Ray tracing acceleration method based on an improved KD-tree structure

Fubing Li; Haoyang Lu; Ao Ye

doi:10.1117/12.3033172

13 June 2024 Ray tracing acceleration method based on an improved KD-tree structure

Fubing Li, Haoyang Lu, Ao Ye

Proceedings Volume 13180, International Conference on Image, Signal Processing, and Pattern Recognition (ISPP 2024); 131801Q (2024) https://doi.org/10.1117/12.3033172
Event: International Conference on Image, Signal Processing, and Pattern Recognition (ISPP 2024), 2024, Guangzhou, China

Abstract

When ray tracing is performed based on the traditional KD-Tree (K-Dimensional tree) structure, a ray may first pass through a number of leaf nodes before it intersects with one facet in the “last” leaf node. During this process, the ray needs to sequentially perform the ray-triangle intersection with all the internal facets in each of the aforementioned leaf nodes before it enters into the next leaf node, wasting a large amount of computation time. In this paper, an improved KD-Tree structure is proposed. In addition to the space occupied by a leaf node in the traditional KD-Tree, the minimum bounding box occupied by the facets of the leaf node is also introduced for the leaf node. If a ray does not intersect with the minimum bounding box when it enters into a leaf node, it will directly penetrate out of the leaf node without performing ray-triangle intersection procedure and then enter into the next leaf node. Simulation results for T-cavity and L-cavity models show that, under the parameter conditions of this paper, the total ray tracing time based on the improved KD-Tree structure is reduced by 10% to 44% compared with the traditional KD-Tree, which effectively improves the computational efficiency of the ray tracing process.

(2024) Published by SPIE. Downloading of the abstract is permitted for personal use only.

Citation Download Citation

Fubing Li, Haoyang Lu, and Ao Ye "Ray tracing acceleration method based on an improved KD-tree structure", Proc. SPIE 13180, International Conference on Image, Signal Processing, and Pattern Recognition (ISPP 2024), 131801Q (13 June 2024); https://doi.org/10.1117/12.3033172

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