Pre-recorded Sessions: From 4 December 2020 | Live Sessions: 10 – 13 December 2020
4 – 13 December 2020
Unbiased Warped-Area Sampling for Differentiable Rendering
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Ultimate Supporter
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Ultimate Attendee
Date/Time:
04 – 13 December 2020
All presentations are available in the virtual platform on-demand.
Lecturer(s):
Sai Praveen Bangaru, Massachusetts Institute of Technology, United States of America
Tzu-Mao Li, Massachusetts Institute of Technology, United States of America
Fredo P. Durand, Massachusetts Institute of Technology, United States of America
Bio:
Description: Differentiable rendering computes derivatives of the light transport equation with respect to arbitrary 3D scene parameters, and enables various applications in inverse rendering and machine learning. We present an unbiased and efficient differentiable rendering algorithm that does not require explicit boundary sampling. We apply the divergence theorem to the derivative of the rendering integral to convert the boundary integral into an area integral. We rewrite the converted area integral to a form that is suitable for Monte Carlo rendering. We then develop an efficient Monte Carlo sampling algorithm for solving the area integral. Our method can be easily plugged into a traditional path tracer and does not require dedicated data structures for sampling boundaries.
