Monocular 3-D tracking of inextensible deformable surfaces under L2-norm
IEEE Transactions on Image Processing
Convex optimization for nonrigid stereo reconstruction
IEEE Transactions on Image Processing
Near-duplicate keyframe retrieval by semi-supervised learning and nonrigid image matching
ACM Transactions on Multimedia Computing, Communications, and Applications (TOMCCAP)
Nonrigid stereo reconstruction using linear programming
Proceedings of the 1st international workshop on 3D video processing
A globally optimal approach for 3D elastic motion estimation from stereo sequences
ECCV'10 Proceedings of the 11th European conference on Computer vision: Part IV
High-resolution object deformation reconstruction with active range camera
Proceedings of the 32nd DAGM conference on Pattern recognition
Exploring ambiguities for monocular non-rigid shape estimation
ECCV'10 Proceedings of the 11th European conference on computer vision conference on Computer vision: Part III
3D structure refinement of nonrigid surfaces through efficient image alignment
ACCV'10 Proceedings of the 10th Asian conference on Computer vision - Volume Part IV
A fast approach to deformable surface 3D tracking
Pattern Recognition
Monocular template-based tracking of inextensible deformable surfaces under L2-norm
ACCV'09 Proceedings of the 9th Asian conference on Computer Vision - Volume Part II
A memetic algorithm for efficient solution of 2D and 3D shape matching problems
AI'12 Proceedings of the 25th Australasian joint conference on Advances in Artificial Intelligence
Direct Model-Based Tracking of 3D Object Deformations in Depth and Color Video
International Journal of Computer Vision
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The key challenge with 3D deformable surface tracking arises from the difficulty in estimating a large number of 3D shape parameters from noisy observations. A recent state-of-the-art approach attacks this problem by formulating it as a Second Order Cone Programming (SOCP) feasibility problem. The main drawback of this solution is the high computational cost. In this paper, we first reformulate the problem into an unconstrained quadratic optimization problem. Instead of handling a large set of complicated SOCP constraints, our new formulation can be solved very efficiently by resolving a set of sparse linear equations. Based on the new framework, a robust iterative method is employed to handle large outliers. We have conducted an extensive set of experiments to evaluate the performance on both synthetic and real-world testbeds, from which the promising results show that the proposed algorithm not only achieves better tracking accuracy, but also executes significantly faster than the previous solution.