Dense Non-rigid Structure-from-Motion Made Easy - A Spatial-Temporal Smoothness based Solution
This addresses the problem of reconstructing dense 3D shapes from 2D images for computer vision applications, but it is incremental as it builds on existing smoothness concepts.
The paper tackles dense non-rigid structure-from-motion by proposing a spatial-temporal smoothness method with an L1 norm for robustness, and it outperforms state-of-the-art methods in experiments on synthetic and real images.
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.