FC-GNN: Recovering Reliable and Accurate Correspondences from Interferences
Authors: Haobo Xu, Jun Zhou, Hua Yang, Renjie Pan, Cunyan Li

Finding correspondences between images is essential for many computer vision tasks and sparse matching pipelines have been popular for decades. However matching noise within and between images along with inconsistent keypoint detection frequently degrades the matching performance. We review these problems and thus propose: 1) a novel and unified Filtering and Calibrating (FC) approach that jointly rejects outliers and optimizes inliers and 2) leveraging both the matching context and the underlying image texture to remove matching uncertainties. Under the guidance of the above innovations we construct Filtering and Calibrating Graph Neural Network (FC-GNN) which follows the FC approach to recover reliable and accurate correspondences from various interferences. FC-GNN conducts an effectively combined inference of contextual and local information through careful embedding and multiple information aggregations predicting confidence scores and calibration offsets for the input correspondences to jointly filter out outliers and improve pixel-level matching accuracy. Moreover we exploit the local coherence of matches to perform inference on local graphs thereby reducing computational complexity. Overall FC-GNN operates at lightning speed and can greatly boost the performance of diverse matching pipelines across various tasks showcasing the immense potential of such approaches to become standard and pivotal components of image matching. Code is avaiable at https://github.com/xuy123456/fcgnn.