Three-dimensional large-coordinate processing method based on GPU

Abstract

The invention discloses a three-dimensional large-coordinate processing method based on a GPU. The method comprises the steps of collecting and obtaining an integer part and a decimal part of a modelposition; solving a correction offset position of the camera position; constructing a correction offset matrix of the model by utilizing the correction offset position of the camera position; solvingan observation matrix of the correction offset matrix; solving the position of the model in the correction space; solving a change matrix of the model in the correction space; solving a change matrixfrom the model space to an equipment space (equipment refers to abstract display equipment); and obtaining the vertex position of the model local vertex in the equipment space according to the changematrix from the model local space to the equipment space. By means of the method, the method has the advantages of being simple in logic, small in calculation workload, accurate in calculation and thelike, and has high practical value and promotional value in the technical field of large-coordinate data processing.

Description

technical field [0001] The invention relates to the technical field of large coordinate data processing, in particular to a GPU-based three-dimensional large coordinate processing method. Background technique [0002] The large coordinates mentioned in this article refer to the coordinates of the model’s position in the world space. On the 3D geographic information platform (hereinafter referred to as 3D GIS), this coordinate is usually relatively large, and at least double floating point is required to ensure its accuracy when drawing on the GPU. Precision; however, when a computer CPU processes floating-point numbers, it usually uses three standards: double floating point, single floating point, and half floating point. Among the above three standards, double-floating point and single-floating point are the most popular; usually, in GPU processing, more single-floating point calculations are used, and its advantages are mainly concentrated in: First, it takes up less stora...

AI Technical Summary

Problems solved by technology

However, this method has the following defects: 1. The double-precision calculation and single-floating-point conversion process in the early stage increases the technical workload, takes up computing memory, and has low processing efficiency. Advantage

However, this method needs to recalculate the local coordinate system every frame because the frustum is changing, and then update the position of the model in the local coordinate system. The first method, which can use GPU instances to draw, but frequently updates the GPU End instance data also severely degrades drawing performance

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