CN114596195A - Topographic data processing method, system, device and computer storage medium - Google Patents
Topographic data processing method, system, device and computer storage medium Download PDFInfo
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Abstract
The application discloses a topographic data processing method, a system and a device and a computer storage medium, which are used for selecting a better algorithm according to the performance of processing equipment, and effectively improving the processing efficiency of topographic data. The method comprises the following steps: reading CPU hardware parameters and GPU hardware parameters; determining the CPU performance according to the CPU hardware parameters, and determining the GPU performance according to the GPU hardware parameters; comparing the CPU performance with the GPU performance to obtain a comparison result; and determining a target algorithm according to the comparison result, and processing the terrain data through the target algorithm.
Description
Technical Field
The present application relates to the field of data processing, and in particular, to a method, system, apparatus, and computer storage medium for processing topographic data.
Background
The terrain in the 3D engine is composed of a height map (height map), which is typically a two-value map in which the grey value of each pixel represents the height value of each point in the terrain. According to the height information provided by the height map and the position of each height pixel in the height map, the world coordinates of the terrain vertexes in the 3D space are calculated, then according to the topological relation of each terrain vertex, which three vertexes each terrain triangular surface needs to be composed of is calculated, and finally the 3D terrain is spliced and rendered in a triangular surface drawing mode.
In the process of rendering the 3D terrain, in order to save computer resources, a level of Detail (LOD) model is provided, and the LOD model refers to a group of models obtained by using methods with different details for objects in the same scene or scenes and used for drawing. The LOD model is established, so that the data volume and the complexity can be effectively reduced, and the real-time processing of the three-dimensional scene is realized. The terrain mesh simplification algorithm based on the LOD is divided into a dynamic LOD algorithm and a static LOD algorithm, the ROAM algorithm is a typical dynamic LOD algorithm, and the GeoMipMap algorithm is a static LOD algorithm.
In the prior art, when processing terrain data, a dynamic LOD algorithm based on a Central Processing Unit (CPU) or a static LOD algorithm based on a Graphics Processing Unit (GPU) is generally used only by one of the two algorithms, but the two algorithms have advantages and disadvantages, the dynamic LOD algorithm occupies more CPU resources, and the static LOD algorithm saves the CPU resources and is easy to generate terrain cracks. In the prior art, a better algorithm cannot be selected according to actual conditions, and the processing efficiency of terrain data is seriously influenced.
Disclosure of Invention
The application provides a topographic data processing method, a system and a device and a computer storage medium, which are used for selecting a better algorithm according to the performance of processing equipment and effectively improving the processing efficiency of topographic data.
The application provides a topographic data processing method in a first aspect, which comprises the following steps:
reading CPU hardware parameters and GPU hardware parameters;
determining the CPU performance according to the CPU hardware parameters, and determining the GPU performance according to the GPU hardware parameters;
comparing the CPU performance with the GPU performance to obtain a comparison result;
and determining a target algorithm according to the comparison result, and processing the terrain data through the target algorithm.
Optionally, the determining a target algorithm according to the comparison result, and the processing the terrain data by the target algorithm includes:
and when the CPU performance is determined to be higher than the GPU performance, processing the terrain data through an ROAM algorithm.
Optionally, the determining a target algorithm according to the comparison result, and the processing the terrain data by the target algorithm includes:
and when the GPU performance is determined to be stronger than the CPU performance, processing the terrain data through a GeoMipMap algorithm.
Optionally, the determining the CPU performance according to the CPU hardware parameter includes:
determining a CPU calculation performance peak value according to the master frequency, the front-end bus frequency and the cache in the CPU hardware parameters;
the determining GPU performance according to the GPU hardware parameters comprises:
determining a GPU computing performance peak value according to the number of CUDA cores, the size of a memory and the bandwidth of the memory in the GPU hardware parameters;
the comparing the CPU performance and the GPU performance to obtain a comparison result comprises:
and comparing the CPU calculation performance peak value with the GPU calculation performance peak value to obtain a comparison result.
