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With the development of numerous 3D sensing technologies, object registrationon cross-source point cloud has aroused researchers' interests. When the pointclouds are captured from different kinds of sensors, there are large anddifferent kinds of variations. In this study, we address an even morechallenging case in which the differently-source point clouds are acquired froma real street view. One is produced directly by the LiDAR system and the otheris generated by using VSFM software on image sequence captured from RGBcameras. When it confronts to large scale point clouds, previous methods mostlyfocus on point-to-point level registration, and the methods have manylimitations.The reason is that the least mean error strategy shows poor abilityin registering large variable cross-source point clouds. In this paper,different from previous ICP-based methods, and from a statistic view, wepropose a effective coarse-to-fine algorithm to detect and register a smallscale SFM point cloud in a large scale Lidar point cloud. Seen from theexperimental results, the model can successfully run on LiDAR and SFM pointclouds, hence it can make a contribution to many applications, such as roboticsand smart city development.

A coarse-to-fine algorithm for registration in 3D street-view cross-source point clouds