Recovered surface normals and 3D shape of the swimming fish
In this paper, we introduce a novel method for reconstructing surface normals and depth of dynamic objects in water. Past shape recovery methods have leveraged various visual cues for estimating shape (e.g., depth) or surface normals. Methods that estimate both compute one from the other. We show that these two geometric surface properties can be simultaneously recovered for each pixel when the object is observed underwater. Our key idea is to leverage multi-wavelength near-infrared light absorption along different underwater light paths in conjunction with surface shading. Our method can handle both Lambertian and non-Lambertian surfaces. We derive a principled theory for this surface normals and shape from water method and a practical calibration method for determining its imaging parameters values. By construction, the method can be implemented as a one-shot imaging system. We prototype both an off-line and a video-rate imaging system and demonstrate the effectiveness of the method on a number of real-world static and dynamic objects. The results show that the method can recover intricate surface features that are otherwise inaccessible.
If you found this work useful, please consider citing us:
@article{kuo2021surface,
title={Surface normals and shape from water},
author={Kuo, Meng-Yu Jennifer and Murai, Satoshi and Kawahara, Ryo and Nobuhara, Shohei and Nishino, Ko},
journal={IEEE Transactions on Pattern Analysis and Machine Intelligence},
volume={44},
number={12},
pages={9150--9162},
year={2021},
publisher={IEEE}
}
}
This work was in part supported by JSPS KAKENHI 15H05918, 17K20143, 18K19815, 26240023, and 20H05951, and JST grant number JPMJCR20G7.