RobuSTereo：恶劣天气下鲁棒的零样本立体匹配
RobuSTereo: Robust Zero-Shot Stereo Matching under Adverse Weather
AI总结：
RobuSTereo通过扩散仿真与立体一致性模块生成恶劣天气立体数据，并结合ConvNet与去噪Transformer的鲁棒特征编码器，显著提升立体匹配模型的零样本泛化能力。
摘要：
基于学习的立体匹配模型在恶劣天气条件下表现不佳，原因在于相应训练数据稀缺，以及从退化图像中提取判别性特征面临挑战。这些局限性严重阻碍了模型对分布外天气条件的零样本泛化。在本文中，我们提出RobuSTereo，这是一个新颖的框架，通过同时解决数据稀缺和特征提取挑战，增强立体匹配模型在恶劣天气下的零样本泛化能力。首先，我们引入了一个基于扩散的仿真流程，并配备立体一致性模块，该模块能够生成针对恶劣条件定制的高质量立体数据。通过在我们的合成数据集上训练立体匹配模型，我们缩小了清晰图像与退化图像之间的域差距，显著提高了模型对未见天气条件的鲁棒性。立体一致性模块确保合成图像对之间的结构对齐，保持几何完整性并提升深度估计精度。其次，我们设计了一个鲁棒的特征编码器，将专用ConvNet与去噪Transformer相结合，从退化图像中提取稳定可靠的特征。ConvNet捕获细粒度的局部结构，而去噪Transformer细化全局表示，有效减轻噪声、低能见度和天气引起的失真的影响。这使得即使在具有挑战性的视觉条件下也能实现更准确的视差估计。大量实验表明，RobuSTereo显著提高了立体匹配模型在各种恶劣天气场景下的鲁棒性和泛化能力。
英文摘要：
Learning-based stereo matching models struggle in adverse weather conditions due to the scarcity of corresponding training data and the challenges in extracting discriminative features from degraded images. These limitations significantly hinder zero-shot generalization to out-of-distribution weather conditions. In this paper, we propose \textbf{RobuSTereo}, a novel framework that enhances the zero-shot generalization of stereo matching models under adverse weather by addressing both data scarcity and feature extraction challenges. First, we introduce a diffusion-based simulation pipeline with a stereo consistency module, which generates high-quality stereo data tailored for adverse conditions. By training stereo matching models on our synthetic datasets, we reduce the domain gap between clean and degraded images, significantly improving the models' robustness to unseen weather conditions. The stereo consistency module ensures structural alignment across synthesized image pairs, preserving geometric integrity and enhancing depth estimation accuracy. Second, we design a robust feature encoder that combines a specialized ConvNet with a denoising transformer to extract stable and reliable features from degraded images. The ConvNet captures fine-grained local structures, while the denoising transformer refines global representations, effectively mitigating the impact of noise, low visibility, and weather-induced distortions. This enables more accurate disparity estimation even under challenging visual conditions. Extensive experiments demonstrate that \textbf{RobuSTereo} significantly improves the robustness and generalization of stereo matching models across diverse adverse weather scenarios.
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。
