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


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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Hofmann, N., & Evans, A. (2021). Efficient Unbiased Volume Path Tracing on the GPU. In Ray Tracing Gems II..


Hofmann, Nikolai, and Alex Evans. "Efficient Unbiased Volume Path Tracing on the GPU." Ray Tracing Gems II. 2021.

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