WaterHE-NeRF: Water-ray Tracing Neural Radiance Fields for Underwater Scene Reconstruction
This work addresses underwater scene reconstruction for applications like marine exploration, but it is incremental as it builds on existing NeRF methods with specific enhancements for water medium effects.
The paper tackles the problem of underwater scene reconstruction by addressing light attenuation and lack of ground truth supervision in Neural Radiance Fields (NeRF), proposing WaterHE-NeRF which achieves improved performance on real and synthetic datasets.
Neural Radiance Field (NeRF) technology demonstrates immense potential in novel viewpoint synthesis tasks, due to its physics-based volumetric rendering process, which is particularly promising in underwater scenes. Addressing the limitations of existing underwater NeRF methods in handling light attenuation caused 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 by Retinex theory that precisely encodes color, density, and illuminance attenuation in three-dimensional space. WaterHE-NeRF, through its illuminance attenuation mechanism, generates both degraded and clear multi-view images and optimizes image restoration by combining reconstruction loss with Wasserstein distance. Additionally, the use of histogram equalization (HE) as pseudo-GT enhances the network's accuracy in preserving original details and color distribution. Extensive experiments on real underwater datasets and synthetic datasets validate the effectiveness of WaterHE-NeRF. Our code will be made publicly available.