Anet: A Deep Neural Network for Automatic 3D Anthropometric Measurement Extraction

Nastaran Kaashki, Pengpeng Hu, Adrian Munteanu

Research output: Contribution to journalArticlepeer-review

6 Citations (Scopus)

Abstract

3D Anthropometric measurement extraction is of paramount importance for several applications such as clothing design, online garment shopping, and medical diagnosis, to name a few. State-of-the-art 3D anthropometric measurement extraction methods estimate the measurements either through some landmarks found on the input scan or by fitting a template to the input scan using optimization-based techniques. Finding landmarks is very sensitive to noise and missing data. Template-based methods address this problem, but the employed optimizationbased template fitting algorithms are computationally very complex and time-consuming. To address the limitations of existing methods, we propose a deep neural network architecture which fits a template to the input scan and outputs the reconstructed body as well as the corresponding measurements. Unlike existing template-based anthropocentric measurement extraction methods, the proposed approach does not need to transfer and refine the measurements from the template to the deformed template, thereby being faster and more accurate. A novel loss function, especially developed for 3D anthropometric measurement extraction is introduced. Additionally, two large datasets of complete and partial front-facing scans are proposed and used in training. This results in two models, dubbed Anet-complete and Anetpartial, which extract the body measurements from complete and partial front-facing scans, respectively. Experimental results on synthesized data as well as on real 3D scans captured by a photogrammetry-based scanner, an Azure Kinect sensor, and the very recent TrueDepth camera system demonstrate that the proposed approach systematically outperforms the state-of-theart methods in terms of accuracy and robustness.
Original languageEnglish
Pages (from-to)831-844
Number of pages14
JournalIEEE Transactions on Multimedia
Volume25
DOIs
Publication statusPublished - 3 Dec 2021
Externally publishedYes

Bibliographical note

Funding Information:
This work was supported in part by the Innoviris under Project eTailor and in part by FWO under Project G084117.

Publisher Copyright:
© 1999-2012 IEEE.

Funder

10.13039/501100004744-Innoviris (Grant Number: BRGRD53B)

Keywords

  • 3D scanning
  • Anthropometric measurement extraction
  • Data mining
  • Deep learning
  • Fitting
  • Loss measurement
  • Point cloud compression
  • Three-dimensional displays
  • Training
  • deep neural networks
  • template fitting

ASJC Scopus subject areas

  • Signal Processing
  • Media Technology
  • Computer Science Applications
  • Electrical and Electronic Engineering

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