StereoAdapter-2: Globally Structure-Consistent Underwater Stereo Depth Estimation

TL;DR

StereoAdapter-2 introduces a novel operator for underwater stereo depth estimation that captures long-range spatial structure efficiently, significantly improving zero-shot performance on underwater benchmarks and demonstrating robustness in real-world tests.

Contribution

It proposes a new ConvSS2D operator with a four-directional scanning strategy for better long-range disparity propagation in underwater stereo depth estimation.

Findings

Achieves 17% improvement on TartanAir-UW benchmark.

Attains 7.2% improvement on SQUID benchmark.

Demonstrates robustness in real-world underwater robot tests.

Abstract

Stereo depth estimation is fundamental to underwater robotic perception, yet suffers from severe domain shifts caused by wavelength-dependent light attenuation, scattering, and refraction. Recent approaches leverage monocular foundation models with GRU-based iterative refinement for underwater adaptation; however, the sequential gating and local convolutional kernels in GRUs necessitate multiple iterations for long-range disparity propagation, limiting performance in large-disparity and textureless underwater regions. In this paper, we propose StereoAdapter-2, which replaces the conventional ConvGRU updater with a novel ConvSS2D operator based on selective state space models. The proposed operator employs a four-directional scanning strategy that naturally aligns with epipolar geometry while capturing vertical structural consistency, enabling efficient long-range spatial propagation within a single update step at linear computational complexity. Furthermore, we construct UW-StereoDepth-80K, a large-scale synthetic underwater stereo dataset featuring diverse baselines, attenuation coefficients, and scattering parameters through a two-stage generative pipeline combining semantic-aware style transfer and geometry-consistent novel view synthesis. Combined with dynamic LoRA adaptation inherited from StereoAdapter, our framework achieves state-of-the-art zero-shot performance on underwater benchmarks with 17% improvement on TartanAir-UW and 7.2% improvment on SQUID, with real-world validation on the BlueROV2 platform demonstrates the robustness of our approach. Code: https://github.com/AIGeeksGroup/StereoAdapter-2. Website: https://aigeeksgroup.github.io/StereoAdapter-2.