Accurate hand measurement data is of crucial importance in medicalscience, fashion industry, and augmented/virtual reality applications. Conventional methods extract the hand measurements manually using a measuring tape, thereby being very time-consumingand yielding unreliable measurements. In this paper, we propose–tothe best of our knowledge–the first deep-learning-based methodto automatically measure the hand in a non-contact manner froma single 3D hand scan. The proposed method employs a 3D handscan, extracts the features, reconstructs the hand by making useof a 3D hand template, transfers the measurements defined on thetemplate and extracts them from the reconstructed hand. In orderto train, validate, and test the method, a novel large-scale synthetichand dataset is generated. The results on both the unseen syntheticdata and the unseen real scans captured by the Occipital structuresensor Mark I demonstrate that the proposed method outperformsthe state-of-the-art method in most hand measurement types
Nourbakhsh Kaashki, N, Dai, X, Hu, P & Munteanu, A 2022, A Deep Learning Approach to Automatically Extract 3D Hand Measurements. in Proceedings of 2022 7th International Conference on Machine Learning Technologies, ICMLT 2022. ACM International Conference Proceeding Series, Association for Computing Machinery New York, NY, United States, pp. 141-146. https://doi.org/10.1145/3529399.3529423
Nourbakhsh Kaashki, N., Dai, X., Hu, P., & Munteanu, A. (2022). A Deep Learning Approach to Automatically Extract 3D Hand Measurements. In Proceedings of 2022 7th International Conference on Machine Learning Technologies, ICMLT 2022 (pp. 141-146). (ACM International Conference Proceeding Series). Association for Computing Machinery New York, NY, United States. https://doi.org/10.1145/3529399.3529423
@inproceedings{ca07324fe8424c04aa86dd2942164eac,
title = "A Deep Learning Approach to Automatically Extract 3D Hand Measurements",
abstract = "Accurate hand measurement data is of crucial importance in medicalscience, fashion industry, and augmented/virtual reality applications. Conventional methods extract the hand measurements manually using a measuring tape, thereby being very time-consumingand yielding unreliable measurements. In this paper, we propose–tothe best of our knowledge–the first deep-learning-based methodto automatically measure the hand in a non-contact manner froma single 3D hand scan. The proposed method employs a 3D handscan, extracts the features, reconstructs the hand by making useof a 3D hand template, transfers the measurements defined on thetemplate and extracts them from the reconstructed hand. In orderto train, validate, and test the method, a novel large-scale synthetichand dataset is generated. The results on both the unseen syntheticdata and the unseen real scans captured by the Occipital structuresensor Mark I demonstrate that the proposed method outperformsthe state-of-the-art method in most hand measurement types",
author = "{Nourbakhsh Kaashki}, Nastaran and Xinxin Dai and Pengpeng Hu and Adrian Munteanu",
note = "Publisher Copyright: {\textcopyright} 2022 ACM. Copyright: Copyright 2022 Elsevier B.V., All rights reserved.",
year = "2022",
month = mar,
day = "11",
doi = "10.1145/3529399.3529423",
language = "English",
isbn = "9781450395748",
series = "ACM International Conference Proceeding Series",
publisher = "Association for Computing Machinery New York, NY, United States",
pages = "141--146",
booktitle = "Proceedings of 2022 7th International Conference on Machine Learning Technologies, ICMLT 2022",
}