  This paper proposes a simple spatial-temporal smoothness based method for
solving dense non-rigid structure-from-motion (NRSfM). First, we revisit the
temporal smoothness and demonstrate that it can be extended to dense case
directly. Second, we propose to exploit the spatial smoothness by resorting to
the Laplacian of the 3D non-rigid shape. Third, to handle real world noise and
outliers in measurements, we robustify the data term by using the $L_1$ norm.
In this way, our method could robustly exploit both spatial and temporal
smoothness effectively and make dense non-rigid reconstruction easy. Our method
is very easy to implement, which involves solving a series of least squares
problems. Experimental results on both synthetic and real image dense NRSfM
tasks show that the proposed method outperforms state-of-the-art dense
non-rigid reconstruction methods.