Optionally, the processing the terrain data by using a quad space partitioning tree processing algorithm, the determining, according to the CPU hardware parameter, the CPU performance of the processing device, and determining, according to the GPU hardware parameter, the GPU performance of the processing device include:
performing four-fork spatial partition tree processing algorithm operation capability evaluation on the CPU according to the CPU hardware parameters to obtain a first evaluation value;
performing quad space division tree processing algorithm operation capability evaluation on the GPU according to the GPU hardware parameters to obtain a second evaluation value;
the comparing the CPU performance and the GPU performance to obtain a comparison result comprises:
and comparing the first evaluation value with the second evaluation value to obtain a comparison result.
Optionally, the terrain data is processed and generated by a quad space split tree processing algorithm, and when it is determined that the GPU performance is stronger than the CPU performance, processing the terrain data by a GeoMipMap algorithm includes:
when the GPU performance is determined to be stronger than the CPU performance, judging whether the side length of the terrain data to be processed meets (2^ n +1) × (2^ n + 1);
and if the side length of the terrain data to be processed meets (2^ n +1) × (2^ n +1), processing the terrain data through an ROAM algorithm.
A second aspect of the present application provides a topographic data processing system comprising:
the reading unit is used for reading the CPU hardware parameters and the GPU hardware parameters;
the determining unit is used for determining the CPU performance according to the CPU hardware parameters and determining the GPU performance according to the GPU hardware parameters;
the comparison unit is used for comparing the CPU performance with the GPU performance to obtain a comparison result;
and the processing unit is used for determining a target algorithm according to the comparison result and processing the terrain data through the target algorithm.
Optionally, the processing unit includes:
and the first processing module is used for processing the terrain data through an ROAM algorithm when the CPU performance is determined to be stronger than the GPU performance.
Optionally, the processing unit includes:
and the second processing module is used for processing the terrain data through a GeoMipMap algorithm when the GPU performance is determined to be stronger than the CPU performance.
Optionally, the determining unit is specifically configured to:
determining a CPU calculation performance peak value according to the master frequency, the front-end bus frequency and the cache in the CPU hardware parameters;
determining a GPU computing performance peak value according to the number of CUDA cores, the size of a memory and the bandwidth of the memory in the GPU hardware parameters;
the comparison unit is specifically configured to:
and comparing the CPU calculation performance peak value with the GPU calculation performance peak value to obtain a comparison result.
Optionally, the terrain data is processed and generated by a quad space partition tree processing algorithm, and the determining unit is specifically configured to:
performing four-fork spatial partition tree processing algorithm operation capability evaluation on the CPU according to the CPU hardware parameters to obtain a first evaluation value;
performing operation capability evaluation on the GPU by using a quad space division tree processing algorithm according to the GPU hardware parameters to obtain a second evaluation value;
the comparison unit is specifically configured to:
and comparing the first evaluation value with the second evaluation value to obtain a comparison result.
Optionally, the terrain data is processed and generated by a quad space partition tree processing algorithm, and the second processing module is further specifically configured to:
when the CPU performance is determined to be stronger than the GPU performance, judging whether the side length of the terrain data to be processed meets (2^ n +1) × (2^ n + 1);
and if the side length of the terrain data to be processed meets (2^ n +1) × (2^ n +1), processing the terrain data through an ROAM algorithm.
A third aspect of the present application provides a topographic data processing apparatus comprising:
the device comprises a processor, a memory, an input and output unit and a bus;
the processor is connected with the memory, the input and output unit and the bus;
the memory stores a program that the processor calls to execute the first aspect and the method of processing topographic data selectable from any one of the first aspect.
A fourth aspect of the present application provides a computer-readable storage medium having a program stored thereon, where the program is executed on a computer to perform the method for processing topographic data as set forth in any one of the first aspect and the second aspect.
