Title: Merging the point clouds of the head and ear by using the iterative closest point method

Authors: Yan Luximon; Nathaniel J. Martin; Roger Ball; Ming Zhang

Addresses: School of Design, The Hong Kong Polytechnic University, Hong Kong ' School of Design, The Hong Kong Polytechnic University, Hong Kong ' School of Design, The Hong Kong Polytechnic University, Hong Kong ' Interdisciplinary Division of Biomedical Engineering, The Hong Kong Polytechnic University, Hong Kong

Abstract: Three dimensional (3D) surface body scanning technology has become an easy and rapid method for capturing the human shape. However, because most scanning systems rely on a direct line of sight, data is consistently missing in shadowed areas. When the head is scanned, data points at the back of the ear and the concha are consistently missing. To create an accurate head shape including the ear shape, the ear shape must be obtained separately. Efficiently merging the ear and head shapes is imperative before modelling and statistical analyses are performed. In this study, the ear and head shapes of the participants were obtained, and then the iterative closest point (ICP) method, a technique for aligning different objects in computer graphics, was applied to merge the ear and the corresponding head. This paper describes the principle and implementation of the procedure. The results indicated that the alignment error between the original ear from the head scan and the accurate ear was approximately 1.6 mm. The results of this study revealed that the method is beneficial to automatically aligning human 3D point cloud data accurately and efficiently. This method can be used for creating an accurate head and ear model for head- and face-related product design.

Keywords: computer-aided design; CAD; image analysis; digital human modelling; DHM; 3D human models; human head; human ear; point clouds; iterative closest point; ICP; ear shape; head shape; alignment error; product design.

DOI: 10.1504/IJDH.2016.079888

International Journal of the Digital Human, 2016 Vol.1 No.3, pp.305 - 317

Available online: 18 Oct 2016 *

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