A method and device for rendering a static object network model
By mapping the mesh data and texture data of the static object network model to a flat image and rendering it, the problem of large amount of rendering calculations in virtual space-time is solved, and high-quality rendering and speed improvement are achieved.
Patent Information
- Application Number
- CN202111366349.3
- Authority / Receiving Office
- CN · China
- Patent Type
- Patents(China)
- Current Assignee / Owner
- Filing Date
- 2021-11-18
- Publication Date
- 2025-01-03
- Estimated Expiration
- 2041-11-18
AI Technical Summary
The rendering calculation of static object network models in existing virtual space-time is large, resulting in slow rendering speed and difficult to achieve high-quality rendering. Especially in virtual spaces with high real-time requirements, the rendering quality has to be reduced.
By obtaining the mesh data and texture data of the static object network model, mapping it to the plane geometric image, compressing and storing it, and establishing a general identification code and model resource list based on the pose state, extracting the sampling points in the plane image for rendering, saving Compute resources to improve rendering speed.
High-quality rendering is achieved, while saving computing resources, improving rendering speed, and avoiding the reduction in rendering quality.
Smart Images
Figure CN114119827B_ABST
Abstract
Description
Technical Field
[0001] The present application provides a rendering technology, and more particularly, relates to a rendering method for a static object network model. The present application also relates to a rendering device for a static object network model. Background Art
[0002] The development of electronic technology provides users with new experiences of work, life and entertainment based on virtual space and time. As an important part of virtual space and time, the realism of network scenes largely determines the quality of the experience. Usually, high-detail and high-precision rendering of static object network models in network space and time can bring shocking feelings to users, attract users, and improve user experience, which is inseparable from the continuous advancement of rendering technology.
[0003] Existing static object network models in virtual space-time are all rendered based on model data. For a highly realistic static object network model, immeasurable calculations will be performed in virtual space-time, which has a great impact on the speed of model rendering. In a virtual space that requires real-time performance, the model rendering quality has to be reduced to increase the speed. Summary of the invention
[0004] In order to solve the problem that the model rendering calculation is large, the time is long, and the static object network model is difficult to render with high quality, the present application provides a rendering method of the static object network model. The present application also relates to a rendering device of the static object network model.
[0005] The present application provides a method for rendering a static object network model, comprising:
[0006] Get the mesh data and texture data of the static object network model;
[0007] Mapping the mesh data parameters to the plane area of the texture data to form a plane geometric image including the mesh data parameters, and compressing the plane geometric image and storing it in a network storage;
[0008] Establishing a universal identification code for the plane geometric image, and establishing a model resource list according to the posture state of the static object network model and the universal identification code;
[0009] Model data is loaded from the network storage according to the model resource list, multiple sampling points in the plane geometric image are extracted according to a preset viewing distance, and the static object network model is rendered according to the mapping relationship between the grid data parameters of the sampling points and the texture data.
[0010] Optionally, the network storage is connected to:
[0011] The backup storage detects and extracts or sends the difference data according to the difference between the data in the backup storage and the network storage.
[0012] Optionally, rendering the static object network model includes:
[0013] Loads a static object mesh model at a compressed resolution, and reloads the static object mesh model at full resolution after loading is complete.
[0014] Optionally, the posture state includes: rotation, scaling or displacement of the static object network model.
[0015] Optionally, the network storage is a distributed architecture.
[0016] The present application also provides a rendering device for a static object network model, comprising:
[0017] An acquisition module, used to acquire mesh data and texture data of a static object network model;
[0018] A storage module, used for mapping the mesh data parameters to the plane area of the texture data to form a plane geometric image including the mesh data parameters, and compressing the plane geometric image and storing it in the network storage;
[0019] A preloading module, used to establish a universal identification code for the plane geometric image, and to establish a model resource list according to the posture state of the static object network model and the universal identification code;
[0020] A rendering module is used to load model data from the network storage according to the model resource list, extract multiple sampling points in the plane geometric image according to a preset viewing distance, and render the static object network model based on the mapping relationship between the grid data parameters of the sampling points and the texture data.
[0021] Optionally, also include:
[0022] The backup storage is used to detect and extract or send differential data according to the difference between the data in the backup storage and the network storage.
