S-Space Graduate School of Convergence Science and Technology (융합과학기술대학원) Dept. of Transdisciplinary Studies(융합과학부) Journal Papers (저널논문_융합과학부)
Procrustean Regression: A Flexible Alignment-Based Framework for Nonrigid Structure Estimation
- Park, Sungheon; Lee, Minsik; Kwak, Nojun
- Issue Date
- IEEE Transactions on Image Processing, Vol.27 No.1, pp. 249-264
- Procrustean Regression: A Flexible Alignment-Based Framework for Nonrigid Structure Estimation; 자연과학; Non-rigid structure from motion; non-rigid shape analysis; Procrustes analysis
- Non-rigid structure from motion (NRSfM) is a fundamental problem of computer vision. Recently, it has been shown that incorporating shape alignment in NRSfM can improve the performance significantly compared with the other algorithms, which do not consider shape alignment. However, realizing this idea was at a cost of a heavy, complicated process, which limits its usefulness and possible extensions. In this paper, we propose a novel regression framework for NRSfM, of which the variables (3D shapes) are regularized based on their aligned shapes. We show that this can be casted into an unconstrained problem or a problem with simple bound constraints, which can be efficiently solved by existing solvers. This framework can be easily integrated with numerous existing models and assumptions, such as orthographic or perspective camera models, occlusion, low-rank assumption, smooth deformations, and so on, which makes it more practical for various real situations. The experimental results show that the proposed method gives competitive result to the state-of-the-art methods for orthographic projection with much less time complexity and memory requirement, and outperforms the existing methods for perspective projection.
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