According to the technical scheme, the method has the following advantages:
the method comprises the steps of obtaining hardware information of a CPU and a GPU of a processing device in advance, evaluating whether the processing device has relatively strong CPU performance or relatively strong GPU performance according to the hardware information, and determining an algorithm adopted for processing topographic data by combining a comparison result. By the method, a better algorithm can be selected according to the CPU performance and the GPU performance of the processing equipment, and the processing efficiency of the terrain data is effectively improved.
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In order to more clearly illustrate the technical solutions in the present application, the drawings needed to be used in the description of the embodiments will be briefly introduced below, and it is obvious that the drawings in the following description are only some embodiments of the present application, and it is obvious for those skilled in the art to obtain other drawings without creative efforts.
Fig. 1 is a schematic flowchart of an embodiment of a terrain data processing method provided in the present application;
fig. 2 is a schematic flowchart of another embodiment of a terrain data processing method provided in the present application;
FIG. 3 is a schematic structural diagram of an embodiment of a terrain data processing system provided by the present application;
FIG. 4 is a schematic structural diagram of another embodiment of a terrain data processing system provided by the present application;
fig. 5 is a schematic structural diagram of an embodiment of a terrain data processing apparatus provided in the present application.
Detailed Description
The application provides a topographic data processing method, a system and a device and a computer storage medium, which are used for selecting a better algorithm according to the performance of processing equipment and improving the processing efficiency of topographic data.
The terrain data processing method provided by the present application may be applied to some processing devices having both a CPU and a GPU, for example, the processing device may be a computer, a projector, a game host, or the like. For convenience of explanation, the processing device is taken as an execution subject in the present application for illustration.
Referring to fig. 1, fig. 1 is a diagram illustrating an embodiment of a method for processing topographic data, the method including:
101. reading CPU hardware parameters and GPU hardware parameters;
in the process of rendering a virtual scene, a level of Detail (LOD) model is generally used to process topographic data, where the LOD model refers to a set of models obtained by using methods with different details for objects in the same scene or in a scene, and the models are used for rendering. The LOD model is established, so that the data volume and the complexity can be effectively reduced, and the real-time processing of the three-dimensional scene is realized. The basic principle of an LOD model is to render the terrain at close up as detailed as possible, while rendering the terrain at far away as simple as possible. In general, besides the "depth-depth" function, the LOD model generally has a "cone visibility culling" function, that is, only whether the terrain within the visual field is rendered is considered, and the terrain outside the visual field is not rendered.
For the drawing of the LOD model, there are two main types of drawing algorithms: the dynamic LOD algorithm based on CPU operation and the static LOD algorithm based on GPU operation. Since the early GPU technology lags behind the CPU technology, developers can select a dynamic LOD algorithm based on CPU operation to process terrain data, and with the development of the GPU technology, the GPU operation capability carried by more and more processing devices is greatly enhanced, and at this time, selecting a dynamic LOD algorithm based on CPU operation to process terrain data cannot fully utilize the computation capability of the GPU, and when processing massive terrain data, the CPU load is too large, which affects the efficiency of terrain data processing.
In the invention, before processing the terrain data, the processing equipment needs to read the hardware parameters of the CPU and the hardware parameters of the GPU, and select a proper LOD algorithm according to the hardware parameters of the CPU and the GPU. Specifically, the processing device may obtain the hardware parameters of the CPU/GPU by identifying the model of the CPU/GPU.
102. Determining the CPU performance according to the CPU hardware parameters, and determining the GPU performance according to the GPU hardware parameters;
the processing equipment respectively reads the hardware parameters of the CPU and the GPU, and respectively evaluates the performance of the CPU and the performance of the GPU according to the hardware parameters of the CPU and the GPU, wherein the performance of the CPU and the performance of the GPU are mainly reflected in the data processing speed of the CPU and the GPU.
103. Comparing the CPU performance with the GPU performance to obtain a comparison result;
and the processing equipment compares the CPU performance and the GPU performance obtained by evaluation and obtains a comparison result. Because the CPU performance of a certain processing device is stronger than the GPU performance and the GPU performance of another processing device is stronger than the CPU performance in different types and different types of processing devices, it is necessary to compare the performance of the CPU and the GPU mounted on different processing devices, so that a suitable algorithm can be selected according to the performance comparison result.
