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 optimization-based 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 Anet-partial, 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-the-art methods in terms of accuracy and robustness.
Nourbakhsh Kaashki, N, Hu, P & Munteanu, A 2023, 'Anet: A Deep Neural Network for Automatic 3D Anthropometric Measurement Extraction', IEEE Transactions on Multimedia, vol. 25, pp. 831-844. https://doi.org/10.1109/TMM.2021.3132487
Nourbakhsh Kaashki, N., Hu, P., & Munteanu, A. (2023). Anet: A Deep Neural Network for Automatic 3D Anthropometric Measurement Extraction. IEEE Transactions on Multimedia, 25, 831-844. https://doi.org/10.1109/TMM.2021.3132487
@article{4750cb6877f94d9cafed21ebdb68d615,
title = "Anet: A Deep Neural Network for Automatic 3D Anthropometric Measurement Extraction",
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 optimization-based 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 Anet-partial, 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-the-art methods in terms of accuracy and robustness.",
keywords = "Anthropometric measurement extraction, 3d scanning, deep neural networks, template fitting",
author = "{Nourbakhsh Kaashki}, Nastaran and Pengpeng Hu and Adrian Munteanu",
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: {\textcopyright} 1999-2012 IEEE. Copyright: Copyright 2023 Elsevier B.V., All rights reserved.",
year = "2023",
doi = "10.1109/TMM.2021.3132487",
language = "English",
volume = "25",
pages = "831--844",
journal = "IEEE Transactions on Multimedia",
issn = "1520-9210",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
}