[0023] Optionally, the rendering module further includes:
[0024] The loading unit is used to load the static object network model with compressed resolution and reload the static object network model with full resolution after loading is completed.
[0025] Optionally, the posture state includes: rotation, scaling or displacement of the static object network model.
[0026] Optionally, the network storage is a distributed architecture.
[0027] The advantages of the technical solution of this application over the prior art are:
[0028] The present application provides a rendering method for a static object network model, including: obtaining mesh data and texture data of the static object network model; mapping mesh data parameters to a plane area of texture data to form a plane geometric image including the mesh data parameters, compressing the plane geometric image and storing it in a network memory; establishing a universal identification code for the plane geometric image, and establishing a model resource list according to the posture state of the static object network model and the universal identification code; loading model data from the network memory according to the model resource list, extracting multiple sampling points in the plane geometric image according to a preset viewing distance, and rendering the static object network model with the mapping relationship between the mesh data parameters of the sampling points and the texture data. Replacing static object network model sampling with plane image sampling for rendering saves a lot of computing resources, improves computing speed and ensures that rendering quality will not be reduced. BRIEF DESCRIPTION OF THE DRAWINGS
[0029] Figure 1 This is a rendering flow chart of the static object network model in this application.
[0030] Figure 2 This is the sampling and rendering flow chart of the static object network model in this application.
[0031] Figure 3 It is a schematic diagram of a rendering device for a static object network model in this application. DETAILED DESCRIPTION
[0032] Many specific details are described in the following description to facilitate a full understanding of the present application. However, the present application can be implemented in many other ways than those described herein, and those skilled in the art can make similar generalizations without violating the connotation of the present application, so the present application is not limited by the specific implementation disclosed below.
[0033] The present application provides a rendering method for a static object network model, including: obtaining mesh data and texture data of the static object network model; mapping mesh data parameters to a plane area of texture data to form a plane geometric image including the mesh data parameters, compressing the plane geometric image and storing it in a network memory; establishing a universal identification code for the plane geometric image, and establishing a model resource list according to the posture state of the static object network model and the universal identification code; loading model data from the network memory according to the model resource list, extracting multiple sampling points in the plane geometric image according to a preset viewing distance, and rendering the static object network model with the mapping relationship between the mesh data parameters of the sampling points and the texture data. Replacing static object network model sampling with plane image sampling for rendering saves a lot of computing resources, improves computing speed and ensures that rendering quality will not be reduced.
[0034] Figure 1 This is a rendering flow chart of the static object network model in this application.
[0035] Please refer to Figure 1 As shown, S101 obtains mesh data and texture data of a static object network model.
[0036] For a static object network model, it can be divided into two parts, the first part is mesh data, and the other part is texture data.
[0037] The mesh data refers to the unit faces that form the static object network model. The unit faces are the smallest indivisible faces that constitute the static network model. These unit faces can be triangles or polygons.
[0038] The static object network model composed of the unit faces can be rendered after adding texture data, and finally a rendered static object network model can be formed. The rendered static object network model is various objects visible to the user in virtual space and time.
[0039] When a static object network model to be rendered is obtained, the present application first divides the static object network model into two groups of data, namely mesh data and texture data, and obtains the mesh data and texture data.
[0040] Please refer to figure 1, S102 maps the mesh data parameters to the plane area of the texture data to form a plane geometric image including the mesh data parameters, and compresses the plane geometric image and stores it in the network storage.
[0041] In this application, the texture data is the modified data of the grid data, and these texture data and grid data are one-to-one corresponding, that is, the texture data of each grid and the grid have a mapping relationship. The texture data can be regarded as an image, which describes the color, material, optical properties and other parameters of the unit surface on the grid data corresponding to each pixel.
[0042] The mesh data parameters of the mesh data are mapped to the plane area of the texture data, that is, the parameters of each unit face on the network model of the static object are mapped to the image of the texture data, and the mesh data parameters include: the pose data of each unit face, that is, the coordinate and direction data. After the above processing, a plane geometric image with mesh data parameters is formed. Then, the plane geometric image is compressed and stored in the network storage.
[0043] The network storage described in this application is a storage system with a distributed architecture, such as NAS, which mainly includes: storage devices and embedded system software. Specifically, the network storage occupies its own node on a local area network, allowing users to access data on the network. By centrally managing and processing all data on the network, the load is unloaded from the application or enterprise server.
