Physically based shading in theory and practice

Hill, Stephen; McAuley, Stephen; Jover, C.; Pesce, Angelo; Wu, Xian-Chun; Cordes, R. D.; Hery, Christophe; Hillaire, Sébastien; Hoffman, Naty; Jiménez, Jorge; Karis, Brian; Lagarde, Sébastien; Lobl, Dan; Villemin, Ryusuke

doi:10.1145/2897826.2927353

Cited by 4 publications

(1 citation statement)

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“…Fitting Procedure. In our rendering examples, we approximate the GGX microfacet BRDF parameterized by an isotropic roughness coe cient α ∈ [0, 1]; this was motivated by the fact that it it is the most widespread BRDF used in the industry [McAuley and Hill 2016]. We adapted the code provided by Heitz et al [2016] to compute the pivot parameter that produced the best approximation for Equation (14) given a speci c input spherical distribution.…”

Section: Shading Pipelinementioning

confidence: 99%

A spherical cap preserving parameterization for spherical distributions

Dupuy¹,

Heitz²,

Belcour³

2017

ACM Trans. Graph.

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Real-time: analytic spherical-cap integration Joint MIS (ours) 64spp MIS (previous) 64spp O ine: joint BRDF/spherical-cap sampling Fig. 1. Real-time application (le ). We approximate BRDFs with our distributions and shade with sphere lights in real-time using their analytic spherical-cap integral. The scene is rendered at 1080p and runs at 60fps on an NVIDIA 980 GTX. O line application (right). We compute the reference image with the exact BRDFs using importance sampling techniques. We use our distribution as a proxy for the BRDF and generate be er samples that are distributed jointly inside the lights and close to the BRDFs. This joint sampling scheme is unbiased and has lower variance than multiple importance sampling with separate BRDF and light sampling.We introduce a novel parameterization for spherical distributions that is based on a point located inside the sphere, which we call a pivot. The pivot serves as the center of a straight-line projection that maps solid angles onto the opposite side of the sphere. By transforming spherical distributions in this way, we derive novel parametric spherical distributions that can be evaluated and importance-sampled from the original distributions using simple, closedform expressions. Moreover, we prove that if the original distribution can be sampled and/or integrated over a spherical cap, then so can the transformed distribution. We exploit the properties of our parameterization to derive e cient spherical lighting techniques for both real-time and o ine rendering. Our techniques are robust, fast, easy to implement, and achieve quality that is superior to previous work.

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Section: Shading Pipelinementioning

confidence: 99%

A spherical cap preserving parameterization for spherical distributions

Dupuy¹,

Heitz²,

Belcour³

2017

ACM Trans. Graph.

View full text Add to dashboard Cite

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Spatiotemporal variance-guided filtering

Schied

Kaplanyan

Wyman

et al. 2017

Proceedings of High Performance Graphics

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MIS compensation

Karlík¹,

Šik²,

Vévoda

et al. 2019

ACM Trans. Graph.

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Multiple importance sampling (MIS) has become an indispensable tool in Monte Carlo rendering, widely accepted as a near-optimal solution for combining different sampling techniques. But an MIS combination, using the common balance or power heuristics, often results in an overly defensive estimator, leading to high variance. We show that by generalizing the MIS framework, variance can be substantially reduced. Specifically, we optimize one of the combined sampling techniques so as to decrease the overall variance of the resulting MIS estimator. We apply the approach to the computation of direct illumination due to an HDR environment map and to the computation of global illumination using a path guiding algorithm. The implementation can be as simple as subtracting a constant value from the tabulated sampling density done entirely in a preprocessing step. This produces a consistent noise reduction in all our tests with no negative influence on run time, no artifacts or bias, and no failure cases.

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Physically based shading in theory and practice

Cited by 4 publications

References 0 publications

A spherical cap preserving parameterization for spherical distributions

A spherical cap preserving parameterization for spherical distributions

Spatiotemporal variance-guided filtering

MIS compensation

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