ConvMatch: Rethinking Network Design for Two-View Correspondence Learning

Multilayer perceptron (MLP) has become the de facto backbone in two-view correspondence learning, for it can extract effective deep features from unordered correspondences individually. However, the problem of natively lacking context information limits its performance although many context-capturing modules are appended in the follow-up studies. In this paper, from a novel perspective, we design a correspondence learning network called ConvMatch that for the first time can leverage a convolutional neural network (CNN) as the backbone, inherently capable of context aggregation. Specifically, with the observation that sparse motion vectors and a dense motion field can be converted into each other with interpolating and sampling, we regularize the putative motion vectors by estimating the dense motion field implicitly, then rectify the errors caused by outliers in local areas with CNN, and finally obtain correct motion vectors from the rectified motion field. Moreover, we propose global information injection and bilateral convolution, to fit the overall spatial transformation better and accommodate the discontinuities of the motion field in case of large scene disparity. Extensive experiments reveal that ConvMatch consistently outperforms state-of-the-arts for relative pose estimation, homography estimation, and visual localization.