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Creators/Authors contains: "Yan, Kan"

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  1. Physics-based differentiable rendering, the estimation of derivatives of ra- diometric measures with respect to arbitrary scene parameters, has a diverse array of applications from solving analysis-by-synthesis problems to train- ing machine learning pipelines incorporating forward rendering processes. Unfortunately, general-purpose differentiable rendering remains challenging due to the lack of efficient estimators as well as the need to identify and handle complex discontinuities such as visibility boundaries. In this paper, we show how path integrals can be differentiated with respect to arbitrary differentiable changes of a scene. We provide a detailed theoretical analysis of this process and establish new differentiable rendering formulations based on the resulting differential path integrals. Our path- space differentiable rendering formulation allows the design of new Monte Carlo estimators that offer significantly better efficiency than state-of-the-art methods in handling complex geometric discontinuities and light transport phenomena such as caustics. 
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