Multi-view Line Matching Based on Multi-view Stereo Vision and Leiden Graph Clustering
Accurate matching of line features is of paramount importance in the reconstruction and optimization of three-dimensional models. However,traditional dual-view line matching encounters challenges due to a limited number of views,resulting in suboptimal robustness in line matching. For line extraction results with breaks,the number of lines extracted for the same line on different images is different,resulting in poor integrity of straight line matching results. To address these issues,this paper proposes a multi-view line matching algorithm that combines Multiple-View Stereo (MVS) and Leiden graph clustering. The algorithm commences by employing the line extraction algorithm and the MVS three-dimensional reconstruction algorithm on input multi-view images for line information extraction and multi-view three-dimensional information extraction,respectively. This process yields lines on each view,dense three-dimensional points encapsulating the image scene,and the correspondence between object-side three-dimensional points and their corresponding image-side two-dimensional points. Building upon this foundation,the algorithm constructs line descriptors in the image domain by considering lines and their matching point sets within their neighborhoods. Subsequently,leveraging the three-dimensional line projection angle constraints,point-line position relationship constraints,and corresponding point constraints,the algorithm filters matching candidates based on these three geometric constraints. Harnessing the similarity relationships between lines on each view,an undirected graph is constructed. Here,lines on each view serve as nodes,and the similarity scores between lines act as edge weights. Simultaneously,connected components composed of single nodes are removed from the undirected graph,resulting in the set of connected components that represent the initial matching results. In the final stage of this process,nodes of each connected component are reconnected based on same-view collinear constraints,forming many sub-undirected graphs. The Leiden algorithm is then applied to cluster the nodes of these sub-undirected graphs. The clusters composed of a single node in the clustering results represent unsuccessfully matched lines,while clusters composed of two or more nodes signify the presence of corresponding lines across multiple views. Ultimately,the algorithm achieves accurate line matching on multi-view images. The experimental results show that the line matching results using the proposed algorithm are improved in terms of the number of line matches and the matching accuracy relative to other comparison algorithms.
multi-view line matchingfeature matchinggeometric constraintsundirected graphsdense 3D pointsmulti-view stereo visionLeiden algorithm
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