WaterMono: Teacher-Guided Anomaly Masking and Enhancement Boosting for Robust Underwater Self-Supervised Monocular Depth Estimation

TL;DR

WaterMono is a novel framework that improves underwater monocular depth estimation by integrating teacher-guided anomaly masking, image enhancement based on underwater image models, and a rotated distillation strategy to handle dynamic scenes and diverse camera angles.

Contribution

It introduces a comprehensive approach combining anomaly detection, image enhancement, and rotational robustness to advance underwater depth estimation in self-supervised settings.

Findings

Effective depth estimation in challenging underwater scenarios

Enhanced image quality through underwater image formation modeling

Improved rotational robustness of depth prediction models

Abstract

Depth information serves as a crucial prerequisite for various visual tasks, whether on land or underwater. Recently, self-supervised methods have achieved remarkable performance on several terrestrial benchmarks despite the absence of depth annotations. However, in more challenging underwater scenarios, they encounter numerous brand-new obstacles such as the influence of marine life and degradation of underwater images, which break the assumption of a static scene and bring low-quality images, respectively. Besides, the camera angles of underwater images are more diverse. Fortunately, we have discovered that knowledge distillation presents a promising approach for tackling these challenges. In this paper, we propose WaterMono, a novel framework for depth estimation coupled with image enhancement. It incorporates the following key measures: (1) We present a Teacher-Guided Anomaly Mask to identify dynamic regions within the images; (2) We employ depth information combined with the Underwater Image Formation Model to generate enhanced images, which in turn contribute to the depth estimation task; and (3) We utilize a rotated distillation strategy to enhance the model's rotational robustness. Comprehensive experiments demonstrate the effectiveness of our proposed method for both depth estimation and image enhancement. The source code and pre-trained models are available on the project home page: https://github.com/OUCVisionGroup/WaterMono.