Shadow volume optimization method based on KD tree

Abstract

The embodiment of the invention provides a shadow volume optimization method based on a KD tree; the method comprises the following steps: extracting sharing edges, located on two sides of a light source, of adjacent surfaces as shared contour edges of the shadow volume; segmenting scene data according to the KD tree, extracting the edge with only one adjacent surface to make shading calculation, and extracting boundaries with no shading as the shadow volume boundary contour edges; building the shadow volume according to the shared contour edges and boundary contour edges; segmenting the shadow volume data according to the KD tree, using the shadow volume to remove a bounding volume, removing shadows, and improving the shadow volume peak processing rate. The shadow volume optimization method can solve the problems that a conventional shadow volume algorithm cannot process a non-closed geometry scene, and uses the CPU end shadow volume cutting works to greatly save data bulk inputted into a GPU, thus reducing the GPU burdens.

Description

technical field [0001] The invention relates to the field of computer graphics rendering, more specifically, to a method for optimizing shadow volumes based on a KD (K-dimensional space) tree. Background technique [0002] A shadow is a certain area in a space where light is blocked. The shadow effect can help people better perceive the distance and relative position between objects, and at the same time help people obtain information such as the geometry of shadow occluders and shadow receivers. [0003] In related technologies, there is no standard dynamic shadow rendering technology so far. In order to render realistic dynamic shadow effects, relevant researchers have made many attempts and proposed many realistic shadow rendering algorithms. Among these different algorithms, the shadow volume algorithm is a mainstream efficient dynamic shadow algorithm. [0004] The shadow volume algorithm is always limited by two key steps: one is the extraction of silhouette edges, ...

AI Technical Summary

Problems solved by technology

For the extraction of silhouette edges, the current extraction method is to judge the phototropism of the adjacent faces that share the edge, but the problem with this method is that the model is required to be closed, and scenes such as leaves cannot be applied

[0005] For the problems of non-closed models and shadow volume rendering in related technologies, no effective solution has been proposed so far.

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