104. And determining a target algorithm according to the comparison result, and processing the terrain data through the target algorithm.
And the processing equipment selects a target algorithm according to the performance comparison result of the CPU and the GPU, the target algorithm is a dynamic LOD algorithm based on CPU operation or a static LOD algorithm based on GPU operation, and the processing equipment processes the terrain data according to the selected target algorithm.
In the invention, the processing equipment acquires the hardware information of the CPU and the GPU of the processing equipment in advance, evaluates whether the processing equipment has relatively stronger CPU performance or relatively stronger GPU performance according to the hardware information, and determines the algorithm adopted for processing the topographic data by combining the comparison result. By the method, a better algorithm can be selected according to the CPU performance and the GPU performance of the processing equipment, and the processing efficiency of the terrain data is effectively improved.
Referring to fig. 2, fig. 2 is a diagram illustrating another embodiment of a method for processing topographic data, where the method includes:
201. reading CPU hardware parameters and GPU hardware parameters;
202. determining the CPU performance according to the CPU hardware parameters, and determining the GPU performance according to the GPU hardware parameters;
in this embodiment, steps 201 to 202 are similar to steps 101 to 102 of the previous embodiment, and are not described again here.
203. Comparing the CPU performance with the GPU performance to obtain a comparison result;
and the processing equipment compares the CPU performance and the GPU performance obtained by evaluation and obtains a comparison result.
In particular, in one possible implementation, comparing CPU performance and GPU performance may be by comparing the computational performance peaks of the CPU and GPU. The CPU calculation performance peak value is the calculation capacity of the CPU under the optimal condition, and mainly depends on the main frequency of the CPU, the front-end bus frequency, the cache and other hardware parameters. The GPU computing performance peak value mainly depends on hardware parameters such as the number of CUDA cores of the GPU, the memory size, and the memory bandwidth.
In NPL operating environment, the terrain data is processed by adopting a four-fork spatial partition tree processing algorithm, namely, the terrain in a visual range is firstly divided into 4 equal parts of rectangular sub-blocks, 4 sub-blocks are detected by judging shadows through calculation, and if the grid precision of a certain sub-block is detected to meet the drawing requirement, the sub-block is not divided downwards; otherwise, the sub-blocks are continuously divided into smaller sub-blocks until the rectangular grids of all the sub-blocks reach the rendering precision. Therefore, in another possible implementation manner, the processing device may respectively evaluate the computing capabilities of the CPU and the GPU based on the quad space partition tree processing algorithm, determine an evaluation value according to a preset evaluation criterion, and compare the CPU performance and the GPU performance by comparing the evaluation value of the CPU and the evaluation value of the GPU.
204. When the CPU performance is determined to be stronger than the GPU performance, processing the terrain data through an ROAM algorithm;
the ROAM algorithm is a representative of a dynamic LOD algorithm based on a CPU, and the main idea of the algorithm is that when terrain data are processed, triangle splitting and combining are carried out on a triangular plate surface of the terrain surface through a complex data structure and a large amount of CPU operation according to multiple factors such as the positions and directions of viewpoints and sight lines, and finally a simplified surface similar to an original surface is formed.
In the NPL operating environment, a quad-tree-based ROAM algorithm and a Geomipmap algorithm are provided, and when the processing equipment determines that the CPU performance is stronger than the GPU performance, the terrain data can be processed through the ROAM algorithm. Processing terrain data using the ROAM algorithm can reduce the number of triangles that are split when the terrain data is processed, thereby increasing the speed of terrain data processing.
205. And when the GPU performance is determined to be stronger than the CPU performance, processing the terrain data through a GeoMipMap algorithm.
