Automatic and Fast Extraction of 3D Hand Measurements using a Deep Neural Network

Recent advancements in 3D scanning technologies enable us to acquire the hand geometry represented as a three-dimensional point cloud. Providing accurate 3D hand scanning and accurately extracting its biometrics are of crucial importance for a number of applications in medical sciences, fashion industry, augmented and virtual reality (AR/VR). Traditional methods for hand measurement extraction require manual intervention using a measuring tape, which is time-consuming and highly dependent on the operator's expertise. In this paper, we propose, to the best of our knowledge, the first deep neural network for automatic hand measurement extraction from a single 3D scan (H-Net). The proposed network follows an encoder-decoder architecture design, taking a point cloud of the hand as input and outputting the reconstructed hand mesh as well as the corresponding measurement values. In order to train the proposed deep model, a novel synthetic dataset of hands in various shapes and poses and their corresponding measurements is proposed. Experimental results on both synthetic data and real scans captured by Occipital Mark I structure sensor demonstrate that the proposed method outperforms the state-of-the-art methods in terms of accuracy and speed.

Automated summary

Recent advancements in 3D scanning technologies have enabled accurate extraction of hand geometry and measurements. In this paper, we propose the first deep neural network for automatic hand measurement extraction and train it with a novel synthetic dataset. Experimental results demonstrate the superiority of our method in terms of accuracy and speed compared to existing techniques.