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Optimal Camera Positioning for Single-View 3D Foot Scan Completion: Evaluation Using Deep Learning-Based Reconstruction
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DOI：10.3390/asi9060119.png)
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Abstract
En 中文
Shoes are increasingly being bought online without being put on in person as internet shopping gains popularity. As a result, returns have increased significantly, which has had negative effects on the economy and the environment. Numerous technologies are available to measure foot size precisely at home or in-store in order to address this problem. People can identify their perfect shoe size and avoid needless returns by taking accurate foot measurements. A single image should be enough to measure the foot in order to make the system as easy as feasible for the user. This is accomplished by using point clouds from one side of the foot, which are produced by capturing a depth image. In order to optimise the reconstruction of partial data, this study investigates the impact of the acquisition position of a single partial foot scan on reconstruction quality and measurement accuracy when a state-of-the-art network is employed for completion. To this end, task-specific partial foot datasets were created with varying camera positions and foot orientations to determine the optimal conditions for depth map acquisition. Utilising the foot dataset that has been introduced for the purposes of training and evaluation, the network was able to generate accurate reconstructions. These reconstructions allowed for the estimation of shoe size in accordance with the European sizing system. The method is accurate enough in all tested positions to reconstruct a foot with sufficient precision. However, we also identified position 5 in our multi-view setup, which is viewed from a lower angle, as the position that leads to the best reconstruction results. Additionally, advantages were found with input data that show more of the forefoot than the heel area. Therefore, the forefoot provides more information on the overall geometry and should be the focus of single-shot procedures.
Keywords:
foot measurement
shape completion
shape reconstruction
neural network
machine learning
shoe sizing
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