Exploring reliable correspondences in a given putative set is a fundamental task in two-view geometry estimation. The random sample consensus (RANSAC) method is a widely used estimator. It typically searches inliers within the putative correspondences initialized by the local similarity of the descriptors. However, RANSAC may be inefficient when actual inliers are heavily contaminated by mismatches. In this study, we attempt to identify true inliers from heavily contaminated two-view correspondences and propose a parallel core sample consensus (CSAC) method based on gradient difference. CSAC employs the gradient difference between two images as a global metric to compensate for the locality of the typical initialization. First, a pool of errors is constructed in parallel based on the gradient differences of the pixels between a pair of correspondences. For four keypoints of two correspondences, the gradients of the pixels on the line between two keypoints in each image are calculated. The error of the two correspondences is the average difference between the two resulting gradient serials. Second, a core set is constructed using the correspondences with the top-k smallest errors in the pool. Subsequently, CSAC searches the inliers in the input set via parallel testing of the minimal sets sampled in the core set. Finally, post-processing refines the resulting inliers based on neighborhood preservation. Experiments comparing seven state-of-the-art methods are conducted on eight publicly available datasets. The experimental results indicate that CSAC outperforms the other competing methods in terms of inlier precision and model accuracy. The source code is available at https://github.com/xintaoding/CSAC .
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