LAUSR

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, to ensure the regularity of straight line edges, 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. First, the center of the inscribed circle of each triangle is used as the seed point for matching based on triangulation and epipolar line constraints. Then the successfully matched seed points are used for region growing while each growing point is matched. The triangulations are updated, and the aforementioned process is repeated until no new matched points are generated. Finally, tracking matching based on the quasi-dense matched points is performed on image sequence and 3D coordinates of matched points are calculated. Two sets of stereo image pairs acquired using smartphones and four sets of image sequence 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 system demonstrated that the 3D point cloud obtained via quasi-dense matching with feature line constraint has more regular edge points and better integrity, thereby confirming the effectiveness of the proposed algorithm.