WaterNeRF: Neural Radiance Fields for Underwater Scenes

Abstract

Underwater imaging is a critical task performed by marine robots for a wide range of applications including aquaculture, marine infrastructure inspection, and environmental monitoring. However, water column effects, such as attenuation and backscattering, drastically change the color and quality of imagery captured underwater. Due to varying water conditions and range-dependency of these effects, restoring underwater imagery is a challenging problem. This impacts downstream perception tasks including depth estimation and 3D reconstruction. In this paper, we leverage state-of-the-art neural radiance fields (NeRFs) to enable physics-informed novel view synthesis with image restoration and dense depth estimation for underwater scenes. Our proposed method, WaterNeRF, estimates parameters of a physics-based model for underwater image formation and uses these parameters for novel view synthesis. After learning the scene structure and radiance field, we can produce novel views of degraded as well as corrected underwater images. We evaluate the proposed method qualitatively and quantitatively on a real underwater dataset.