Gradient-domain Volumetric Photon Density Estimation

Abstract : Gradient-domain rendering can improve the convergence of surface-based light transport by exploiting smoothness in image space. Scenes with participating media exhibit similar smoothness and could potentially benefit from gradient-domain techniques. We introduce the first gradient-domain formulation of image synthesis with homogeneous participating media, including four novel and efficient gradient-domain volumetric density estimation algorithms. We show that naive extensions of gradient domain path-space and density estimation methods to volumetric media, while functional, can result in inefficient estimators. Focussing on point-, beam- and plane-based gradient-domain estimators, we introduce a novel shift mapping that eliminates redundancies in the naive formulations using spatial relaxation within the volume. We show that gradient-domain volumetric rendering improves convergence compared to primal domain state-of-the-art, across a suite of scenes. Our formulation and algorithms support progressive estimation and are easy to incorporate atop existing renderers.
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Adrien Gruson, Binh-Son Hua, Nicolas Vibert, Derek Nowrouzezahrai, Toshiya Hachisuka. Gradient-domain Volumetric Photon Density Estimation. ACM Transactions on Graphics, Association for Computing Machinery, 2018, 37, ⟨10.1145/3197517.3201363⟩. ⟨hal-01796774⟩

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