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