[0044] The network storage is directly connected to the disk storage array through the network, and the magnetic array has the characteristics of high capacity, high performance, high reliability, etc. The storage device can be connected to the Internet directly without a server through a standard network topology structure, and does not rely on a general operating system. Instead, it adopts a simplified operating system designed for users and specially used for data storage, and has built-in protocols required for network connection.
[0045] The network storage of the present application is also connected to a backup storage, and detects and extracts or sends the difference data according to the difference between the data in the backup storage and the network storage. Specifically, the backup storage can be set as a part of multiple network storages, or a backup storage can be set separately and connected to the network node.
[0046] Please refer to Figure 1 As shown, S103 establishes a universal identification code for the plane geometric image, and establishes a model resource list according to the posture state of the static object network model and the universal identification code.
[0047] The universal identification code shown in this application is calculated and generated based on the current time, counter and hardware identification, such as the MAC address of the wireless network card and other data, and is an identification code that uniquely represents the plane geometric image.
[0048] After the universal identification code is established, a model resource list is established based on the universal identification code and the posture state of the static object network model. The network posture state of the static object includes: the rotation, scaling or displacement parameters of the static object network model. Specifically, the posture state represents the position and direction of the static object network model in virtual space and time, including: the rotation, scaling or displacement of the static object network model.
[0049] The model resource list in the present application is a list of static object network models, including all or part of the static object network models in the virtual space-time.
[0050] Please refer to Figure 1 As shown, S104 loads model data from the network storage according to the model resource list, extracts multiple sampling points in the plane geometric image according to a preset viewing distance, and renders the static object network model based on the mapping relationship between the mesh data parameters of the sampling points and the texture data.
[0051] The model data in this application refers to a plane geometric image, and the model resource list lists the various static object network models contained in the virtual space-time. According to the model resource list, the plane geometric image is loaded from the network storage to render the static object network model.
[0052] Figure 2 This is the sampling and rendering flow chart of the static object network model in this application.
[0053] Please refer to Figure 2 As shown, S201, firstly obtain grid data parameters from the plane geometric image.
[0054] S202, according to the posture of the static object network model in the virtual space-time, the posture is the position and posture of the static object network model, and then determine the number and sampling points of the static network model.
[0055] Specifically, multiple distance thresholds may be set, and a sampling point quantity value may be set for an interval consisting of two consecutive thresholds, and then the number of sampling points may be determined by the threshold interval in which the distance threshold is located. The length of the interval is fixed and pre-set, and is represented by the letter Q.
[0056] The formula for determining the number of sampling points according to the distance threshold is as follows:
[0057]
[0058] Wherein, N is the number of sampling points, a is the half angle value of the viewing angle, is the relationship coefficient between the number of sampling points and the viewing distance, and the relationship coefficient changes dynamically according to the distance. The i represents the interval where the static object is located.
[0059] S203, after selecting the sampling point, determine the mesh data parameters and texture data at the sampling point according to the mapping relationship between the sampling point and the mesh data parameters, and render the static object network model in combination with the mesh data parameters and texture data of the sampling point.
[0060] In the present application, the rendering of the static object network model also includes: loading a static object network model with a compressed resolution, and reloading the static object network model with a full resolution after loading is complete. The compressed resolution and full resolution refer to whether the sampling points are fully collected according to the preset number of sampling points. Specifically, when the static object network model is loaded for the first time, a static object network model with a lower resolution is first selected for rendering. When the static object network model appears in the virtual space and time, a high-definition static object network model is loaded.
[0061] The present application also provides a rendering device for a static object network model, including: an acquisition model 301, a storage module 302, a preloading module 303 and a rendering module 304.
[0062] Figure 3 It is a schematic diagram of a rendering device for a static object network model in this application.
[0063] Please refer to Figure 3 As shown, the acquisition module 301 is used to acquire the mesh data and texture data of the static object network model.
[0064] For a static object network model, it can be divided into two parts, the first part is mesh data, and the other part is texture data.
[0065] The mesh data refers to the unit faces that form the static object network model. The unit faces are the smallest indivisible faces that constitute the static network model. These unit faces can be triangles or polygons.
[0066] The static object network model composed of the unit faces can be rendered after adding texture data, and finally a rendered static object network model can be formed. The rendered static object network model is various objects visible to the user in virtual space and time.
