Photometric Stereo using Gaussian Splatting and inverse rendering
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arXiv
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| Main Authors: | , , |
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| Format: | Preprint |
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2025
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| _version_ | 1866918087324860416 |
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| author | Ducastel, Matéo Tschumperlé, David Quéau, Yvain |
| author_facet | Ducastel, Matéo Tschumperlé, David Quéau, Yvain |
| contents | Recent state-of-the-art algorithms in photometric stereo rely on neural networks and operate either through prior learning or inverse rendering optimization. Here, we revisit the problem of calibrated photometric stereo by leveraging recent advances in 3D inverse rendering using the Gaussian Splatting formalism. This allows us to parameterize the 3D scene to be reconstructed and optimize it in a more interpretable manner. Our approach incorporates a simplified model for light representation and demonstrates the potential of the Gaussian Splatting rendering engine for the photometric stereo problem. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2507_06684 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Photometric Stereo using Gaussian Splatting and inverse rendering Ducastel, Matéo Tschumperlé, David Quéau, Yvain Image and Video Processing Artificial Intelligence Recent state-of-the-art algorithms in photometric stereo rely on neural networks and operate either through prior learning or inverse rendering optimization. Here, we revisit the problem of calibrated photometric stereo by leveraging recent advances in 3D inverse rendering using the Gaussian Splatting formalism. This allows us to parameterize the 3D scene to be reconstructed and optimize it in a more interpretable manner. Our approach incorporates a simplified model for light representation and demonstrates the potential of the Gaussian Splatting rendering engine for the photometric stereo problem. |
| title | Photometric Stereo using Gaussian Splatting and inverse rendering |
| topic | Image and Video Processing Artificial Intelligence |
| url | https://arxiv.org/abs/2507.06684 |