Our paper on 3D shape recovery of complex non-rigid objects is accepted to IJCV

[2015/09/21]

The following paper is accepted to International Journal of Computer Vision (IJCV):

  • Complex Non-Rigid 3D Shape Recovery Using a Procrustean Normal Distribution Mixture Model by Jungchan Cho, Minsik Lee, and Songhwai Oh

  • 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.