[0067] When a static object network model to be rendered is obtained, the present application first divides the static object network model into two groups of data, namely mesh data and texture data, and obtains the mesh data and texture data.
[0068] Please refer to body 3, the storage module 302 is used to map the mesh data parameters to the plane area of the texture data to form a plane geometric image including the mesh data parameters, and compress the plane geometric image and store it in the network storage.
[0069] In this application, the texture data is the modified data of the grid data, and these texture data and grid data are one-to-one corresponding, that is, the texture data of each grid and the grid have a mapping relationship. The texture data can be regarded as an image, which describes the color, material, optical properties and other parameters of the unit surface on the grid data corresponding to each pixel.
[0070] The mesh data parameters of the mesh data are mapped to the plane area of the texture data, that is, the parameters of each unit face on the network model of the static object are mapped to the image of the texture data, and the mesh data parameters include: the pose data of each unit face, that is, the coordinate and direction data. After the above processing, a plane geometric image with mesh data parameters is formed. Then, the plane geometric image is compressed and stored in the network storage.
[0071] The network storage described in this application is a storage system with a distributed architecture, such as NAS, which mainly includes: storage devices and embedded system software. Specifically, the network storage occupies its own node on a local area network, allowing users to access data on the network. By centrally managing and processing all data on the network, the load is unloaded from the application or enterprise server.
[0072] The network storage is directly connected to the disk storage array through the network, and the magnetic array has the characteristics of high capacity, high performance, high reliability, etc. The storage device can be connected to the Internet directly without a server through a standard network topology structure, and does not rely on a general operating system. Instead, it adopts a simplified operating system designed for users and specially used for data storage, and has built-in protocols required for network connection.
[0073] The network storage of the present application is also connected to a backup storage, and detects and extracts or sends the difference data according to the difference between the data in the backup storage and the network storage. Specifically, the backup storage can be set as a part of multiple network storages, or a backup storage can be set separately and connected to the network node.
[0074] Please refer to Figure 3 As shown, the preloading module 303 is used to establish a universal identification code for the plane geometric image, and to establish a model resource list according to the posture state of the static object network model and the universal identification code.
[0075] The universal identification code shown in this application is calculated and generated based on the current time, counter and hardware identification, such as the MAC address of the wireless network card and other data, and is an identification code that uniquely represents the plane geometric image.
[0076] After the universal identification code is established, a model resource list is established based on the universal identification code and the posture state of the static object network model. The network posture state of the static object includes: the rotation, scaling or displacement parameters of the static object network model. Specifically, the posture state represents the position and direction of the static object network model in virtual space and time, including: the rotation, scaling or displacement of the static object network model.
[0077] The model resource list in the present application is a list of static object network models, including all or part of the static object network models in the virtual space-time.
[0078] Please refer to Figure 3 As shown, the rendering module 304 is used to load model data from the network storage according to the model resource list, extract multiple sampling points in the plane geometric image according to a preset viewing distance, and render the static object network model based on the mapping relationship between the mesh data parameters of the sampling points and the texture data.
[0079] The model data in this application refers to a plane geometric image, and the model resource list lists the various static object network models contained in the virtual space-time. According to the model resource list, the plane geometric image is loaded from the network storage to render the static object network model.
[0080] Figure 2 This is the sampling and rendering flow chart of the static object network model in this application.
[0081] Please refer to Figure 2 As shown, S201, firstly obtain grid data parameters from the plane geometric image.
[0082] S202, according to the posture of the static object network model in the virtual space-time, the posture is the position and posture of the static object network model, and then determine the number and sampling points of the static network model.
[0083] Specifically, multiple distance thresholds may be set, and a sampling point quantity value may be set for two consecutive threshold intervals, and then the sampling point quantity is determined by the threshold interval in which the distance is located. The length of the interval is fixed and pre-set, and is represented by the letter Q.
[0084] The formula for determining the number of sampling points according to the distance threshold is as follows:
[0085]
[0086] Wherein, N is the number of sampling points, a is the half angle value of the viewing angle, is the relationship coefficient between the number of sampling points and the viewing distance, and the relationship coefficient changes dynamically according to the distance. The i represents the interval where the static object is located.
