3DBODY.TECH 2019 - Paper 19.074

C. Shu et al., "Localizing Anthropometric Landmarks Using 3-D Surface Features", in Proc. of 3DBODY.TECH 2019 - 10th Int. Conf. and Exh. on 3D Body Scanning and Processing Technologies, Lugano, Switzerland, 22-23 Oct. 2019, pp. 74-80, doi:10.15221/19.074.


Localizing Anthropometric Landmarks Using 3-D Surface Features


Chang SHU 1, Pengcheng XI 1, Allan KEEFE 2

1 National Research Council of Canada, Ottawa, Canada;
2 Defence Research and Development Canada, Toronto, Canada


Accurate localization of anthropometric landmarks is crucial for processing and analyzing 3-D anthropometric data. For example, landmarks are used to extract dimensional measurements from 3-D scans of human bodies. They can also be used to fit a template model to the scans such that a correspondence across the scans can be established. From this correspondence, we can perform statistical shape analysis to understand the variabilities of human shapes. In this paper, we propose a new method for localizing anthropometric landmarks using a combination of 3-D surface features and the latest deep learning techniques. The method makes use of geometric features represented as descriptor vectors. We first identify a set of locations that exhibit salient geometric features. Then we use pre-registered 3-D models to train a classifier for each geometric landmark. With the geometric landmarks, we fit a template to the data scan. The full set of anthropometric landmarks can be predicted from the template-fitted model. We validate our method using the 2012 Canadian Forces Anthropometric Survey (CFAS) dataset where 2,200 full-body scans were collected.


Full paper: 19074shu.pdf
Proceedings: 3DBODY.TECH 2019, 22-23 Oct. 2019, Lugano, Switzerland
Pages: 74-80
DOI: 10.15221/19.074

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