Dense Geometry Supervision for Underwater Depth Estimation
The paper "Dense Geometry Supervision for Underwater Depth Estimation" addresses the challenges of monocular depth estimation in underwater environments, which include limited data availability and the difficulty of adapting existing methods designed for terrestrial applications. The authors propose a novel, cost-effective approach that leverages multi-view depth estimation coupled with enhanced underwater images to generate supervisory signals. These signals serve as the foundation for constructing a suitable dataset, which facilitates the training of monocular depth estimation models for underwater use.
Monocular depth estimation traditionally relies on either supervised learning, which has been hampered by the lack of high-quality annotated depth data specific to underwater scenes, or unsupervised methods, which suffer from limitations related to occlusion and image quality variations. The authors tackle these challenges by creating a dataset using a multi-view stereo (MVS) technique applied to images synthesized and enhanced through neural radiance fields (NeRF). This process enhances depth accuracy in static underwater scenes, which are first carefully selected from video footage. The enhanced images and depth maps generated through MVS are subjected to post-processing that filters unreliable data based on confidence maps, ensuring only high-quality depth supervision.
The introduction of a texture-depth fusion module based on underwater optical imaging principles marks a significant innovation. This module exploits depth cues embedded in texture data from RGB images, enhancing the model's ability to distinguish between water and solid objects and thus improving depth estimation accuracy. By integrating features derived from the Underwater Light Attenuation Prior (ULAP) with enhanced images using Seathru algorithms, the module effectively decouples depth estimation from image quality inconsistencies inherent in dynamic underwater settings.
Experimentation on the FLSea dataset reveals that the proposed method significantly bolsters model performance in terms of accuracy and adaptability to underwater conditions. Various models, including NewCRFs, IEBins, AdaBins, and others, benefited from fine-tuning with the constructed dataset, showing marked improvement in standard depth estimation metrics. Moreover, incorporating the Depth-Texture Fusion Module further boosted performance across models, demonstrating its utility in refining depth predictions for complex underwater environments.
The implications of this research are multifaceted. Practically, the approach offers a cost-effective solution for deploying monocular depth estimation in operational underwater scenarios, such as those encountered by autonomous underwater vehicles (AUVs) and remotely operated vehicles (ROVs). Theoretically, it advances the understanding of underwater optical imaging, illustrating how texture-related information can be leveraged to improve depth estimation models. Future developments may explore further integration of dynamic scene analysis and the application of unsupervised methods augmented by deep learning frameworks, potentially extending this methodology's applicability to real-time underwater exploration and monitoring tasks.
This paper presents a promising advancement in underwater depth estimation, showcasing how innovative data construction and fusion techniques can overcome existing limitations while providing a robust foundation for further improvements in AI-driven underwater research and applications.