Photon surfaces for robust, unbiased volumetric density estimation

1Dartmouth College

In ACM Transactions on Graphics (Proceedings of SIGGRAPH), 2019

Teaser
We compare the equal-time variance of different estimators in a scene containing participating media. We show the full light transport in the scene (left), single scattering (middle/right, top half) and multiple scattering volumetric transport (middle/right, bottom half). Our estimators (middle) provide significant variance reduction compared to prior density estimators (right) at equal render time.

Abstract

We generalize photon planes to photon surfaces: a new family of unbiased volumetric density estimators which we combine using multiple importance sampling. To derive our new estimators, we start with the extended path integral which duplicates the vertex at the end of the camera and photon subpaths and couples them using a blurring kernel. To make our formulation unbiased, however, we use a delta kernel to couple these two end points. Unfortunately, sampling the resulting singular integral using Monte Carlo is impossible since the probability of generating a contributing light path by independently sampling the two subpaths is zero. Our key insight is that we can eliminate the delta kernel and make Monte Carlo estimation practical by integrating any three dimensions analytically, and integrating only the remaining dimensions using Monte Carlo. We demonstrate the practicality of this approach by instantiating a collection of estimators which analytically integrate the distance along the camera ray and two arbitrary sampling dimensions along the photon subpath (e.g., distance, direction, surface area). This generalizes photon planes to curved “photon surfaces”, including new “photon cone”, “photon cylinder”, “photon sphere”, and multiple new “photon plane” estimators. These estimators allow us to handle light paths not supported by photon planes, including single scattering, and surface-to-media transport. More importantly, since our estimators have complementary strengths due to analytically integrating different dimensions of the path integral, we can combine them using multiple importance sampling. This combination mitigates singularities present in individual estimators, substantially reducing variance while remaining fully unbiased. We demonstrate our improved estimators on a number of scenes containing homogeneous media with highly anisotropic phase functions, accelerating both multiple scattering and single scattering compared to prior techniques.

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Acknowledgements

We would like the thank members of the Dartmouth Visual Computing Lab for fruitful discussions and the anonymous reviewers for suggestions on improving the paper. We use scenes provided by the following Blend Swap artists: Mareck (Bathroom), Jay-Artist (Kitchen, LivingRoom), NovaAshbell (ClassRoom), and Wig42 (HorseRoom, DiningRoom). The bunny model was provided by the Stanford 3D Scanning Repository and the cloud data by Walt Disney Animation Studios. This work was supported by NSF Grants IIS-1812796 and CNS-1205521.

Cite

Xi Deng, Shaojie Jiao, Benedikt Bitterli, Wojciech Jarosz. Photon surfaces for robust, unbiased volumetric density estimation. ACM Transactions on Graphics (Proceedings of SIGGRAPH), 38(4), July 2019.
@article{deng19photon,
    author = "Deng, Xi and Jiao, Shaojie and Bitterli, Benedikt and Jarosz, Wojciech",
    title = "Photon surfaces for robust, unbiased volumetric density estimation",
    journal = "ACM Transactions on Graphics (Proceedings of SIGGRAPH)",
    volume = "38",
    number = "4",
    year = "2019",
    month = jul,
    doi = "10/gf6rx9",
    keywords = "photon beams, photon mapping, participating media, multiple importance sampling, MIS",
    abstract = "We generalize photon planes to photon surfaces: a new family of unbiased volumetric density estimators which we combine using multiple importance sampling. To derive our new estimators, we start with the extended path integral which duplicates the vertex at the end of the camera and photon subpaths and couples them using a blurring kernel. To make our formulation unbiased, however, we use a delta kernel to couple these two end points. Unfortunately, sampling the resulting singular integral using Monte Carlo is impossible since the probability of generating a contributing light path by independently sampling the two subpaths is zero. Our key insight is that we can eliminate the delta kernel and make Monte Carlo estimation practical by integrating any three dimensions analytically, and integrating only the remaining dimensions using Monte Carlo. We demonstrate the practicality of this approach by instantiating a collection of estimators which analytically integrate the distance along the camera ray and two arbitrary sampling dimensions along the photon subpath (e.g., distance, direction, surface area). This generalizes photon planes to curved ``photon surfaces'', including new ``photon cone'', ``photon cylinder'', ``photon sphere'', and multiple new ``photon plane'' estimators. These estimators allow us to handle light paths not supported by photon planes, including single scattering, and surface-to-media transport. More importantly, since our estimators have complementary strengths due to analytically integrating different dimensions of the path integral, we can combine them using multiple importance sampling. This combination mitigates singularities present in individual estimators, substantially reducing variance while remaining fully unbiased. We demonstrate our improved estimators on a number of scenes containing homogeneous media with highly anisotropic phase functions, accelerating both multiple scattering and single scattering compared to prior techniques."
}
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