## 1 Introduction  
### Background and Purpose of the Study  
Terrestrial laser scanning (TLS) is a system that can acquire the three-dimensional (3D) position information of an object remotely with a laser. Terrestrial laser scanning has been increasingly used in the surveys of buildings, bridges, and collapsed rock slopes because it allows easy and rapid surveying, even in the lack of access due to its contactless data collection. Terrestrial laser scanning is mainly developed for the purpose of providing a rapid and accurate remote sensing for 3D objects modeling but can be also used to monitor the structural condition. Moreover, TLS shows an equivalent capacity in damage detection and deformation (or volumetric change) evaluation to conventional photographic methods [1]. For the detection of damages such as crack, spalling, and abrasion, [PERSON] et al. [2] showed the feasibility of TLS by presenting that the measurable width, depth, and orientation of cracks according to the sampling interval of the laser scanner, and the range of the laser beam. [PERSON] and [PERSON] [3] proved that a laser displacement sensor can measure a cracks' width up to 0.7 mm on a concrete specimen. [PERSON] et al. [4] configured the fundamental mathematics to determine the detectable minimum crack width with a terrestrial laser scanner in unit-based masonry by a parametric study using orthogonal offset, interval scan angle, crack orientation, and crack depth. [PERSON] et al. [5] proposed a crack detection method based on a second derivative image processing of thermal images from laser heated spots, comparing with the dye penetrant inspection. [PERSON] et al. [6] presented a detection technique that can simultaneously localize and quantify spalling defects on concrete surfaces using TLS. [PERSON] et al. [7] developed an automatic method based on image processing and laser scanning in assessing cracks in bridges. The captured images were orthorectified by geometric information surveyed by TLS, and then an image processing was performed for crack characterization. [PERSON] et al. [8] suggested an automatic crack detection method through TLS for concrete beam surfaces using a few simple imaging processes techniques such as conversion to gray scale, median filtering, and thresholding. For the deformation or deflection of structures, [PERSON] et al. [9] took advantage of planes fitted to point clouds extracted from the TLS data when gauging the displacement for monitoring of structural deformation. The plane model is suitable for small areas; thus the section is divided into grid cells. As for monitoring of tunnels, [PERSON] et al. [10] configured the tunnel model with a cylinder to detect deformations in a bored tunnel. [PERSON] et al. [11] analyzed the deformation of a structure surface by statistical regression such as a polynomial function. [PERSON] [12] proposed the lofting method by estimation of control points and the method showed good accuracy in the 3D non-uniform rational b-splines (NURBS) surface. [PERSON] et al. [13] presented a 3D point cloud change analysis approach for tracking small movements over time through a combination of a direct point-wise distance metrics in conjunction with statistical sampling. [PERSON] and [PERSON] [14] suggested an algorithm to identify structural anomalies in 3D point clouds of infrastructure systems by linear and non-linear transformations from RGB to non-RGB spaces. Above these, many researchers have carried out various studies on the damage detection and deformation quantification on various structures by TLS or other equivalent methods, such as photography [15; 16; 17; 18; 19; 20]. Although these studies have evaluated TLS for the purpose of structural health monitoring, the data capacity, resolution, and accuracy of crack recognition are still pointed out as drawbacks of TLS [21; 22].