Authors:
Junjie Hu
and
Terumasa Aoki
Affiliation:
Tohoku University, Japan
Keyword(s):
Non-rigid Structure From Motion, Sparse Representation, l1-norm Minimization.
Related
Ontology
Subjects/Areas/Topics:
Applications
;
Computer Vision, Visualization and Computer Graphics
;
Geometry and Modeling
;
Image-Based Modeling
;
Motion, Tracking and Stereo Vision
;
Pattern Recognition
;
Software Engineering
;
Stereo Vision and Structure from Motion
Abstract:
This paper presents a convex solution for simultaneously recovering 3D non-rigid structures and camera motions
from 2D image sequences based on sparse representation. Most existing methods rely on low rank assumption.
However, it will lead to poor reconstruction for objects with strong local deformation. Also, when
camera motion is unknown, there is no convex solution for non-rigid structure from motion (NRSfM). In order
to solve this problem, we estimate non-rigid structures by sparse representation. In this paper, we estimate
camera motions through a sparse spectral-norm minimization approach, and then a fast l1-norm minimization
algorithm is introduced to reconstruct 3D structures. Both of them are convex, therefore, our method gives
a global optimum. Our method can handle objects with strong local deformation and also doesn’t need low
rank prior. Experimental results show that our method achieves state-of-the-art reconstruction performance on
CMU benchmark dataset.