Complex Non-rigid 3D Shape Recovery Using a Procrustean Normal Distribution Mixture Model
- Authors
- Cho, Jungchan; Lee, Minsik; Oh, Songhwai
- Issue Date
- May-2016
- Publisher
- SPRINGER
- Keywords
- 3D reconstruction; Shape analysis; Non-rigid structure from motion; Non-rigid shape recovery
- Citation
- INTERNATIONAL JOURNAL OF COMPUTER VISION, v.117, no.3, pp.226 - 246
- Indexed
- SCIE
SCOPUS
- Journal Title
- INTERNATIONAL JOURNAL OF COMPUTER VISION
- Volume
- 117
- Number
- 3
- Start Page
- 226
- End Page
- 246
- URI
- https://scholarworks.bwise.kr/erica/handle/2021.sw.erica/13700
- DOI
- 10.1007/s11263-015-0860-7
- ISSN
- 0920-5691
- Abstract
- Recovering the 3D shape of a non-rigid object is a challenging problem. Existing methods make the low-rank assumption and do not scale well with the increased degree of freedom found in complex non-rigid deformations or shape variations. Moreover, in general, the degree of freedom of deformation is assumed to be known in advance, which limits the applicability of non-rigid structure from motion algorithms in a practical situation. In this paper, we propose a method for handling complex shape variations based on the assumption that complex shape variations can be represented probabilistically by a mixture of primitive shape variations. The proposed model is a generative probabilistic model, called a Procrustean normal distribution mixture model, which can model complex shape variations without rank constraints. Experimental results show that the proposed method significantly outperforms existing methods.
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