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

  • ©Pascal Gautron




    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.


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    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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