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.

翻译：神经辐射场（NeRF）技术在新型视角合成任务中展现出巨大潜力，其基于物理的体渲染过程尤其适用于水下场景。针对现有水下NeRF方法在处理水介质引起的光衰减方面的局限性以及缺乏真实地面真值（GT）监督的问题，本研究提出WaterHE-NeRF。我们基于Retinex理论开发了一种新的水射线追踪场，可在三维空间中精确编码颜色、密度和照度衰减。WaterHE-NeRF通过其照度衰减机制生成退化与清晰的多视角图像，并结合重建损失与Wasserstein距离优化图像复原。此外，利用直方图均衡化（HE）作为伪GT，增强了网络在保留原始细节和颜色分布方面的准确性。在真实水下数据集与合成数据集上的广泛实验验证了WaterHE-NeRF的有效性。我们的代码将公开发布。