Antithetic Sampling for Monte Carlo Differentiable Rendering

Cheng Zhang
University of California, Irvine, USA and Facebook Reality Labs, USA

Zhao Dong
Facebook Reality Labs, USA

Michael Doggett
Lund University and Facebook Reality Labs, USA

Shuang Zhao
University of California, Irvine, USA

ACM Transactions on Graphics (SIGGRAPH 2021), 40(4), August 2021

Abstract

Stochastic sampling of light transport paths is key to Monte Carlo forward rendering, and previous studies have led to mature techniques capable of drawing high-contribution light paths in complex scenes. These sampling techniques have also been applied to differentiable rendering. In this paper, we demonstrate that path sampling techniques developed for forward rendering can become inefficient for differentiable rendering of glossy materials - especially when estimating derivatives with respect to global scene geometries. To address this problem, we introduce antithetic sampling of BSDFs and light-transport paths, allowing significantly faster convergence and can be easily integrated into existing differentiable rendering pipelines. We validate our method by comparing our derivative estimates to those generated with existing unbiased techniques. Further, we demonstrate the effectiveness of our technique by providing equal-quality and equal-time comparisons with existing sampling methods.

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