The human foot is an incredibly complex combination of bones, joints, and muscles which helps humans with their balance, posture, and mobility. Foot measurement extraction plays an essential role in many applications ranging from medical science to fashion industry. Conventional foot measurement extraction methods require manual interventions using a measuring tape or its digital twins. Recent advancements in 3D scanning technologies and deep learning strategies enable us to propose, to the best of our knowledge, the first deep-learning-based approach to automatic foot measurement extraction from a single 3D scan. The proposed method involves three steps: (i) 3D foot data acquisition, (ii) template fitting, and (iii) measurement extraction. The foot is scanned with the Occipital Structure Sensor Pro. The template fitting process is performed using a deep neural network trained for foot template fitting. Finally, the measurement defined on the template are transferred and refined to the fitted template. The template fitting method is trained on a novel large dataset of \textit{dense} synthetic foot samples. The experimental measurement results demonstrate that the proposed method performs well on both unseen synthetic and real scans.
Nourbakhsh Kaashki, N, Royen, RD, Dai, X, Hu, P & Munteanu, A 2022, A Deep-learning-based Approach to Automatically Measuring Foots from a 3D scan. in IEEE Asia-Pacific Conference on Computer Science and Data Engineering (CSDE). Proceedings of IEEE Asia-Pacific Conference on Computer Science and Data Engineering, CSDE 2022, IEEE, Gold Coast, Australia, pp. 1-5, 2022 IEEE Asia-Pacific Conference on Computer Science and Data Engineering (CSDE), 18/12/22. https://doi.org/10.1109/CSDE56538.2022.10089290
Nourbakhsh Kaashki, N., Royen, R. D., Dai, X., Hu, P., & Munteanu, A. (2022). A Deep-learning-based Approach to Automatically Measuring Foots from a 3D scan. In IEEE Asia-Pacific Conference on Computer Science and Data Engineering (CSDE) (pp. 1-5). (Proceedings of IEEE Asia-Pacific Conference on Computer Science and Data Engineering, CSDE 2022). IEEE. https://doi.org/10.1109/CSDE56538.2022.10089290
@inproceedings{bb74af03cfff4757910b0edefcd7bb55,
title = "A Deep-learning-based Approach to Automatically Measuring Foots from a 3D scan",
abstract = "The human foot is an incredibly complex combination of bones, joints, and muscles which helps humans with their balance, posture, and mobility. Foot measurement extraction plays an essential role in many applications ranging from medical science to fashion industry. Conventional foot measurement extraction methods require manual interventions using a measuring tape or its digital twins. Recent advancements in 3D scanning technologies and deep learning strategies enable us to propose, to the best of our knowledge, the first deep-learning-based approach to automatic foot measurement extraction from a single 3D scan. The proposed method involves three steps: (i) 3D foot data acquisition, (ii) template fitting, and (iii) measurement extraction. The foot is scanned with the Occipital Structure Sensor Pro. The template fitting process is performed using a deep neural network trained for foot template fitting. Finally, the measurement defined on the template are transferred and refined to the fitted template. The template fitting method is trained on a novel large dataset of \textit{dense} synthetic foot samples. The experimental measurement results demonstrate that the proposed method performs well on both unseen synthetic and real scans. ",
author = "{Nourbakhsh Kaashki}, Nastaran and Royen, {Remco Donovan} and Xinxin Dai and Pengpeng Hu and Adrian Munteanu",
note = "Funding Information: The 2nd author is funded by Fonds Wetenschappelijk Onderzoek (FWO) - 1S89420N. Publisher Copyright: {\textcopyright} 2022 IEEE. Copyright: Copyright 2023 Elsevier B.V., All rights reserved.; 2022 IEEE Asia-Pacific Conference on Computer Science and Data Engineering (CSDE) ; Conference date: 18-12-2022 Through 20-12-2022",
year = "2022",
month = dec,
day = "18",
doi = "10.1109/CSDE56538.2022.10089290",
language = "English",
isbn = "978-1-6654-5306-6",
series = "Proceedings of IEEE Asia-Pacific Conference on Computer Science and Data Engineering, CSDE 2022",
publisher = "IEEE",
pages = "1--5",
booktitle = "IEEE Asia-Pacific Conference on Computer Science and Data Engineering (CSDE)",
url = "https://ieee-csde.org/csde2022/",
}