Anthropometric clothing measurements from 3D body scans
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ORIGINAL PAPER
Anthropometric clothing measurements from 3D body scans Song Yan1
· Johan Wirta2 · Joni-Kristian Kämäräinen1
Received: 18 June 2018 / Revised: 11 September 2019 / Accepted: 6 December 2019 © The Author(s) 2020
Abstract We propose a full processing pipeline to acquire anthropometric measurements from 3D measurements. The first stage of our pipeline is a commercial point cloud scanner. In the second stage, a pre-defined body model is fitted to the captured point cloud. We have generated one male and one female model from the SMPL library. The fitting process is based on non-rigid iterative closest point algorithm that minimizes overall energy of point distance and local stiffness energy terms. In the third stage, we measure multiple circumference paths on the fitted model surface and use a nonlinear regressor to provide the final estimates of anthropometric measurements. We scanned 194 male and 181 female subjects, and the proposed pipeline provides mean absolute errors from 2.5 to 16.0 mm depending on the anthropometric measurement. Keywords Anthropometric measurement · 3D body model · Non-rigid ICP
1 Introduction Anthropometric measurements, such as chest and hip circumference or shoulder-to-shoulder distance, provide detailed information about the body shape. The body shape information is essential for industrial design [18], clothing design [8], medical sciences [16] and ergonomics [19]. The measurements have traditionally been made manually from physical subject using a tape measure, but the raise of online shopping and personalized tools set new demand for computerized anthropometric measurements. A standard pipeline for computerized anthropometric measurements is the following [3,7,10,25–27]: (1) a 2D or 3D body scan producing a 3D point cloud or an initial model, (2) fitting of a pre-defined model and (3) measurements from the fitted model. The main challenge is the step two which should provide an accurate and watertight volumetric model of a subject so that impor-
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Song Yan [email protected] Johan Wirta [email protected] Joni-Kristian Kämäräinen [email protected]
1
Computing Sciences, Tampere University, Tampere, Finland
2
NOMO Technologies Ltd, Espoo, Finland
tant measurements can be made on the model surface. Challenges arise from different sensor modalities, poses and occluded regions. The proposed method in this work shares the main steps of the standard pipeline (Fig. 1), but instead of physiologically valid model fit, we adopt a non-rigid iterative closest point (ICP) registration between the model and captured point clouds. Moreover, we do not make anthropometric measurements directly from the fitted model surface, but extract a set of physiologically meaningful surface features (body circumferences) and use them to train a regressor that provides estimates of the physical anthropometric measurements. Our main contributions are: – A full processing pipeline from 3D body scans to anthropometric measurements. – The body model registration step using a non-rigid ICP
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