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arXiv 2602.16915cs.CV

StereoAdapter-2: 基于全局结构一致性的水下立体深度估计

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

The University of Melbourne(墨尔本大学);Peking University(北京大学);Australian Centre for Robotics(澳大利亚机器人中心)

Zeyu Ren, Xiang Li, Yiran Wang, Zeyu Zhang, Hao Tang

AI总结:

StereoAdapter-2通过引入基于选择性状态空间模型的ConvSS2D操作符,实现了高效的水下立体深度估计,提升了水下基准测试的性能。

摘要:

立体深度估计是水下机器人感知的基础,但受到波长依赖性光衰减、散射和折射等因素的影响,存在严重的领域偏移问题。近期的方法利用单目基础模型结合基于GRU的迭代精炼进行水下适应;然而,GRU中的顺序门控和局部卷积核需要多次迭代才能实现长距离视差传播,限制了在大视差和纹理缺失的水下区域的性能。在本文中,我们提出了StereoAdapter-2,该方法将传统的ConvGRU更新器替换为基于选择性状态空间模型的新型ConvSS2D操作符。所提出的操作符采用四方向扫描策略,自然与视差几何对齐,同时捕捉垂直结构一致性,使得在单次更新步骤中以线性计算复杂度实现高效的长距离空间传播。此外,我们构建了UW-StereoDepth-80K,一个大规模的合成水下立体数据集,通过结合语义感知的风格迁移和几何一致的新视角合成的两阶段生成管道,涵盖了多样化的基线、衰减系数和散射参数。结合来自StereoAdapter的动态LoRA适应,我们的框架在水下基准测试中实现了最先进的零样本性能,其在TartanAir-UW上提升了17%,在SQUID上提升了7.2%,实际世界验证在BlueROV2平台上展示了方法的鲁棒性。代码:https://github.com/AIGeeksGroup/StereoAdapter-2。网站:https://aigeeksgroup.github.io/StereoAdapter-2。

英文摘要:

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.

机构、总结、中文摘要均由AI分析而来,可能存在错误,请以论文正文为准。

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