“Advances in Spatial Hashing: A Pragmatic Approach towards Robust, Real-time Light Transport Simulation” by Gautron

  • ©Pascal Gautron

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    Advances in Spatial Hashing: A Pragmatic Approach towards Robust, Real-time Light Transport Simulation

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    Spatial hashing is a battle-tested technique for efficiently storing sparse spatial data. Originally designed to optimize secondary light bounces in path tracing, it has been extended for real-time ambient occlusion and diffuse environment lighting. We complement spatial hashing by introducing support for view-dependent effects using world-space temporal filtering. Optimizing the hash key generation, we improve performance using a much better cache coherence and aliasing reduction. Finally, we enhance the sampling quality using methods including visibility-aware environment sampling.

References:


    1. Nikolaus Binder, Sascha Fricke, and Alexander Keller. 2022. Massively Parallel Path Space Filtering. In Monte Carlo and Quasi-Monte Carlo Methods, MCQMC 2020, Oxford, United Kingdom, August 10–14, Alexander Keller (Ed.). Springer.
    2. Nikolaus Binder and Alexander Keller. 2019. Massively Parallel Construction of Radix Tree Forests for the Efficient Sampling of Discrete Probability Distributions. CoRR abs/1901.05423(2019). arXiv:1901.05423http://arxiv.org/abs/1901.05423
    3. George S. Fishman and Louis R. Moore. 1984. Sampling from a Discrete Distribution while Preserving Monotonicity. The American Statistician 38, 3 (1984), 219–223.
    4. Pascal Gautron. 2020. Real-Time Ray-Traced Ambient Occlusion of Complex Scenes Using Spatial Hashing. In ACM SIGGRAPH 2020 Talks. Article 5. https://doi.org/10.1145/3388767.3407375
    5. Pascal Gautron. 2021. Ray Tracing Gems II: Next Generation Real-Time Rendering with DXR, Vulkan, and OptiX. Apress, Chapter Practical Spatial Hash Map Updates, 659–671. https://doi.org/10.1007/978-1-4842-7185-8_41
    6. Christoph Schied, Anton Kaplanyan, Chris Wyman, Anjul Patney, Chakravarty R. Alla Chaitanya, John Burgess, Shiqiu Liu, Carsten Dachsbacher, Aaron Lefohn, and Marco Salvi. 2017. Spatiotemporal Variance-Guided Filtering: Real-Time Reconstruction for Path-Traced Global Illumination. In Proceedings of High Performance Graphics. Article 2. https://doi.org/10.1145/3105762.3105770

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