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Neural Radiance Field (NeRF) technology demonstrates immense potential innovel viewpoint synthesis tasks, due to its physics-based volumetric renderingprocess, which is particularly promising in underwater scenes. Addressing thelimitations of existing underwater NeRF methods in handling light attenuationcaused by the water medium and the lack of real Ground Truth (GT) supervision,this study proposes WaterHE-NeRF. We develop a new water-ray tracing field byRetinex theory that precisely encodes color, density, and illuminanceattenuation in three-dimensional space. WaterHE-NeRF, through its illuminanceattenuation mechanism, generates both degraded and clear multi-view images andoptimizes image restoration by combining reconstruction loss with Wassersteindistance. Additionally, the use of histogram equalization (HE) as pseudo-GTenhances the network's accuracy in preserving original details and colordistribution. Extensive experiments on real underwater datasets and syntheticdatasets validate the effectiveness of WaterHE-NeRF. Our code will be madepublicly available.

WaterHE-NeRF: Water-ray Tracing Neural Radiance Fields for Underwater Scene Reconstruction