Distinctive Image Features from Scale-Invariant Keypoints
International Journal of Computer Vision
Uncalibrated Perspective Reconstruction of Deformable Structures
ICCV '05 Proceedings of the Tenth IEEE International Conference on Computer Vision - Volume 2
Globally Optimal Estimates for Geometric Reconstruction Problems
International Journal of Computer Vision
Practical Global Optimization for Multiview Geometry
International Journal of Computer Vision
Nonrigid Structure-from-Motion: Estimating Shape and Motion with Hierarchical Priors
IEEE Transactions on Pattern Analysis and Machine Intelligence
An Effective Approach to 3D Deformable Surface Tracking
ECCV '08 Proceedings of the 10th European Conference on Computer Vision: Part III
Monocular template-based reconstruction of smooth and inextensible surfaces
ACCV'10 Proceedings of the 10th Asian conference on Computer vision - Volume Part III
Monocular Template-based Reconstruction of Inextensible Surfaces
International Journal of Computer Vision
Laplacian meshes for monocular 3d shape recovery
ECCV'12 Proceedings of the 12th European conference on Computer Vision - Volume Part III
Beyond feature points: structured prediction for monocular non-rigid 3d reconstruction
ECCV'12 Proceedings of the 12th European conference on Computer Vision - Volume Part IV
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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.