Quasi-Dense Matching Algorithm for Close-Range Image Combined With Feature Line Constraint

Abstract

Point-based sparse or dense matching can typically obtain satisfactory 3D point clouds of general contour features, but the deformation problem at the edges of artificial objects is prominent. Thus, a quasi-dense matching algorithm for close-range images combined with feature line constraint is proposed in this study. The method utilizes reliable matched points to construct the initial Delaunay triangulation and then optimizes the triangulation using the matched feature line. On this basis, iterative quasi-dense matching based on triangulation constraint is implemented. The process achieves matching with the center of the inscribed circle of each triangle as the seed point and growing matching. Two sets of stereo image pairs acquired using smartphones and four sets of sequence images provided by public datasets are selected for quasi-dense matching experiments. The comparison of results of constraint matching of the two triangulations before and after optimization as well as the matching results obtained via VisualSFM software demonstrated that the 3D point cloud obtained via quasi-dense matching with feature line constraint presents better results at the edges of buildings, thereby confirming the effectiveness of the proposed algorithm.