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

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.

Automated summary

3D anthropometric measurement extraction is crucial for various applications. Existing methods often suffer from sensitivity to noise and missing data, as well as computational complexity. To address these limitations, we propose a deep neural network architecture that fits a template to the input scan and outputs reconstructed body and measurements, without the need for transferring and refining measurements. A novel loss function and two large datasets are introduced for training. Experimental results show that our approach outperforms state-of-the-art methods in terms of accuracy and robustness, using both synthesized and real 3D scans.