Differentiable Rendering of Neural SDFs through Reparameterization-Reference-Cited by-同舟云学术

Differentiable Rendering of Neural SDFs through Reparameterization

Published:2022-11-29 Issue: Volume: Page:
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Container-title:SIGGRAPH Asia 2022 Conference Papers
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Author:

Bangaru Sai Praveen¹^ORCID,Gharbi Michael²,Luan Fujun²,Li Tzu-Mao³,Sunkavalli Kalyan²,Hasan Milos²,Bi Sai²,Xu Zexiang²,Bernstein Gilbert⁴,Durand Fredo¹

Affiliation:

1. Computer Science and Artificial Intelligence Lab, Massachusetts Institute of Technology, United States of America

2. Adobe Research, United States of America

3. University of California San Diego, United States of America

4. Computer Science and Artificial Intelligence Lab, Massachusetts Institute of Technology, United States of America and UC Berkeley, United States of America

Funder

NSF (National Science Foundation)

Publisher

ACM

Link

https://dl.acm.org/doi/pdf/10.1145/3550469.3555397

Reference50 articles.

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2. Matan Atzmon and Yaron Lipman . 2020 . SAL: Sign Agnostic Learning of Shapes From Raw Data. In IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR). Matan Atzmon and Yaron Lipman. 2020. SAL: Sign Agnostic Learning of Shapes From Raw Data. In IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR).

3. Unbiased warped-area sampling for differentiable rendering

4. Sai Bi Zexiang Xu Pratul P. Srinivasan Ben Mildenhall Kalyan Sunkavalli Milos Hasan Yannick Hold-Geoffroy David J. Kriegman and Ravi Ramamoorthi. 2020a. Neural Reflectance Fields for Appearance Acquisition. CoRR abs/2008.03824(2020). Sai Bi Zexiang Xu Pratul P. Srinivasan Ben Mildenhall Kalyan Sunkavalli Milos Hasan Yannick Hold-Geoffroy David J. Kriegman and Ravi Ramamoorthi. 2020a. Neural Reflectance Fields for Appearance Acquisition. CoRR abs/2008.03824(2020).

5. Deep Reflectance Volumes: Relightable Reconstructions from Multi-view Photometric Images

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