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

• Published in: IEEE transactions on multimedia Vol. 25; pp. 831 - 844
• Main Authors: Kaashki, Nastaran Nourbakhsh, Hu, Pengpeng, Munteanu, Adrian
• Format: Journal Article
• Language: English
• Published: Piscataway IEEE 2023; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: 3D scanning; Algorithms; Anthropometric measurement extraction; Anthropometry; Artificial neural networks; Computer architecture; Data mining; Deep learning; deep neural networks; Fitting; Loss measurement; Missing data; Neural networks; Noise sensitivity; Optimization; Photogrammetry; Point cloud compression; State of the art; template fitting; Three-dimensional displays; Training
• ISSN: 1520-9210; 1941-0077
• DOI: 10.1109/TMM.2021.3132487

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.