Nonrigid Shape Recovery by Gaussian Process Regression

• Main Authors: ZHU, Jianke, HOI, Steven C. H., LIU, Michael R.
• Format: text
• Language: English
• Published: Institutional Knowledge at Singapore Management University 2009
• Subjects: Shape; Gaussian processes; feature extraction; image matching; mesh generation; optimisation; Computer Sciences; Databases and Information Systems
• Online Access: https://ink.library.smu.edu.sg/sis_research/2373; https://ink.library.smu.edu.sg/context/sis_research/article/3373/viewcontent/NonrigidShapeRecovery_Gaussian_afv.pdf
• Institution: Singapore Management University

Most state-of-the-art nonrigid shape recovery methods usually use explicit deformable mesh models to regularize surface deformation and constrain the search space. These triangulated mesh models heavily relying on the quadratic regularization term are difficult to accurately capture large deformations, such as severe bending. In this paper, we propose a novel Gaussian process regression approach to the nonrigid shape recovery problem, which does not require to involve a predefined triangulated mesh model. By taking advantage of our novel Gaussian process regression formulation together with a robust coarse-to-fine optimization scheme, the proposed method is fully automatic and is able to handle large deformations and outliers. We conducted a set of extensive experiments for performance evaluation in various environments. Encouraging experimental results show that our proposed approach is both effective and robust to nonrigid shape recovery with large deformations.