The GeoMipMap algorithm is a static LOD algorithm based on a GPU, the algorithm divides a terrain into a plurality of small blocks through the GPU, each terrain has different detail levels through preprocessing, and terrain blocks needing to be rendered and the detail levels of each terrain block are selected according to the positions of viewpoints when the terrain is displayed.
When the processing equipment determines that the performance of the GPU is stronger than that of the CPU, the topographic data are processed through the GeoMipMap algorithm, and a large amount of CPU calculation can be reduced by processing the topographic data through the GeoMipMap algorithm, so that CPU calculation resources are saved, the processing capacity of the GPU is fully utilized, and massive topographic data can be better processed.
It should be noted that, because the GeoMipMap algorithm actually describes the terrain data by using different resolution grid models, the terrain is processed in blocks on the xz plane of the coordinate system, each block has its own level of detail, and the side length can satisfy 2^ n + 1. Therefore, in the NPL operating environment, if the GeoMipMap algorithm is used to simulate the terrain, the side length of the terrain data is required to satisfy (2^ n +1) × (2^ n + 1). Therefore, in the NPL operating environment, when the GPU performance is determined to be stronger than the CPU performance, whether the side length of the terrain data to be processed meets (2^ n +1) × (2^ n +1) needs to be further judged, and if so, the terrain data can be processed by adopting a GeoMipMap algorithm; if not, then ROAM or other possible algorithms may be required for processing.
In this embodiment, the processing device obtains hardware information of the CPU and the GPU of the processing device in advance, and then evaluates whether the processing device has relatively strong CPU performance or relatively strong GPU performance according to the hardware information, where the performance may be a peak value of the computational performance of the CPU and the GPU, or an evaluation value of the computational capability of the CPU and the GPU based on a quad space partition tree processing algorithm. And when the processing equipment determines that the CPU performance is stronger, processing the terrain data by adopting an ROAM algorithm, and when the GPU performance is stronger, processing the terrain data by adopting a GeoMipMap algorithm. By the method, a better algorithm can be selected according to the CPU performance and the GPU performance of the processing equipment, and the processing efficiency of the terrain data is effectively improved.
Referring to fig. 3, fig. 3 is a diagram illustrating an embodiment of a terrain data processing system according to the present application, the system comprising:
a reading unit 301, configured to read CPU hardware parameters and GPU hardware parameters;
a determining unit 302, configured to determine CPU performance according to the CPU hardware parameters, and determine GPU performance according to the GPU hardware parameters;
a comparing unit 303, configured to compare the CPU performance and the GPU performance to obtain a comparison result;
and the processing unit 304 is configured to determine a target algorithm according to the comparison result, and process the terrain data through the target algorithm.
In this embodiment, the reading unit 301 obtains hardware information of a CPU and a GPU of a processing device, the determining unit 302 and the comparing unit 303 evaluate whether the processing device has relatively strong CPU performance or relatively strong GPU performance according to the hardware information, and the processing unit 304 determines an algorithm used for processing topographic data according to a comparison result. By the method, a better algorithm can be selected according to the CPU performance and the GPU performance of the processing equipment, and the processing efficiency of the terrain data is effectively improved.
Referring to fig. 4, fig. 4 is a diagram illustrating another embodiment of the terrain data processing system provided in the present application, where the system includes:
a reading unit 401, configured to read CPU hardware parameters and GPU hardware parameters;
a determining unit 402, configured to determine CPU performance according to the CPU hardware parameters, and determine GPU performance according to the GPU hardware parameters;
a comparing unit 403, configured to compare the CPU performance and the GPU performance to obtain a comparison result;
and the processing unit 404 is configured to determine a target algorithm according to the comparison result, and process the terrain data through the target algorithm.
In this embodiment system, the processing unit further includes:
a first processing module 4041, configured to process the terrain data through a ROAM algorithm when it is determined that the CPU performance is stronger than the GPU performance.
The second processing module 4042 is configured to process the terrain data through the GeoMipMap algorithm when it is determined that the GPU performance is stronger than the CPU performance.
