Abstract
Feature matching aims at generating correspondences across images, which is widely used in many computer vision tasks. Although considerable progress has been made on feature descriptors and fast matching for initial correspondence hypotheses, selecting good ones from them is still challenging and critical to the overall performance. More importantly, existing methods often take a long computational time, limiting their use in real-time applications. This paper attempts to separate true correspondences from false ones at high speed. We term the proposed method (GMS) grid-based motion Statistics, which incorporates the smoothness constraint into a statistic framework for separation and uses a grid-based implementation for fast calculation. GMS is robust to various challenging image changes, involving in viewpoint, scale, and rotation. It is also fast, e.g., take only 1 or 2 ms in a single CPU thread, even when 50K correspondences are processed. This has important implications for real-time applications. What’s more, we show that incorporating GMS into the classic feature matching and epipolar geometry estimation pipeline can significantly boost the overall performance. Finally, we integrate GMS into the well-known ORB-SLAM system for monocular initialization, resulting in a significant improvement.
Highlights
Feature matching is one of the most fundamental problems in the computer vision community
We evaluate the proposed method on fundamental matrices (FMs)-Bench (Bian et al 2019), where correspondences selection methods are integrated into a classic feature matching and epipolar geometry estimation pipeline (i.e., SIFT, RANSAC, and the 8-point algorithm), and the overall performance is compared
Compares with SIFT–ratio test (RT), our approach can lead to significantly better results on Tanks and Temples (T&T) and Community Photo Collection (CPC) datasets, demonstrating the efficacy of
Summary
Feature matching is one of the most fundamental problems in the computer vision community. Considerable progress has been made on features, matchers, and estimators, the overall performance is still limited by the false correspondences, i.e, they cause robust estimators to fail to find a correct model and true inliers. This problem is critical but received relatively less attention than other problems motioned above. Existing approaches are time-consuming (Lin et al 2017), limiting their use in real-time applications To address this gap, we propose a novel method termed (GMS) grid-based motion statistics for separating true correspondences from false ones at high speed
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