Neural rendering of underwater scenes under flat refractive geometry

Ting Bai; Xiaoqiang Zhang; Caiyu Xiong; Zhixin Zhang; Lingyan Ran; Hongyu Chu; Hu Deng

doi:10.1117/12.3048562

5 December 2024 Neural rendering of underwater scenes under flat refractive geometry

Ting Bai, Xiaoqiang Zhang, Caiyu Xiong, Zhixin Zhang, Lingyan Ran, Hongyu Chu, Hu Deng

Proceedings Volume 13418, Fifteenth International Conference on Information Optics and Photonics (CIOP 2024); 134182G (2024) https://doi.org/10.1117/12.3048562
Event: 15th International Conference on Information Optics and Photonics (CIOP2024), 2024, Xi’an, China

Abstract

As a promising technique, the Neural Radiance Fields (NeRF) and neural rendering are now widely applied in novel view synthesis and scene reconstruction. In the vanilla NeRF and subsequent neural rendering methods, one important assumption of the scene is that there is only one type of light medium in the scene, hence the light rays in such methods would remain straight during rendering. However, for underwater scenes, the camera is usually placed in a waterproofing and transplant housing. The light ray path in such a scenario would be air-water or air-housing material-water, which would cause refraction and violate the basic assumptions in vanilla NeRF. To address the issue of novel view synthesis in scenes with refractive media, this paper proposes a refractive neural rendering method under flat refractive geometry. First, the distance from the origin of the light ray to the refraction plane and the normal vector are precalibrated, which is utilized to model the per-pixel refracted ray direction, and the intersection point of the refraction plane. With the refracted rays, a neural radiance field can be trained and can be used for novel view synthesis in refractive scenes. The method is validated on synthetic and real data, revealing accurate novel view synthesis of scenes under refractive surfaces from sparse multi-view images.

(2024) Published by SPIE. Downloading of the abstract is permitted for personal use only.

Citation Download Citation

Ting Bai, Xiaoqiang Zhang, Caiyu Xiong, Zhixin Zhang, Lingyan Ran, Hongyu Chu, and Hu Deng "Neural rendering of underwater scenes under flat refractive geometry", Proc. SPIE 13418, Fifteenth International Conference on Information Optics and Photonics (CIOP 2024), 134182G (5 December 2024); https://doi.org/10.1117/12.3048562

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KEYWORDS

Refraction

Cameras

Calibration

Interfaces

Education and training

Water

Machine learning

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