Efficient Unbiased Volume Path Tracing on the GPU
Hofmann N, Evans A (2021)
Publication Language: English
Publication Type: Book chapter / Article in edited volumes
Publication year: 2021
Edited Volumes: Ray Tracing Gems II
ISBN: 978-1-4842-7184-1
DOI: 10.1007/978-1-4842-7185-8_43
Open Access Link: https://doi.org/10.1007/978-1-4842-7185-8_43
Abstract
We present a set of optimizations that improve the performance of high-quality volumetric path tracing. We build upon unbiased volume sampling techniques, i.e., null-collision trackers, with voxel data stored in an OpenVDB tree. The presented optimizations achieve an overall 2x to 3x speedup when implemented on a modern GPU, with an approximately 6.5x reduction in memory footprint. The improvements primarily stem from a multi-level digital differential analyzer (DDA) to step through a grid of precomputed bounds; a replacement of the top levels of the OpenVDB tree with a dense indirection texture, similar to virtual textures, while preserving some sparsity; and quantization of the voxel data, encoded using GPU-supported block compression. Finally, we examine the isolated effect of our optimizations, covering stochastic filtering, the use of dense indirection textures, compressed voxel data, and singleversus multi-level DDAs.
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APA:
Hofmann, N., & Evans, A. (2021). Efficient Unbiased Volume Path Tracing on the GPU. In Ray Tracing Gems II..
MLA:
Hofmann, Nikolai, and Alex Evans. "Efficient Unbiased Volume Path Tracing on the GPU." Ray Tracing Gems II. 2021.
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