Automated markerless registration of point clouds from TLS and structured light scanner for heritage documentation

International audienceThree-dimensional (3D) model is a major form of cultural heritage documentation. Due to the acquisition methods and characteristic of point cloud, point cloud selection usually has an effect on 3D modeling of cultural heritage. In order to acquire a fine and complete point cloud, this paper chose two scanned instruments: Terrestrial Laser Scanning (TLS) and Structured Light Scanner (SLS). TLS can scan entire area with one scan, and the resolution of the LiDAR point cloud is millimeter-scale. The resolution of SLS point clouds is high, about sub-millimeter, which shows better capacity of detailed description than TLS, but the field of view is limited and registration of multiple point clouds easily produces accumulative error. To overcome above drawbacks, this paper combines these two complementary scanning data. The TLS point cloud represents the whole region, and each SLS point cloud cover a part of it with higher accuracy. After acquisition, it is necessary to register and blend the TLS and SLS point-clouds to produce a unique and detailed model for the whole scene. Existing registration systems mostly rely on manual post-processing or marker-based alignment. Manual registration is however time consuming and tedious, while markers increase the complexity of scanning and are not always acceptable in cultural site documentation. Therefore, we propose an automated markerless registration and fusion pipeline for point clouds. Firstly, we replace the marker-based coarse alignment by an automated registration of SLS and TLS point clouds; secondly, we refine the alignment of SLS point-clouds on TLS data using the Iterative Corresponding Point algorithm; finally, we seamless stitch the SLS and TLS point clouds by globally regularizing the registration error for the all the point clouds at once. Our experiments shows the efficiency of the proposed approach on two real-world cases, involving detailed point clouds correctly aligned without requiring markers or manual tuning. This paper provides an operational process reference for automated markerless registration of multi-source point clouds