the shortest distance from the end of the second candidate path far from the user to the coordinate position of the task, determine the first path in the second candidate path, and use the remaining path as the second path; Step 9: assign a first weight to the first path and assign a second weight to the second path to obtain a weighted path set; wherein the sum of the weights of all the paths in the weighted path set is 1.

3. The method for loading a large-scale textured model based on an Unreal scene according to claim 2, characterized in that: The first computing power is allocated to the path with the largest weight coefficient in the weighted path set; For the path with a non-maximum weight coefficient in the weighted path set, the second computing power is allocated, including: Step 10: Determine the dispatchable computing power , for the second path, the second computing power is evenly allocated Preload. Step 11: Allocate the first computing power to the first path Preload; among them, ; represents the number of second paths, ; Step 12: Determine the total required computing power for preloading on the first path ,judge , then the total required computing power is allocated to the first path Preload and allocate computing power to the second path Preload.

4. The method for loading a large-scale textured model based on an Unreal scene according to claim 3, characterized in that: Get the weather type in the simulation scene, and divide the weather type into short-term weather and long-term weather, including: Step 13, obtaining the weather type of the simulated scene and the visual depth under each weather type; Step 14, setting a depth threshold, classifying the weather with a visual depth greater than the depth threshold as long-view weather, and classifying the weather with a visual depth less than the depth threshold as short-view weather.

5. The method for loading a large-scale textured model based on an Unreal scene according to claim 4, characterized in that: According to the path in the weighted path set selected by the user, the weather is set for the corresponding path and computing power is redistributed, including: Step 15, determining whether the path selected by the user is the first path or the second path; Step 16: If the user's selected path is the first path, the second path is stopped from being preloaded, and the schedulable computing power is All are allocated to the first path; Step 17: If the user selects the path The second path, then The second path sets short-sighted weather and dispatches computing power All allocated to A second path.

6. The method for loading a large-scale textured model based on an Unreal scene according to claim 5, characterized in that: Preload, including: Step 18, start the Unreal engine's LOD2 to load the preloaded path.

7. The method for loading a large-scale textured model based on an Unreal scene according to claim 6, characterized in that: Also includes: Step 19, dividing the display image, setting the central area of ​​the display image as the central visual area of ​​the screen, and setting the remaining area of ​​the display image as the peripheral visual area of ​​the screen; Step 20, performing a first loading on the central visual area of ​​the screen, the first loading comprising: Start loading the Unreal engine's LOD0 for the model in the central visual area of ​​the screen; Step 21, performing a second loading on the peripheral visual area of ​​the screen, the second loading comprising: Enable the Unreal Engine's LOD1 to load the model in the peripheral visual area of ​​the screen.

8. The method for loading a large-scale textured model based on an Unreal scene according to claim 7, characterized in that: The center area of ​​the display screen is set as the screen center visual area, and the remaining area of ​​the display screen is set as the screen peripheral visual area, including: Step 22, calculate the radius of the central visual area of ​​the human eye, the formula is as follows: ;in, Represents the radius of the central visual area of ​​the human eye, Indicates the distance between the user and the display screen, which is a custom constant. Indicates the central visual angle of the human eye. ; Step 23, convert the radius of the central visual area of ​​the human eye into the radius of the central visual area of ​​the screen, the formula is as follows: ;in, Indicates the radius of the central visual area of ​​the screen, Indicates the width of the display screen; Step 24: for any point in the display screen ,judge Belongs to the central visual area of ​​the screen. satisfy ,but belongs to the central visual area of ​​the screen, otherwise it belongs to the peripheral visual area of ​​the screen; among them, Indicates the central horizontal coordinate of the display screen. Indicates the center vertical coordinate of the display screen.

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