[0087] S203, after selecting the sampling point, determine the mesh data parameters and texture data at the sampling point according to the mapping relationship between the sampling point and the mesh data parameters, and render the static object network model in combination with the mesh data parameters and texture data of the sampling point.
[0088] In the present application, the rendering of the static object network model also includes: loading a static object network model with a compressed resolution, and reloading the static object network model with a full resolution after loading is complete. The compressed resolution and full resolution refer to whether the sampling points are fully collected according to the preset number of sampling points. Specifically, when the static object network model is loaded for the first time, a static object network model with a lower resolution is first selected for rendering. When the static object network model appears in the virtual space and time, a high-definition static object network model is loaded.
Claims
1. A method for rendering a static object network model, characterized in that: include: Get the mesh data and texture data of the static object network model; Mapping the mesh data parameters to the plane area of the texture data to form a plane geometric image including the mesh data parameters, and compressing the plane geometric image and storing it in a network storage; Establishing a universal identification code for the plane geometric image, and establishing a model resource list according to the posture state of the static object network model and the universal identification code; The model data of the plane geometric image is loaded from the network storage according to the model resource list, a plurality of sampling points in the plane geometric image are extracted according to a preset viewing distance, and the static object network model is rendered according to the mapping relationship between the mesh data parameters of the sampling points and the texture data; specifically, by setting a plurality of distance thresholds, and setting a sampling point quantity value for an interval consisting of two consecutive thresholds, and then determining the sampling point quantity according to the threshold interval where the distance threshold is located, the length of the interval is fixed and preset, and is represented by the letter Q; The formula for determining the number of sampling points according to the distance threshold is as follows: Wherein, N is the number of sampling points, a is the half angle value of the viewing angle, and M is i is the relationship coefficient between the number of sampling points and the viewing distance, and the relationship coefficient changes dynamically according to the distance. The i represents the interval where the static object is located.
2. The method for rendering a static object network model according to claim 1, characterized in that: The network storage is connected with: The backup storage detects and extracts or sends the difference data according to the difference between the data in the backup storage and the network storage.
3. The rendering method of the static object network model according to claim 1, characterized in that: The rendering of the static object network model includes: Loads a static object mesh model at a compressed resolution, and reloads the static object mesh model at full resolution after loading is complete.
4. The rendering method of the static object network model according to claim 1, characterized in that: The posture state includes: rotation, scaling or displacement of the static object network model.
5. The method for rendering a static object network model according to claim 1, characterized in that: The network storage is a distributed architecture.
6. A rendering device for a static object network model, characterized in that: include: An acquisition module, used to acquire mesh data and texture data of a static object network model; A storage module, used for mapping the mesh data parameters to the plane area of the texture data to form a plane geometric image including the mesh data parameters, and compressing the plane geometric image and storing it in the network storage; A preloading module, used to establish a universal identification code for the plane geometric image, and to establish a model resource list according to the posture state of the static object network model and the universal identification code; A rendering module, used for loading the model data of the plane geometric image from the network storage according to the model resource list, extracting a plurality of sampling points in the plane geometric image according to a preset viewing distance, and rendering the static object network model according to the mapping relationship between the mesh data parameters of the sampling points and the texture data; specifically, by setting a plurality of distance thresholds, and setting a sampling point quantity value for an interval consisting of two consecutive thresholds, and then determining the sampling point quantity by the threshold interval where the distance threshold is located, the length of the interval is fixed and preset, and is represented by the letter Q; The formula for determining the number of sampling points according to the distance threshold is as follows: Wherein, N is the number of sampling points, a is the half angle value of the viewing angle, and M is i is the relationship coefficient between the number of sampling points and the viewing distance, and the relationship coefficient changes dynamically according to the distance. The i represents the interval where the static object is located.
7. The rendering device of the static object network model according to claim 6, characterized in that: Also includes: The backup storage is used to detect and extract or send differential data according to the difference between the data in the backup storage and the network storage.
8. The rendering device for a static object network model according to claim 6, characterized in that: The rendering module also includes: The loading unit is used to load the static object network model with compressed resolution and reload the static object network model with full resolution after loading is completed.
9. The rendering device for a static object network model according to claim 6, characterized in that: The posture state includes: rotation, scaling or displacement of the static object network model.
10. The rendering device of the static object network model according to claim 6, characterized in that: The network storage is a distributed architecture.