Near-Light Color Photometric Stereo for Mono-Chromatic Non-Lambertian Surfaces
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arXiv
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| Autores principales: | , , , , , , , , |
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| Formato: | Preprint |
| Publicado: |
2026
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| _version_ | 1866911422131208192 |
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| author | Li, Zonglin Ren, Jieji Zhou, Shuangfan Guo, Heng Zhang, Jinnuo Zhou, Jiang Shi, Boxin Ma, Zhanyu Gu, Guoying |
| author_facet | Li, Zonglin Ren, Jieji Zhou, Shuangfan Guo, Heng Zhang, Jinnuo Zhou, Jiang Shi, Boxin Ma, Zhanyu Gu, Guoying |
| contents | Color photometric stereo enables single-shot surface reconstruction, extending conventional photometric stereo that requires multiple images of a static scene under varying illumination to dynamic scenarios. However, most existing approaches assume ideal distant lighting and Lambertian reflectance, leaving more practical near-light conditions and non-Lambertian surfaces underexplored. To overcome this limitation, we propose a framework that leverages neural implicit representations for depth and BRDF modeling under the assumption of mono-chromaticity (uniform chromaticity and homogeneous material), which alleviates the inherent ill-posedness of color photometric stereo and allows for detailed surface recovery from just one image. Furthermore, we design a compact optical tactile sensor to validate our approach. Experiments on both synthetic and real-world datasets demonstrate that our method achieves accurate and robust surface reconstruction. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2601_12666 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | Near-Light Color Photometric Stereo for Mono-Chromatic Non-Lambertian Surfaces Li, Zonglin Ren, Jieji Zhou, Shuangfan Guo, Heng Zhang, Jinnuo Zhou, Jiang Shi, Boxin Ma, Zhanyu Gu, Guoying Computer Vision and Pattern Recognition Color photometric stereo enables single-shot surface reconstruction, extending conventional photometric stereo that requires multiple images of a static scene under varying illumination to dynamic scenarios. However, most existing approaches assume ideal distant lighting and Lambertian reflectance, leaving more practical near-light conditions and non-Lambertian surfaces underexplored. To overcome this limitation, we propose a framework that leverages neural implicit representations for depth and BRDF modeling under the assumption of mono-chromaticity (uniform chromaticity and homogeneous material), which alleviates the inherent ill-posedness of color photometric stereo and allows for detailed surface recovery from just one image. Furthermore, we design a compact optical tactile sensor to validate our approach. Experiments on both synthetic and real-world datasets demonstrate that our method achieves accurate and robust surface reconstruction. |
| title | Near-Light Color Photometric Stereo for Mono-Chromatic Non-Lambertian Surfaces |
| topic | Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2601.12666 |