3.1.2. Data Registration and Noise Removal

After the completion of the scan of the small-scale high formwork model set, it was necessary to conduct post processing of the raw point cloud data. Co-registration of multiple point clouds in a unit reference frame is important for further data processing. Two methods are usually adopted: homologous points identification and surface matching [54]. Homologous points identification needs several points indicated a same object that can be identified without spatial ambiguity in subsequent point clouds. Hence, targets-based registration was used in this study. A control network with more stable points should be established in order to periodically observe the targets and verify their stability. In this study, four stable locations were identified as TLS targets in such a way that they were geometrically well distributed.

The scans were registered using the targets and Iterative Closet Point (ICP) adjustment. The details of the ICP algorithm can be found in the study of Besl and McKay [55] and in the study of Sgrenzaroli and Wolfart [56]. The data processing was conducted using algorithms implemented in the scanner combined software JRC 3D Reconstructor. The combined software not only registers the multiple scans data but can also remove the noise data and reduce the data density to facilitate further data processing. Alternatively, point clouds data can be exported in many formats like ASCII for post processing in MATLAB.

**Figure 3.** The layout of the TLS measurement.
