WaterNeRF: Neural Radiance Fields for Underwater Scenes
This work addresses the challenge of restoring underwater imagery for applications like marine robotics, aquaculture, and environmental monitoring, representing an incremental advance in adapting NeRF to domain-specific conditions.
The paper tackles the problem of underwater imaging degradation due to water column effects like attenuation and backscattering, which hinder tasks such as depth estimation and 3D reconstruction, by proposing WaterNeRF, a method that combines neural radiance fields with a physics-based model to enable dense depth estimation and color correction, achieving state-of-the-art results as evaluated on a real underwater dataset.
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 advance state-of-the-art in neural radiance fields (NeRFs) to enable physics-informed dense depth estimation and color correction. Our proposed method, WaterNeRF, estimates parameters of a physics-based model for underwater image formation, leading to a hybrid data-driven and model-based solution. After determining the scene structure and radiance field, we can produce novel views of degraded as well as corrected underwater images, along with dense depth of the scene. We evaluate the proposed method qualitatively and quantitatively on a real underwater dataset.