Tree-Mamba: A Tree-Aware Mamba for Underwater Monocular Depth Estimation

TL;DR

Tree-Mamba introduces a novel tree-aware scanning strategy for underwater monocular depth estimation, leveraging a new dataset and outperforming existing methods in accuracy and efficiency.

Contribution

The paper proposes a tree-aware Mamba model with an adaptive spanning tree strategy and introduces the BlueDepth dataset with reliable labels for improved underwater depth estimation.

Findings

Tree-Mamba outperforms existing methods in qualitative and quantitative evaluations.

The new dataset BlueDepth provides reliable depth labels for training and benchmarking.

The proposed method achieves high accuracy with competitive computational efficiency.

Abstract

Underwater Monocular Depth Estimation (UMDE) is a critical task that aims to estimate high-precision depth maps from underwater degraded images caused by light absorption and scattering effects in marine environments. Recently, Mamba-based methods have achieved promising performance across various vision tasks; however, they struggle with the UMDE task because their inflexible state scanning strategies fail to model the structural features of underwater images effectively. Meanwhile, existing UMDE datasets usually contain unreliable depth labels, leading to incorrect object-depth relationships between underwater images and their corresponding depth maps. To overcome these limitations, we develop a novel tree-aware Mamba method, dubbed Tree-Mamba, for estimating accurate monocular depth maps from underwater degraded images. Specifically, we propose a tree-aware scanning strategy that adaptively constructs a minimum spanning tree based on feature similarity. The spatial topological features among the tree nodes are then flexibly aggregated through bottom-up and top-down traversals, enabling stronger multi-scale feature representation capabilities. Moreover, we construct an underwater depth estimation benchmark (called BlueDepth), which consists of 38,162 underwater image pairs with reliable depth labels. This benchmark serves as a foundational dataset for training existing deep learning-based UMDE methods to learn accurate object-depth relationships. Extensive experiments demonstrate the superiority of the proposed Tree-Mamba over several leading methods in both qualitative results and quantitative evaluations with competitive computational efficiency. Code and dataset will be available at https://wyjgr.github.io/Tree-Mamba.html.