Learning robust local image feature matching is a fundamental low-level vision task, which has been widely explored in the past few years. Recently, detector-free local feature matchers based on transformers have shown promising results, which largely outperform pure Convolutional Neural Network (CNN) based ones. But correlations produced by transformer-based methods are spatially limited to the center of source views' coarse patches, because of the costly attention learning. In this work, we rethink this issue and find that such matching formulation degrades pose estimation, especially for low-resolution images. So we propose a transformer-based cascade matching model -- Cascade feature Matching TRansformer (CasMTR), to efficiently learn dense feature correlations, which allows us to choose more reliable matching pairs for the relative pose estimation. Instead of re-training a new detector, we use a simple yet effective Non-Maximum Suppression (NMS) post-process to filter keypoints through the confidence map, and largely improve the matching precision. CasMTR achieves state-of-the-art performance in indoor and outdoor pose estimation as well as visual localization. Moreover, thorough ablations show the efficacy of the proposed components and techniques.

翻译：学习鲁棒的局部图像特征匹配是一项基础的低级视觉任务，近年来得到了广泛探索。近期，基于Transformer的无检测器局部特征匹配器展现出良好前景，其性能大幅优于纯卷积神经网络（CNN）方法。然而，由于注意力学习成本高昂，基于Transformer的方法产生的相关性在空间上局限于源视图粗粒度块的中心区域。本文重新审视该问题，发现这种匹配公式会降低姿态估计性能，尤其对于低分辨率图像。为此，我们提出一种基于Transformer的级联匹配模型——级联特征匹配Transformer（CasMTR），以高效学习密集特征相关性，从而能够为相对姿态估计选择更可靠的匹配对。不同于重新训练新检测器，我们采用简单而有效的非极大值抑制（NMS）后处理，通过置信度图过滤关键点，并大幅提升匹配精度。CasMTR在室内外姿态估计以及视觉定位任务中均实现了最先进的性能。此外，充分的消融实验证明了所提出组件及技术的有效性。