A Simple Approach to Differentiable Rendering of SDFs
Fuente:
arXiv
Saved in:
| Main Authors: | , , , , |
|---|---|
| Format: | Preprint |
| Published: |
2024
|
| Subjects: | |
| Online Access: | |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1866910476062949376 |
|---|---|
| author | Wang, Zichen Deng, Xi Zhang, Ziyi Jakob, Wenzel Marschner, Steve |
| author_facet | Wang, Zichen Deng, Xi Zhang, Ziyi Jakob, Wenzel Marschner, Steve |
| contents | We present a simple algorithm for differentiable rendering of surfaces represented by Signed Distance Fields (SDF), which makes it easy to integrate rendering into gradient-based optimization pipelines. To tackle visibility-related derivatives that make rendering non-differentiable, existing physically based differentiable rendering methods often rely on elaborate guiding data structures or reparameterization with a global impact on variance. In this article, we investigate an alternative that embraces nonzero bias in exchange for low variance and architectural simplicity. Our method expands the lower-dimensional boundary integral into a thin band that is easy to sample when the underlying surface is represented by an SDF. We demonstrate the performance and robustness of our formulation in end-to-end inverse rendering tasks, where it obtains results that are competitive with or superior to existing work. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2405_08733 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | A Simple Approach to Differentiable Rendering of SDFs Wang, Zichen Deng, Xi Zhang, Ziyi Jakob, Wenzel Marschner, Steve Graphics Computer Vision and Pattern Recognition We present a simple algorithm for differentiable rendering of surfaces represented by Signed Distance Fields (SDF), which makes it easy to integrate rendering into gradient-based optimization pipelines. To tackle visibility-related derivatives that make rendering non-differentiable, existing physically based differentiable rendering methods often rely on elaborate guiding data structures or reparameterization with a global impact on variance. In this article, we investigate an alternative that embraces nonzero bias in exchange for low variance and architectural simplicity. Our method expands the lower-dimensional boundary integral into a thin band that is easy to sample when the underlying surface is represented by an SDF. We demonstrate the performance and robustness of our formulation in end-to-end inverse rendering tasks, where it obtains results that are competitive with or superior to existing work. |
| title | A Simple Approach to Differentiable Rendering of SDFs |
| topic | Graphics Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2405.08733 |