Differentiable Mobile Display Photometric Stereo
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arXiv
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| Main Authors: | , , , , |
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| Format: | Preprint |
| Published: |
2025
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| _version_ | 1866917916273803264 |
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| author | Ban, Gawoon Kim, Hyeongjun Choi, Seokjun Yoon, Seungwoo Baek, Seung-Hwan |
| author_facet | Ban, Gawoon Kim, Hyeongjun Choi, Seokjun Yoon, Seungwoo Baek, Seung-Hwan |
| contents | Display photometric stereo uses a display as a programmable light source to illuminate a scene with diverse illumination conditions. Recently, differentiable display photometric stereo (DDPS) demonstrated improved normal reconstruction accuracy by using learned display patterns. However, DDPS faced limitations in practicality, requiring a fixed desktop imaging setup using a polarization camera and a desktop-scale monitor. In this paper, we propose a more practical physics-based photometric stereo, differentiable mobile display photometric stereo (DMDPS), that leverages a mobile phone consisting of a display and a camera. We overcome the limitations of using a mobile device by developing a mobile app and method that simultaneously displays patterns and captures high-quality HDR images. Using this technique, we capture real-world 3D-printed objects and learn display patterns via a differentiable learning process. We demonstrate the effectiveness of DMDPS on both a 3D printed dataset and a first dataset of fallen leaves. The leaf dataset contains reconstructed surface normals and albedos of fallen leaves that may enable future research beyond computer graphics and vision. We believe that DMDPS takes a step forward for practical physics-based photometric stereo. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2502_05055 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Differentiable Mobile Display Photometric Stereo Ban, Gawoon Kim, Hyeongjun Choi, Seokjun Yoon, Seungwoo Baek, Seung-Hwan Computer Vision and Pattern Recognition Artificial Intelligence Graphics Machine Learning Display photometric stereo uses a display as a programmable light source to illuminate a scene with diverse illumination conditions. Recently, differentiable display photometric stereo (DDPS) demonstrated improved normal reconstruction accuracy by using learned display patterns. However, DDPS faced limitations in practicality, requiring a fixed desktop imaging setup using a polarization camera and a desktop-scale monitor. In this paper, we propose a more practical physics-based photometric stereo, differentiable mobile display photometric stereo (DMDPS), that leverages a mobile phone consisting of a display and a camera. We overcome the limitations of using a mobile device by developing a mobile app and method that simultaneously displays patterns and captures high-quality HDR images. Using this technique, we capture real-world 3D-printed objects and learn display patterns via a differentiable learning process. We demonstrate the effectiveness of DMDPS on both a 3D printed dataset and a first dataset of fallen leaves. The leaf dataset contains reconstructed surface normals and albedos of fallen leaves that may enable future research beyond computer graphics and vision. We believe that DMDPS takes a step forward for practical physics-based photometric stereo. |
| title | Differentiable Mobile Display Photometric Stereo |
| topic | Computer Vision and Pattern Recognition Artificial Intelligence Graphics Machine Learning |
| url | https://arxiv.org/abs/2502.05055 |