Learning to Rasterize Differentiably
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arXiv
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| Main Authors: | , , , |
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| Format: | Preprint |
| Published: |
2022
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| _version_ | 1866917721805946880 |
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| author | Wu, Chenghao Mailee, Hamila Montazeri, Zahra Ritschel, Tobias |
| author_facet | Wu, Chenghao Mailee, Hamila Montazeri, Zahra Ritschel, Tobias |
| contents | Differentiable rasterization changes the standard formulation of primitive rasterization -- by enabling gradient flow from a pixel to its underlying triangles -- using distribution functions in different stages of rendering, creating a "soft" version of the original rasterizer. However, choosing the optimal softening function that ensures the best performance and convergence to a desired goal requires trial and error. Previous work has analyzed and compared several combinations of softening. In this work, we take it a step further and, instead of making a combinatorial choice of softening operations, parameterize the continuous space of common softening operations. We study meta-learning tunable softness functions over a set of inverse rendering tasks (2D and 3D shape, pose and occlusion) so it generalizes to new and unseen differentiable rendering tasks with optimal softness. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2211_13333 |
| institution | arXiv |
| publishDate | 2022 |
| record_format | arxiv |
| spellingShingle | Learning to Rasterize Differentiably Wu, Chenghao Mailee, Hamila Montazeri, Zahra Ritschel, Tobias Graphics Computer Vision and Pattern Recognition Differentiable rasterization changes the standard formulation of primitive rasterization -- by enabling gradient flow from a pixel to its underlying triangles -- using distribution functions in different stages of rendering, creating a "soft" version of the original rasterizer. However, choosing the optimal softening function that ensures the best performance and convergence to a desired goal requires trial and error. Previous work has analyzed and compared several combinations of softening. In this work, we take it a step further and, instead of making a combinatorial choice of softening operations, parameterize the continuous space of common softening operations. We study meta-learning tunable softness functions over a set of inverse rendering tasks (2D and 3D shape, pose and occlusion) so it generalizes to new and unseen differentiable rendering tasks with optimal softness. |
| title | Learning to Rasterize Differentiably |
| topic | Graphics Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2211.13333 |