## 1 Introduction  
Many applications of laser scanning rely on accurate registrations that integrate point clouds obtained from different scan stations into a complete one [1; 2; 3]. Regardless of the recent development of automatic targetless methods for point cloud registration [4; 5; 6], conventional target-based methods such as the sphere-based method, are still being applied by many professionals thanks to the directness and manipulability for installing the artificial targets (e.g., spherical targets [7; 8] and calibration board [9]). However, placing and shifting multiple targets within the field of view of many instrument stations can be a tedious and time-consuming process, especially when the number of scans is large [10]. One possible solution for tackling this problem is to develop a registration method that only requires a single target, instead of multiple targets. In addition, if the target itself incurs a low cost, many targets can be used and fixed simultaneously so the cost of shifting and reinstalling can be reduced. In general, using targets for registration aims to estimate a set of approximate parameters which transform one point cloud from its own coordinate system to another. Once the two point clouds are approximately aligned, the iterative closest point (ICP)algorithm [11], or one of its variants can be used to accurately match the point clouds so that the point clouds are completely registered [12; 13; 14]. The former is often referred to as coarse registration while the latter is often defined as the fine registration [15]. The accuracy of the transformation parameters estimated in the coarse registration can greatly affect the final accuracy delivered by the fine registration. For the past decade, much research effort has been dedicated to targetless coarse registration which uses natural features detected from the scans as targets instead of using artificial targets such as spheres and modeled with different geometric primitives [16]. For example, [PERSON] et al. proposed a coarse registration method that utilizes linear features obtained from the extraction of the interception between walls and grounds in an indoor environment [17]. [PERSON] et al. developed a linear feature-based method for both indoor and outdoor environment [18]. Instead of using linear features, [PERSON] et al. developed a descriptor-based method that is based on natural planar features extracted from the scenes [19]. [PERSON] et al. utilized the cylindrical features extracted from trees to perform the registration for the point clouds obtained from forests [20]. [PERSON] et al. extracted an octagonal lamp pole for registrations [21]. A geometric model of an octagonal prism model was developed to simulate multiple conjugate points for the subsequent registrations. [PERSON] and [PERSON] extracted crest lines from states to perform the registration for cultural heritage documentation [22]. They computed the principal curvature using eigenvalue decomposition to form the create lines so that the method can be used in environments without straight line features. As mentioned before, regardless of the state-of-art of the targetless registration methods, conventional target-based methods are still widely used by many professionals. It is because manually placing the targets is more straightforward and manipulable. Using targets for registration reduces the impact of the complexity of the point clouds. Figure 1 shows scans being performed in a harsh factory environment. In such an environment, installing white spherical targets provides greater control which in turn makes registration quality independent of the natural features that cannot always be accurately extracted. Spheres plated with retroreflective materials are one of the most common targets used for point cloud registration since they are omnidirectional [23].