*2.2. Object Recognition*

Extensive studies on the use of TLS in object recognition have been conducted. TLS data can generally be extracted based on features. Some studies also proposed to specify the point data based on the RGB value. For example, Pu and Vosselman [33] proposed a region-growing method to effectively extract planar objects from point clouds of a building façade, and a feature recognition method was used to classify the planar objects. In the study [39], the least square method was used to fit lines to extract important lines and points from the point clouds of structural elements on buildings damaged by an earthquake.

Some studies also focused on developing an automatic algorithm to recognize and segmen<sup>t</sup> required objects from point clouds. For example, Riveiro et al. [40] recommended an approach that can automatically extract masonry blocks from point clouds. In the study [41], machine learning methods were used to automatically classify the morphological segments of a hillslope affected by shallow landslides into seven classes (e.g., scarp, eroded area, deposit, rock outcrop and different classes of vegetation). Lee et al. [42] used a method that automatically extracts pipelines and their detailed parts, such as elbows and tees, from point clouds. Similarly, Czerniawski et al. [43] proposed as fully automatic approach for extracting pipe spools from point clouds. Some studies also developed methods that can recognize infrastructure objects based on point clouds. For example, Holgado-Barco et al. [44] extracted features from Light Detection and Ranging (LiDAR) data to model a road axis.

Most of the studies have focused on extracting data from buildings or infrastructure such as roads or bridges. However, few studies extract important elements from point cloud data based on the specific features of a high formwork. The combination of the RGB value of the point cloud data and the symmetry of the structure and special shape of the main components of the high formwork can help to rapidly extract important data from scan data.
