TY - GEN
T1 - Convex approach for non-rigid structure from motion via sparse representation
AU - Hu, Junjie
AU - Aoki, Terumasa
PY - 2017/1/1
Y1 - 2017/1/1
N2 - 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.
AB - 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.
KW - L1-norm Minimization
KW - Non-rigid Structure From Motion
KW - Sparse Representation
UR - http://www.scopus.com/inward/record.url?scp=85045310286&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85045310286&partnerID=8YFLogxK
M3 - Conference contribution
AN - SCOPUS:85045310286
T3 - VISIGRAPP 2017 - Proceedings of the 12th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications
SP - 333
EP - 339
BT - VISAPP
A2 - Braz, Jose
A2 - Tremeau, Alain
A2 - Imai, Francisco
PB - SciTePress
T2 - 12th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications, VISIGRAPP 2017
Y2 - 27 February 2017 through 1 March 2017
ER -