Unbiased warped-area sampling for differentiable rendering
Name
3414685.3417833.pdf
Description
Published version
Size
6.99 MB
Format
Adobe PDF
Checksum (MD5)
1118d222807321a09862180849e41b75
Author(s)
Bangaru, Sai Praveen
Li, Tzu-Mao
Durand, Frédo
Date Issued
2020
Journal
ACM Transactions on Graphics
Publisher
Association for Computing Machinery (ACM)
Citation
Bangaru, Sai Praveen, Li, Tzu-Mao and Durand, Frédo. 2020. "Unbiased warped-area sampling for differentiable rendering." ACM Transactions on Graphics, 39 (6).
Version
Final published version
Abstract
© 2020 Owner/Author. 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. We analyze the convergence properties through bias-variance metrics, and demonstrate our estimator's advantages over existing methods for some synthetic inverse rendering examples.
MIT Department
Massachusetts Institute of Technology. Computer Science and Artificial Intelligence Laboratory
Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science
Terms of Use
Creative Commons Attribution 4.0 International license
Persistent DSpace Link
DOI of Published Version
https://doi.org/10.1145/3414685.3417833
