@online{Golyanik_arXiv1909.02468,
TITLE = {Intrinsic Dynamic Shape Prior for Fast, Sequential and Dense Non-Rigid Structure from Motion with Detection of Temporally-Disjoint Rigidity},
AUTHOR = {Golyanik, Vladislav and Jonas, Andr{\'e} and Stricker, Didier and Theobalt, Christian},
LANGUAGE = {eng},
URL = {http://arxiv.org/abs/1909.02468},
EPRINT = {1909.02468},
EPRINTTYPE = {arXiv},
YEAR = {2019},
ABSTRACT = {While dense non-rigid structure from motion (NRSfM) has been extensively<br>studied from the perspective of the reconstructability problem over the recent<br>years, almost no attempts have been undertaken to bring it into the practical<br>realm. The reasons for the slow dissemination are the severe ill-posedness,<br>high sensitivity to motion and deformation cues and the difficulty to obtain<br>reliable point tracks in the vast majority of practical scenarios. To fill this<br>gap, we propose a hybrid approach that extracts prior shape knowledge from an<br>input sequence with NRSfM and uses it as a dynamic shape prior for sequential<br>surface recovery in scenarios with recurrence. Our Dynamic Shape Prior<br>Reconstruction (DSPR) method can be combined with existing dense NRSfM<br>techniques while its energy functional is optimised with stochastic gradient<br>descent at real-time rates for new incoming point tracks. The proposed<br>versatile framework with a new core NRSfM approach outperforms several other<br>methods in the ability to handle inaccurate and noisy point tracks, provided we<br>have access to a representative (in terms of the deformation variety) image<br>sequence. Comprehensive experiments highlight convergence properties and the<br>accuracy of DSPR under different disturbing effects. We also perform a joint<br>study of tracking and reconstruction and show applications to shape compression<br>and heart reconstruction under occlusions. We achieve state-of-the-art metrics<br>(accuracy and compression ratios) in different scenarios.<br>},
}