The determining unit 402 is specifically configured to:
determining a CPU calculation performance peak value according to the master frequency, the front-end bus frequency and the cache in the CPU hardware parameters;
determining a GPU computing performance peak value according to the number of CUDA cores in GPU hardware parameters, the size of a memory and the bandwidth of the memory;
the comparing unit 401 is specifically configured to:
and comparing the CPU calculation performance peak value with the GPU calculation performance peak value to obtain a comparison result.
Or the like, or, alternatively,
the determining unit 402 is specifically configured to:
performing four-fork spatial partition tree processing algorithm operation capability evaluation on the CPU according to CPU hardware parameters to obtain a first evaluation value;
performing operation capability evaluation on the GPU by using a quad space division tree processing algorithm according to GPU hardware parameters to obtain a second evaluation value;
the comparing unit 403 is specifically configured to:
and comparing the first evaluation value with the second evaluation value to obtain a comparison result.
Optionally, the terrain data is processed and generated by a quad space partition tree processing algorithm, and the second processing module 4042 is further specifically configured to:
when the CPU performance is determined to be stronger than the GPU performance, judging whether the side length of the terrain data to be processed meets (2^ n +1) × (2^ n + 1);
and if the side length of the terrain data to be processed meets (2^ n +1) × (2^ n +1), processing the terrain data through an ROAM algorithm.
In the system of this embodiment, the functions of each unit correspond to the steps in the method embodiment shown in fig. 2, and are not described herein again.
Referring to fig. 5, fig. 5 is a diagram illustrating an embodiment of a terrain data processing apparatus according to the present application, where the apparatus includes:
a processor 501, a memory 502, an input/output unit 503, and a bus 504;
the processor 501 is connected with the memory 502, the input/output unit 503 and the bus 504;
the memory 502 holds a program that the processor 501 calls to execute any of the above-described topographic data processing methods.
The present application also relates to a computer-readable storage medium having a program stored thereon, wherein the program, when executed on a computer, causes the computer to perform any of the above-described terrain data processing methods.
It is clear to those skilled in the art that, for convenience and brevity of description, the specific working processes of the above-described systems, apparatuses and units may refer to the corresponding processes in the foregoing method embodiments, and are not described herein again.
In the several embodiments provided in the present application, it should be understood that the disclosed system, apparatus and method may be implemented in other manners. For example, the above-described apparatus embodiments are merely illustrative, and for example, the division of the units is only one logical division, and other divisions may be realized in practice, for example, a plurality of units or components may be combined or integrated into another system, or some features may be omitted, or not executed. In addition, the shown or discussed mutual coupling or direct coupling or communication connection may be an indirect coupling or communication connection through some interfaces, devices or units, and may be in an electrical, mechanical or other form.
The units described as separate parts may or may not be physically separate, and parts displayed as units may or may not be physical units, may be located in one place, or may be distributed on a plurality of network units. Some or all of the units can be selected according to actual needs to achieve the purpose of the solution of the embodiment.
In addition, functional units in the embodiments of the present application may be integrated into one processing unit, or each unit may exist alone physically, or two or more units are integrated into one unit. The integrated unit can be realized in a form of hardware, and can also be realized in a form of a software functional unit.
The integrated unit, if implemented in the form of a software functional unit and sold or used as a stand-alone product, may be stored in a computer readable storage medium. Based on such understanding, the technical solutions of the present application, which are essential or part of the technical solutions contributing to the prior art, or all or part of the technical solutions, may be embodied in the form of a software product, which is stored in a storage medium and includes several instructions for causing a computer device (which may be a personal computer, a server, or a network device, etc.) to execute all or part of the steps of the methods described in the embodiments of the present application. And the aforementioned storage medium includes: a U-disk, a removable hard disk, a read-only memory (ROM), a Random Access Memory (RAM), a magnetic disk or an optical disk, and the like.
