Monocular Template-Based Tracking of Inextensible Deformable Surfaces under L2-Norm

We present a method for recovering the 3D shape of an inextensible deformable surface from a monocular image sequence. State-of-the-art method on this problem [1] utilizes L∞-norm of reprojection residual vectors and formulate the tracking problem as a Second Order Cone Programming (SOCP) problem. Instead of using L∞ which is sensitive to outliers, we use L2-norm of reprojection errors. Generally, using L2 leads a non-convex optimization problem which is difficult to minimize. Instead of solving the non-convex problem directly, we design an iterative L2-norm approximation process to approximate the non-convex objective function, in which only a linear system needs to be solved at each iteration. Furthermore, we introduce a shape regularization term into this iterative process in order to keep the inextensibility of the recovered mesh. Compared with previous methods, ours performs more robust to outliers and large inter-frame motions with high computational efficiency. The robustness and accuracy of our approach are evaluated quantitatively on synthetic data and qualitatively on real data.