  4D scans of dynamic deformable human body parts help researchers have a
better understanding of spatiotemporal features. However, reconstructing 4D
scans based on multiple asynchronous cameras encounters two main challenges: 1)
finding the 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. In
this paper, we introduce a generic framework that is 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, which is 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 in total, 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 using the proposed
ADGC-LSTM network.
