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| Main Authors: | , , , |
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| Format: | Preprint |
| Published: |
2026
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2605.06214 |
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| _version_ | 1866911657332047872 |
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| author | Ding, Huakeng Chen, Yaowen Zhou, Kun Wu, Hongzhi |
| author_facet | Ding, Huakeng Chen, Yaowen Zhou, Kun Wu, Hongzhi |
| contents | We present a differentiable framework to adaptively compute 4D illumination conditions with respect to an object, for efficient, high-quality simultaneous acquisition of its shape and reflectance, with a unified spatial-angular structured light and a single camera. Using a simple histogram-based pixel-level probability model for depth and reflectance, we differentiably link the next illumination condition(s) with a loss that encourages the reduction in depth uncertainty. As new structured illumination is cast, corresponding image measurements are used to update the uncertainty at each pixel. Finally, a fine-tuning-based approach reconstructs the depth map and reflectance parameter maps, by minimizing the differences between all physical measurements and their simulated counterparts. The effectiveness of our framework is demonstrated on physical objects with wide variations in shape and appearance. Our depth results compare favorably with state-of-the-art techniques, while our reflectance results are comparable when validated against photographs. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2605_06214 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | Differentiable Adaptive 4D Structured Illumination for Joint Capture of Shape and Reflectance Ding, Huakeng Chen, Yaowen Zhou, Kun Wu, Hongzhi Computer Vision and Pattern Recognition We present a differentiable framework to adaptively compute 4D illumination conditions with respect to an object, for efficient, high-quality simultaneous acquisition of its shape and reflectance, with a unified spatial-angular structured light and a single camera. Using a simple histogram-based pixel-level probability model for depth and reflectance, we differentiably link the next illumination condition(s) with a loss that encourages the reduction in depth uncertainty. As new structured illumination is cast, corresponding image measurements are used to update the uncertainty at each pixel. Finally, a fine-tuning-based approach reconstructs the depth map and reflectance parameter maps, by minimizing the differences between all physical measurements and their simulated counterparts. The effectiveness of our framework is demonstrated on physical objects with wide variations in shape and appearance. Our depth results compare favorably with state-of-the-art techniques, while our reflectance results are comparable when validated against photographs. |
| title | Differentiable Adaptive 4D Structured Illumination for Joint Capture of Shape and Reflectance |
| topic | Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2605.06214 |