## 1 Introduction  
As a result of progressive improvements in the field of three-dimensional (3D)-scanning techniques, the whole human body can be reproduced in detail and digitized in the form of the 3D computer model. There are many fields which benefit from using precise measurements based on 3D scans. In medicine, examples of usage can be found in spine deformation detection [1, 2], CT/MRI image analysis [3] or maxilla-facial diagnosis [4]. Other applications relate to, e.g., the skeleton rig processing for animation [5], motion capture [6], and human system engineering in the clothing industry [7, 8]. All applications above focus on measurements and their accuracy. To enable the localization, the reference in the form of characteristic points needs to be specified. Such a role in 3D scan processing is played by human body landmarks. The body landmarks are described as unique and unambiguous locations on the human skin that can act as references for users to locate and identify points of interest [9]. Due to the direct influence on a measurement quality, these points are one of the most important scopes of interest in anthropometry [10]. Anthropometric landmarks can be obtained via traditional, manual, semi-automatic or automated processing of human body scans. Both traditional tape measurements and the manual point selection on a scan are time-consuming tasks. They also require human involvement which is naturally exposed to the subjectivity of researcher and human-related errors [2, 11]. The semi-automatic methods are widely used among researchers [12, 13]. Although this approach enables acquisition of landmark position during movement, an initial marking process needs to performed manually. Automated landmarking became more popular in the field of anthropometry applications, accelerating the marking process. Despite improvement in the accuracy and repeatability of automatic acquisition, this approach is still characterized by a strong dependency on the quality of input data. Growing interest in computer-aided landmarking led researchers to develop various algorithms for automatic detection of characteristic points. A number of them use body features and shape analysis, for example, section analysis provided by [14]. The major drawback of those methods is their reliance on the accuracy of a taken pose, assuming that each person is similar in terms of body shape and mobility. If the subject is irregularly tilted, the algorithm's accuracy decreases. A pose-independent method was presented by [15], where face landmarks were calculated using coefficients of fundamental forms such as principal and Gaussian curvature as well as a tangent map. A different approach was introduced by [PERSON] et al. [16], who used a neural network for landmark detection on 3D scans. This method successfully detects positions of characteristic points in various poses but requires complete and high-quality body shape representation. The current state-of-the-art method [17] uses deep convolutional neural networks (CNN) for the detection and classification of body landmarks. The method demonstrates robustness in dealing with scan imperfections such as noise and surface flipping due to the use of 2D images. The learning process is performed on orthogonally projected images from scans consisting of three layers: Gaussian curvature, a frontly lighted model, and a depth map. However, the CNN detection and classification process depends strongly on input data quality. The proposed mid-surface projection approach is a novel technique dealing with the CNN data preparation step. It is based on a method of [PERSON] et al. [17] and designed to enhance the quality of projected 2D images of the scan which have a direct impact on CNN performance. This method utilized scan-fitted 3D surface unwrapping, which is used for projection on the 2D image, instead of the orthogonal approach, where images are generated directly from the scan.