This paper proposes a new method for Non-Rigid Structure-from-Motion (NRSfM)
from a long monocular video sequence observing a non-rigid object performing
recurrent and possibly repetitive dynamic action. Departing from the
traditional idea of using linear low-order or lowrank shape model for the task
of NRSfM, our method exploits the property of shape recurrency (i.e., many
deforming shapes tend to repeat themselves in time). We show that recurrency is
in fact a generalized rigidity. Based on this, we reduce NRSfM problems to
rigid ones provided that certain recurrency condition is satisfied. Given such
a reduction, standard rigid-SfM techniques are directly applicable (without any
change) to the reconstruction of non-rigid dynamic shapes. To implement this
idea as a practical approach, this paper develops efficient algorithms for
automatic recurrency detection, as well as camera view clustering via a
rigidity-check. Experiments on both simulated sequences and real data
demonstrate the effectiveness of the method. Since this paper offers a novel
perspective on rethinking structure-from-motion, we hope it will inspire other
new problems in the field.

本文提出了一种基于形状重复性的非刚性运动结构恢复算法，利用了形状重复性是一种广义刚性的特性，因此将非刚性结构恢复问题降维到了刚性结构恢复问题上，并发展了有效的算法以及摄像机视角聚类方法，实验结果表明该方法非常有效。