## 1 Introduction  
An image-guided surgery system visualizes the three-dimensional position of a surgical tool using preoperative medical images and provides the location of lesions and surrounding areas in real time [1, 2]. The imaging information of this type of system allows surgeons to perform minimally invasive surgery and plan a path to the lesion in advance, thereby improving the quality of the surgery and reducing the operating time [3]. Therefore, this technology is widely used for different types of neurosurgery, such as tumor biopsy and resection, craniotomy, and deep brain stimulation [4], as well as in head and neck surgery, including sinusitis surgery [5]. Spatial registration, one of the core elements of image-guided ENT- and neurosurgery systems, is the process of aligning the coordinates of a medical image taken before surgery with the spatial coordinates of the patient acquired during surgery in the same space [6]. If the registration between the two spaces is not accurate, a gap arises between the position of the actual surgical tool and that of the virtual surgical tool displayed on the image. Therefore, the registration technology is a crucial factor that affects the accuracy of an image-guided ENT- and neurosurgery system [3, 4, 7]. Patient-to-medical image registration methods can be divided into point registration and surface registration methods [8]. A surface registration method finds the transformation matrix based on a point cloud in the patient space and the medical image space, and does not require attachment of specific fiducial markers [9; 10]. Therefore, this type of registration method is free from problems such as skin swelling due to attachment of fiducial markers [11], and additional medical imaging is unnecessary, thereby eliminating cost and hassle [2]. In surface registration, also called marker-less registration, many corresponding points in each space are matched up through a sequential process of coarse and precise registration [12]. Coarse registration, which is rough and relatively inaccurate, is performed using the initial anatomical landmarks of each space, then precise registration is performed by applying an iterative closest point (ICP) algorithm. ICP is an optimization method that iteratively finds the medical image coordinates that satisfy the minimum distance from the point cloud in the patient space, and a transformation matrix is acquired when the sum of the Euclidean distances between corresponding points is minimal [13; 14]. Unfortunately, despite the various advantages of surface registration, its accuracy continues to be controversial. Several studies reported that the accuracy of the technique is similar to that of point registration [15], but many other studies reported significantly lower accuracy [2; 16]. Therefore, despite the various advantages of the surface registration technique, concerns about its accuracy remain. The acquisition and processing of the point sets of each space are essential to improving the accuracy of a surface registration method that is based on the point sets in the patient and medical image space [12]. While medical images such as CT often show excellent resolution and accuracy [3; 17], most previous studies focused on the extraction of a point cloud in patient space; these studies showed that accuracy can be affected by such factors as equipment performance or clinical proficiency [2; 9; 12; 18]. The approaches to extraction of the point cloud in patient space can be divided into contact-type approaches that extract points by tracing probes on the surface of the face and non-contact approaches that collect points using additional scanner devices [6]. In the latter, a scanner device can be used to avoid contact with the surface of the face, thereby reducing the error due to deformation of soft skin when obtaining the point cloud. Furthermore, this method has the advantage of being able to obtain more point sets, which increases the accuracy of registration.