Claims (10)
1. A method of processing topographic data, the method comprising:
reading CPU hardware parameters and GPU hardware parameters;
determining the CPU performance according to the CPU hardware parameters, and determining the GPU performance according to the GPU hardware parameters;
comparing the CPU performance with the GPU performance to obtain a comparison result;
and determining a target algorithm according to the comparison result, and processing the terrain data through the target algorithm.
2. The method of claim 1, wherein determining a target algorithm from the comparison results by which to process terrain data comprises:
and when the CPU performance is determined to be higher than the GPU performance, processing the terrain data through an ROAM algorithm.
3. The method of claim 1, wherein determining a target algorithm from the comparison results by which to process terrain data comprises:
and when the GPU performance is determined to be stronger than the CPU performance, processing the terrain data through a GeoMipMap algorithm.
4. The method of any of claims 1 to 3, wherein said determining CPU performance from said CPU hardware parameters comprises:
determining a CPU calculation performance peak value according to the master frequency, the front-end bus frequency and the cache in the CPU hardware parameters;
the determining GPU performance according to the GPU hardware parameters comprises:
determining a GPU computing performance peak value according to the number of CUDA cores, the size of a memory and the bandwidth of the memory in the GPU hardware parameters;
the comparing the CPU performance and the GPU performance to obtain a comparison result comprises:
and comparing the CPU calculation performance peak value with the GPU calculation performance peak value to obtain a comparison result.
5. The method of any of claims 1 to 3, wherein the terrain data is processed by a quad spatial partitioning tree processing algorithm, and wherein determining the CPU performance of the processing device based on the CPU hardware parameters and determining the GPU performance of the processing device based on the GPU hardware parameters comprises:
performing four-fork spatial partition tree processing algorithm operation capability evaluation on the CPU according to the CPU hardware parameters to obtain a first evaluation value;
performing operation capability evaluation on the GPU by using a quad space division tree processing algorithm according to the GPU hardware parameters to obtain a second evaluation value;
the comparing the CPU performance and the GPU performance to obtain a comparison result comprises:
and comparing the first evaluation value with the second evaluation value to obtain a comparison result.
6. The method of claim 3, wherein the terrain data is processed by a quad spatial partitioning tree processing algorithm, and wherein the processing the terrain data by a GeoMipMap algorithm when the GPU performance is determined to be stronger than the CPU performance comprises:
when the GPU performance is determined to be stronger than the CPU performance, judging whether the side length of the terrain data to be processed meets (2^ n +1) × (2^ n + 1);
and if the side length of the terrain data to be processed meets (2^ n +1) × (2^ n +1), processing the terrain data through an ROAM algorithm.
7. A terrain data processing system, the system comprising:
the reading unit is used for reading the CPU hardware parameters and the GPU hardware parameters;
the determining unit is used for determining the CPU performance according to the CPU hardware parameters and determining the GPU performance according to the GPU hardware parameters;
the comparison unit is used for comparing the CPU performance with the GPU performance to obtain a comparison result;
and the processing unit is used for determining a target algorithm according to the comparison result and processing the terrain data through the target algorithm.
8. The system of claim 7, wherein the processing unit comprises:
and the first processing module is used for processing the terrain data through an ROAM algorithm when the CPU performance is determined to be stronger than the GPU performance.
9. The system of claim 7, wherein the processing unit comprises:
and the second processing module is used for processing the terrain data through a GeoMipMap algorithm when the GPU performance is determined to be stronger than the CPU performance.
10. A topographic data processing device, characterized in that the device comprises:
the device comprises a processor, a memory, an input and output unit and a bus;
the processor is connected with the memory, the input and output unit and the bus;
the memory holds a program that the processor calls to perform the method of any one of claims 1 to 6.
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| CN115297359A (en) * | 2022-07-29 | 2022-11-04 | 北京字跳网络技术有限公司 | Multimedia data transmission method and device, electronic equipment and storage medium |
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| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN115297359A (en) * | 2022-07-29 | 2022-11-04 | 北京字跳网络技术有限公司 | Multimedia data transmission method and device, electronic equipment and storage medium |
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