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FilterGNN:Image feature matching with cascaded outlier filters and linearattention

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摘要 The cross-view matching of local image features is a fundamental task in visual localization and 3D reconstruction.This study proposes FilterGNN,a transformer-based graph neural network(GNN),aiming to improve the matching efficiency and accuracy of visual descriptors.Based on high matching sparseness and coarse-to-fine covisible area detection,FilterGNN utilizes cascaded optimal graph-matching filter modules to dynamically reject outlier matches.Moreover,we successfully adapted linear attention in FilterGNN with post-instance normalization support,which significantly reduces the complexity of complete graph learning from O(N2)to O(N).Experiments show that FilterGNN requires only 6%of the time cost and 33.3%of the memory cost compared with SuperGlue under a large-scale input size and achieves a competitive performance in various tasks,such as pose estimation,visual localization,and sparse 3D reconstruction.
出处 《Computational Visual Media》 SCIE EI CSCD 2024年第5期873-884,共12页 计算可视媒体(英文版)
基金 supported by the National Natural Science Foundation of China(Grant No.62220106003) Tsinghua-Tencent Joint Laboratory for Internet Innovation Technology.
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