Stereo Depth Estimation in real-world environments poses significant challenges due to dynamic domain shifts, sparse or unreliable supervision, and the high cost of acquiring dense ground-truth labels. While recent Test-Time Adaptation (TTA) methods offer promising solutions, most rely on static target domain assumptions and input-invariant adaptation strategies, limiting their effectiveness under continual shifts. In this paper, we propose RobIA, a novel Robust, Instance-Aware framework for Continual Test-Time Adaptation (CTTA) in stereo depth estimation. RobIA integrates two key components: (1) Attend-and-Excite Mixture-of-Experts (AttEx-MoE), a parameter-efficient module that dynamically routes input to frozen experts via lightweight self-attention mechanism tailored to epipolar geometry, and (2) Robust AdaptBN Teacher, a PEFT-based teacher model that provides dense pseudo-supervision by complementing sparse handcrafted labels. This strategy enables input-specific flexibility, broad supervision coverage, improving generalization under domain shift. Extensive experiments demonstrate that RobIA achieves superior adaptation performance across dynamic target domains while maintaining computational efficiency.

翻译：真实环境中的立体深度估计面临动态域偏移、监督信号稀疏或不可靠以及获取密集真值标签成本高昂等重大挑战。尽管近期测试时自适应方法提供了有前景的解决方案，但多数方法依赖静态目标域假设和输入不变的适应策略，限制了其在持续偏移下的有效性。本文提出RobIA——一种面向立体深度估计的鲁棒实例感知持续测试时自适应框架。RobIA整合了两个核心组件：（1）Attend-and-Excite混合专家模块，该参数高效模块通过轻量级自注意力机制动态路由输入至冻结的专家网络，该机制专为极线几何定制；（2）鲁棒AdaptBN教师模型，该基于参数高效微调的教师模型通过补充稀疏手工标注来提供密集伪监督。该策略实现了输入特定的灵活性及广泛的监督覆盖，提升了域偏移下的泛化能力。大量实验表明，RobIA在动态目标域中实现了卓越的自适应性能，同时保持了计算效率。