FootCapture: Towards an AR-based System for 3D Foot Object Acquisition through Photogrammetry
Published in Medical Imaging with Deep Learning, 2024
We present FootCapture, an AR-based mobile application designed to simplify the acquisition of high-quality 3D foot models for clinical applications such as chronic wound monitoring and orthopedics. We developed an intuitive dome-based interface that guides untrained users to capture optimal images for photogrammetry-based reconstruction. In a comparative user study (n=7), we evaluated FootCapture against Apple’s GuidedCapture. While usability scores were comparable, we observed that FootCapture consistently produced more robust and accurate 3D models. Our method demonstrates superior resilience to user errors and enables a flexible, low-cost workflow, making it a valuable tool for clinical practice.
Recommended citation: Khan-Blouki, Valentin; Seiz, Franziska; Walter, Nicolas; Jaus, Alexander; Marinov, Zdravko; Luijten, Gijs; Egger, Jan; Seibold, Constantin Marc; Solte, Dirk; Kleesiek, Jens; Stiefelhagen, Rainer. (2024). "FootCapture: Towards an AR-based System for 3D Foot Object Acquisition through Photogrammetry." Medical Imaging with Deep Learning
Download Paper