Segmentation of 3D Point Cloud Data Representing Full Human Body Geometry: A Review. (July 2023)

• Record Type: Journal Article
• Authors: Krawczyk, Damian; Sitnik, Robert
• Is Part Of: Pattern recognition. Volume 139(2023)
• Publication Date: 2023-07
• Subjects: human body silhouette segmentation -- automated point cloud segmentation -- surface measurement -- 3D data acquisition; Pattern perception -- Periodicals; Perception des structures -- Périodiques; Patroonherkenning; 006.4
• DOI: 10.1016/j.patcog.2023.109444
• Languages: English
• ISSNs: 0031-3203

Abstract

Highlights: A comparison and a review of the segmentation techniques of the 3D human body models The surface measurement of the 3D point clouds representing the human body geometry The real-time applications of segmentation algorithms of 3D human body models The recommendation of the segmentation techniques for the 4D human body models Abstract: This article aims to present a review of the segmentation techniques of the 3D data representing human body in the form of point clouds. The techniques discussed are divided into three subgroups: 2D contour approaches, topological techniques, and machine learning heuristics. These subgroups are then reviewed regarding the following aspects: computation time, accuracy, reliability, dependency on human pose, and segment count. The authors emphasize an analysis of these algorithms with respect to their exploitation in the segmentation of 3D data varying in time, as well as further improvement of the applications in anthropometry. The conclusion reached is that machine learning approach tends to be the most suitable solution for future 4D applications. Another foreseeable direction of development in the field of segmentation algorithms is the classification of the points on the borders between segments and maintaining fluent and consistent edges of the segments between the subsequent frames.