提出新模型提升水下立体深度估计，显著改善大视差与无纹理区域表现。
StereoAdapter-2: Globally Structure-Consistent Underwater Stereo Depth Estimation
- 用新型ConvSS2D替代传统GRU，单步完成长距离视差传播。
- 在TartanAir-UW和SQUID上分别提升17%和7.2%，实现最优零样本性能。
- 适用于水下机器人感知，尤其适合复杂光照与结构缺失场景。
立体深度估计对水下机器人感知至关重要，但受波长依赖的光衰减、散射和折射影响，存在严重领域偏移。现有方法利用基于GRU的单目基础模型进行迭代优化，但其序列门控和局部卷积核需多轮迭代才能实现远距离视差传播，限制了在大视差与无纹理区域的表现。本文提出StereoAdapter-2，将传统ConvGRU更新器替换为基于选择性状态空间模型的新型ConvSS2D算子。该算子采用四方向扫描策略，天然契合视极几何并捕捉垂直结构一致性，可在单次更新中以线性计算复杂度实现高效长程空间传播。此外，我们构建了大型合成水下立体数据集UW-StereoDepth-80K，通过两阶段生成流程结合语义感知风格迁移与几何一致的新视角合成，涵盖多样基线、衰减系数与散射参数。结合来自StereoAdapter的动态LoRA适配机制，本框架在水下基准上实现最先进零样本性能，在TartanAir-UW上提升17%，在SQUID上提升7.2%，蓝鳍无人机平台实测验证了方法鲁棒性。
原文摘要 · Abstract (English)
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.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。