Event-based Photometric Stereo via Rotating Illumination and Per-Pixel Learning
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arXiv
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| Autori principali: | , , , , , |
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| Natura: | Preprint |
| Pubblicazione: |
2026
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| _version_ | 1866910062433271808 |
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| author | Kim, Hyunwoo Kim, Won-Hoe Lee, Sanghoon Cai, Jianfei Nam, Giljoo Hyun, Jae-Sang |
| author_facet | Kim, Hyunwoo Kim, Won-Hoe Lee, Sanghoon Cai, Jianfei Nam, Giljoo Hyun, Jae-Sang |
| contents | Photometric stereo is a technique for estimating surface normals using images captured under varying illumination. However, conventional frame-based photometric stereo methods are limited in real-world applications due to their reliance on controlled lighting, and susceptibility to ambient illumination. To address these limitations, we propose an event-based photometric stereo system that leverages an event camera, which is effective in scenarios with continuously varying scene radiance and high dynamic range conditions. Our setup employs a single light source moving along a predefined circular trajectory, eliminating the need for multiple synchronized light sources and enabling a more compact and scalable design. We further introduce a lightweight per-pixel multi-layer neural network that directly predicts surface normals from event signals generated by intensity changes as the light source rotates, without system calibration. Experimental results on benchmark datasets and real-world data collected with our data acquisition system demonstrate the effectiveness of our method, achieving a 7.12\% reduction in mean angular error compared to existing event-based photometric stereo methods. In addition, our method demonstrates robustness in regions with sparse event activity, strong ambient illumination, and scenes affected by specularities. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2603_10748 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | Event-based Photometric Stereo via Rotating Illumination and Per-Pixel Learning Kim, Hyunwoo Kim, Won-Hoe Lee, Sanghoon Cai, Jianfei Nam, Giljoo Hyun, Jae-Sang Computer Vision and Pattern Recognition Photometric stereo is a technique for estimating surface normals using images captured under varying illumination. However, conventional frame-based photometric stereo methods are limited in real-world applications due to their reliance on controlled lighting, and susceptibility to ambient illumination. To address these limitations, we propose an event-based photometric stereo system that leverages an event camera, which is effective in scenarios with continuously varying scene radiance and high dynamic range conditions. Our setup employs a single light source moving along a predefined circular trajectory, eliminating the need for multiple synchronized light sources and enabling a more compact and scalable design. We further introduce a lightweight per-pixel multi-layer neural network that directly predicts surface normals from event signals generated by intensity changes as the light source rotates, without system calibration. Experimental results on benchmark datasets and real-world data collected with our data acquisition system demonstrate the effectiveness of our method, achieving a 7.12\% reduction in mean angular error compared to existing event-based photometric stereo methods. In addition, our method demonstrates robustness in regions with sparse event activity, strong ambient illumination, and scenes affected by specularities. |
| title | Event-based Photometric Stereo via Rotating Illumination and Per-Pixel Learning |
| topic | Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2603.10748 |