4D Feet: Registering Walking Foot Shapes Using Attention Enhanced Dynamic-Synchronized Graph Convolutional LSTM Network

4D-scans of dynamic deformable human body parts help researchers have a better understanding of spatiotemporal features. However, reconstructing 4D-scans utilizing multiple asynchronous cameras encounters two main challenges: 1) finding dynamic correspondences among different frames captured by each...

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Bibliographic Details
Published in:IEEE open journal of the Computer Society 2024, Vol.5, p.343-355
Main Authors: TAJDARI, FARZAM, HUYSMANS, TOON, YAO, XINHE, XU, JUN, ZEBARJADI, MARYAM, SONG, YU
Format: Article
Language:English
Subjects:
4D foot scanner
Algorithms
Body parts
Cameras
dynamic feature recognition
Feature extraction
Feature recognition
Foot
Formability
LSTM network
non-rigid registration
Point cloud compression
Shape
Synchronism
Synchronization
synchronized scans
Three dimensional models
Three-dimensional displays
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Summary:4D-scans of dynamic deformable human body parts help researchers have a better understanding of spatiotemporal features. However, reconstructing 4D-scans utilizing multiple asynchronous cameras encounters two main challenges: 1) finding dynamic correspondences among different frames captured by each camera at the timestamps of the camera in terms of dynamic feature recognition, and 2) reconstructing 3D-shapes from the combined point clouds captured by different cameras at asynchronous timestamps in terms of multi-view fusion. Here, we introduce a generic framework able to 1) find and align dynamic features in the 3D-scans captured by each camera using the nonrigid-iterative-closest-farthest-points algorithm; 2) synchronize scans captured by asynchronous cameras through a novel ADGC-LSTM-based-network capable of aligning 3D-scans captured by different cameras to the timeline of a specific camera; and 3) register a high-quality template to synchronized scans at each timestamp to form a high-quality 3D-mesh model using a non-rigid registration method. With a newly developed 4D-foot-scanner, we validate the framework and create the first open-access data-set, namely the 4D-feet. It includes 4D-shapes (15 fps) of the right and left feet of 58 participants (116 feet including 5147 3D-frames), covering significant phases of the gait cycle. The results demonstrate the effectiveness of the proposed framework, especially in synchronizing asynchronous 4D-scans.
ISSN:2644-1268
2644-1268
DOI:10.1109/OJCS.2024.3406645