article · ACM Transactions on Graphics · 2020
Path-space differentiable rendering
Cheng Zhang, Bailey Miller, Kai Yan, Ioannis Gkioulekas, Shuang Zhao
Cited by 144Abstract and details only
Abstract
Physics-based differentiable rendering, the estimation of derivatives of radiometric measures with respect to arbitrary scene parameters, has a diverse array of applications from solving analysis-by-synthesis problems to training 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. We validate our method by comparing our derivative estimates to those generated using the finite-difference method. To demonstrate the effectiveness of our technique, we compare inverse-rendering performance with a few state-of-the-art differentiable rendering methods.
Cite this paper
Zhang, C., Miller, B., Yan, K., Gkioulekas, I., & Zhao, S. (2020). Path-space differentiable rendering. ACM Transactions on Graphics, 39(4). https://doi.org/10.1145/3386569.3392383
Zhang, Cheng, et al. “Path-space differentiable rendering.” ACM Transactions on Graphics, vol. 39, no. 4, 2020, https://doi.org/10.1145/3386569.3392383.
Zhang, Cheng, Bailey Miller, Kai Yan, Ioannis Gkioulekas, and Shuang Zhao. 2020. “Path-space differentiable rendering.” ACM Transactions on Graphics 39 (4). https://doi.org/10.1145/3386569.3392383.
Zhang, C. et al. (2020) ‘Path-space differentiable rendering’, ACM Transactions on Graphics, 39(4). Available at: https://doi.org/10.1145/3386569.3392383.
@article{zhang2020path,
title = {{Path-space differentiable rendering}},
author = {Zhang, Cheng and Miller, Bailey and Yan, Kai and Gkioulekas, Ioannis and Zhao, Shuang},
journal = {ACM Transactions on Graphics},
volume = {39},
number = {4},
year = {2020},
doi = {10.1145/3386569.3392383}
}Made from the index record. Check names, capitalisation and page numbers against the paper. Need another style? AI for Research has over 10,000.