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Efficient Unbiased Volume Path Tracing on the GPU

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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 [16, 8, 15], with voxel data stored in an OpenVDB [14, 13] tree. The presented optimizations achieve an overall 2* to 3* speedup when implemented on a modern GPU, with an approximately 6.5* reduction in memory footprint. The improvements primarily stem from a multi-level digital differential analyzer (DDA) [1, 11, 6] 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 [3, 4, 17], 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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Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License (http://creativecommons.org/licenses/by-nc-nd/4.0/), which permits any noncommercial use, sharing, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if you modified the licensed material. You do not have permission under this license to share adapted material derived from this chapter or parts of it.

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Hofmann, N., Evans, A. (2021). Efficient Unbiased Volume Path Tracing on the GPU. In: Marrs, A., Shirley, P., Wald, I. (eds) Ray Tracing Gems II. Apress, Berkeley, CA. https://doi.org/10.1007/978-1-4842-7185-8_43

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