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. However, existing underwater NeRF methods face challenges in handling light attenuation caused by the water medium and the lack of real Ground Truth (GT) supervision. To address these issues, we propose WaterHE-NeRF, a novel approach incorporating a water-ray matching field developed based on Retinex theory. This field precisely encodes color, density, and illuminance attenuation in three-dimensional space. WaterHE-NeRF employs an illuminance attenuation mechanism to generate degraded and clear multi-view images, optimizing image restoration by combining reconstruction loss with Wasserstein distance. Furthermore, using histogram equalization (HE) as pseudo-GT, WaterHE-NeRF enhances the network’s accuracy in preserving original details and color distribution. Extensive experiments on real underwater and synthetic datasets validate the effectiveness of WaterHE-NeRF.

中文翻译：

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WaterHE-NeRF：用于水下场景重建的水射线匹配神经辐射场


由于其基于物理的体积渲染过程，神经辐射场 （NeRF） 技术在新颖的视点合成任务中表现出巨大的潜力，这在水下场景中尤其有前途。然而，现有的水下 NeRF 方法在处理由水介质引起的光衰减和缺乏真正的地面实况 （GT） 监督方面面临挑战。为了解决这些问题，我们提出了 WaterHE-NeRF，这是一种基于 Retinex 理论开发的结合水射线匹配场的新方法。此场对 3D 空间中的颜色、密度和照度衰减进行精确编码。WaterHE-NeRF 采用照度衰减机制生成降级和清晰的多视图图像，通过将重建损失与 Wasserstein 距离相结合来优化图像恢复。此外，使用直方图均衡 （HE） 作为伪 GT，WaterHE-NeRF 提高了网络在保留原始细节和颜色分布方面的准确性。对真实水下和合成数据集的广泛实验验证了 WaterHE-NeRF 的有效性。