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Fast product importance sampling of environment maps

Published: 12 August 2018 Publication History

Abstract

Environment maps have been used for decades in production path-tracers to recreate ambient lighting conditions captured from real world scenes. Stochastic sampling of the radiance integral can be very challenging however, as both the BSDF and the environment can have strong peaks that are not aligned with each other. Multiple importance sampling (MIS) between the environment and the BSDF is a common way to reduce variance by re-weighting each estimator, but can still result in wasted samples. Product importance sampling is an effective way to reduce the variance by drawing samples using a probability distribution built from the product of the BSDF and the environment map. To our knowledge, the most practical product sampling technique [Clarberg and Akenine-Möller 2008] is still relatively costly for production rendering because it approximates the BSDF by a sparse quad-tree built on the fly from a few hundred BSDF samples. Due to the high complexity of the multi-lobed models used in film rendering, this cost can be prohibitive.

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MP4 File (71-190-conty-estevez.mp4)

References

[1]
Petrik Clarberg. 2008. Fast Equal-Area Mapping of the (Hemi)Sphere using SIMD. Journal of Graphics Tools 13, 3 (2008), 53-68.
[2]
Petrik Clarberg and Tomas Akenine-Möller. 2008. Practical Product Importance Sampling for Direct Illumination. Computer Graphics Forum (Proceedings of Eurographics) 27, 2 (2008).
[3]
Kartic Subr and James Arvo. 2007. Steerable Importance Sampling. In Proceedings of the 2007 IEEE Symposium on Interactive Ray Tracing (RT '07). IEEE Computer Society, Washington, DC, USA, 133-140.

Cited By

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  • (2024)Neural Product Importance Sampling via Warp CompositionSIGGRAPH Asia 2024 Conference Papers10.1145/3680528.3687566(1-11)Online publication date: 3-Dec-2024
  • (2024)Path guiding for wavefront path tracing: A memory efficient approach for GPU path tracersComputers & Graphics10.1016/j.cag.2024.103945121(103945)Online publication date: Jun-2024
  • (2020)Practical Product Sampling by Fitting and Composing WarpsComputer Graphics Forum10.1111/cgf.1406039:4(149-158)Online publication date: 20-Jul-2020
  • Show More Cited By

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    cover image ACM Conferences
    SIGGRAPH '18: ACM SIGGRAPH 2018 Talks
    August 2018
    158 pages
    ISBN:9781450358200
    DOI:10.1145/3214745
    Permission to make digital or hard copies of part or all of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for third-party components of this work must be honored. For all other uses, contact the Owner/Author.

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    New York, NY, United States

    Publication History

    Published: 12 August 2018

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    Author Tags

    1. illumination
    2. image based lighting
    3. ray tracing
    4. sampling

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    View all
    • (2024)Neural Product Importance Sampling via Warp CompositionSIGGRAPH Asia 2024 Conference Papers10.1145/3680528.3687566(1-11)Online publication date: 3-Dec-2024
    • (2024)Path guiding for wavefront path tracing: A memory efficient approach for GPU path tracersComputers & Graphics10.1016/j.cag.2024.103945121(103945)Online publication date: Jun-2024
    • (2020)Practical Product Sampling by Fitting and Composing WarpsComputer Graphics Forum10.1111/cgf.1406039:4(149-158)Online publication date: 20-Jul-2020
    • (2020)Practical Product Path Guiding Using Linearly Transformed CosinesComputer Graphics Forum10.1111/cgf.1405139:4(23-33)Online publication date: 20-Jul-2020
    • (2019)Hierarchical russian roulette for vertex connectionsACM Transactions on Graphics10.1145/3306346.332301838:4(1-12)Online publication date: 12-Jul-2019

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