In the fields of computer vision and robotics, accurate pixel-level correspondences are essential for enabling advanced tasks such as structure-from-motion and simultaneous localization and mapping. Recent correspondence pruning methods usually focus on learning local consistency through k-nearest neighbors, which makes it difficult to capture robust context for each correspondence. We propose CorrAdaptor, a novel architecture that introduces a dual-branch structure capable of adaptively adjusting local contexts through both explicit and implicit local graph learning. Specifically, the explicit branch uses KNN-based graphs tailored for initial neighborhood identification, while the implicit branch leverages a learnable matrix to softly assign neighbors and adaptively expand the local context scope, significantly enhancing the model's robustness and adaptability to complex image variations. Moreover, we design a motion injection module to integrate motion consistency into the network to suppress the impact of outliers and refine local context learning, resulting in substantial performance improvements. The experimental results on extensive correspondence-based tasks indicate that our CorrAdaptor achieves state-of-the-art performance both qualitatively and quantitatively. The code and pre-trained models are available at https://github.com/TaoWangzj/CorrAdaptor.

翻译：在计算机视觉与机器人学领域，精确的像素级对应关系是实现运动恢复结构与同步定位与建图等高级任务的关键。现有的对应关系剪枝方法通常侧重于通过k近邻学习局部一致性，这难以捕获每个对应关系的鲁棒上下文。本文提出CorrAdaptor，一种新颖的双分支架构，能够通过显式与隐式的局部图学习自适应地调整局部上下文。具体而言，显式分支采用基于KNN的图结构进行初始邻域识别，而隐式分支则利用可学习矩阵实现软邻域分配并自适应扩展局部上下文范围，显著增强了模型对复杂图像变化的鲁棒性与适应性。此外，我们设计了运动注入模块，将运动一致性整合到网络中，以抑制异常值的影响并优化局部上下文学习，从而获得显著的性能提升。在大量基于对应关系的任务上的实验结果表明，CorrAdaptor在定性与定量评估中均达到了最先进的性能。代码与预训练模型公开于https://github.com/TaoWangzj/CorrAdaptor。