FEATURE MATCHING ENHANCEMENT USING THE GRAPH NEURAL NETWORK (GNN-RANSAC)
نویسندگان
چکیده
Abstract. Improving the performance of feature matching plays a key role in computers vision and photogrammetry applications, such as fast image recognition, Structure from Motion (SFM), aerial triangulation, Visual Simultaneous Localization Mapping (VSLAM), etc., where RANSAC algorithm is frequently used for outlier detection; note that most widely robust approach computer detection. It known ratio primarily determines number trial runs needed, which eventually, computation time. Over time, different methods have been proposed to reject false-positive correspondences improve RANSAC, GR_RANSAC, SuperGlue, LPRANSAC. The specific objective this study propose filtering based on Graph Neural Networks (GNN), pre-processing step before can result improvements rejecting outliers. research idea descriptors corresponding points, well their spatial relationship, should be similar sequences. In graph representation, built by adjacency matrix data (nodes features), there similarity points are close each other domain. From many GNNs techniques, Attention (GATs) were selected they assign importance neighbour’s contribution anisotropic operations, so features neighbour nodes not considered same way, unlike techniques. our approach, we build image, because two-dimensional relationships between domain consecutive images similar. Then during processing, with any significantly neighbours Next, updated GNN layer. GNN-RANSAC tested experimentally real pairs. Clearly, pre-filtering increases inlier results faster convergence compared ordinary making it attractive real-time applications. Furthermore, no need learn features.
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ژورنال
عنوان ژورنال: The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences
سال: 2022
ISSN: ['1682-1777', '1682-1750', '2194-9034']
DOI: https://doi.org/10.5194/isprs-archives-xlvi-m-2-2022-83-2022