TY - JOUR
T1 - 3D Shape Estimation with an Enhanced Sparse Representation Approach
AU - Wang, Jia Xiang
AU - Sun, Zhan Li
AU - Zeng, Zhi Gang
AU - Lam, Kin Man
N1 - Funding Information:
Manuscript received June 22, 2021; revised August 7, 2021; accepted August 7, 2021. Date of publication August 11, 2021; date of current version August 31, 2021. This work was supported by the National Natural Science Foundation of China under Grant 61972002, in part by the Open Project of Anhui Provincial Key Laboratory of Multimodal Cognitive Computation, Anhui University (No. MMC202004), and in part by the High-performance Computing Platform of Anhui University for providing computing resources. The associate editor coordinating the review of this manuscript and approving it for publication was Dr. Lu Gan. (Corresponding author: Zhan-Li Sun.) Jia-Xiang Wang and Zhan-Li Sun are with the School of Artificial Intelligence, Anhui University, Hefei 230000, China (e-mail: [email protected]; [email protected]).
Publisher Copyright:
© 1994-2012 IEEE.
PY - 2021/8
Y1 - 2021/8
N2 - In this paper, an enhanced sparse representation approach is proposed to estimate the 3D shapes of objects in 2D image sequences. In the proposed method, the unknown 3D shape is estimated via a two-stage scheme, namely the main 3D shape estimation stage and the compensatory 3D shape estimation stage. Moreover, a reweighted sparse representation model is constructed to extract the shape bases for each estimation stage. In the sparse model, a reweighted constraint is enforced to enhance the coefficient sparsity of the shape bases. Experimental results on the well-known CMU image sequences demonstrate the effectiveness and feasibility of the proposed approach.
AB - In this paper, an enhanced sparse representation approach is proposed to estimate the 3D shapes of objects in 2D image sequences. In the proposed method, the unknown 3D shape is estimated via a two-stage scheme, namely the main 3D shape estimation stage and the compensatory 3D shape estimation stage. Moreover, a reweighted sparse representation model is constructed to extract the shape bases for each estimation stage. In the sparse model, a reweighted constraint is enforced to enhance the coefficient sparsity of the shape bases. Experimental results on the well-known CMU image sequences demonstrate the effectiveness and feasibility of the proposed approach.
KW - 3D reconstruction
KW - Non-rigid structure from motion
KW - sparse representation model
UR - http://www.scopus.com/inward/record.url?scp=85114648123&partnerID=8YFLogxK
U2 - 10.1109/LSP.2021.3104088
DO - 10.1109/LSP.2021.3104088
M3 - Journal article
AN - SCOPUS:85114648123
SN - 1070-9908
VL - 28
SP - 1685
EP - 1688
JO - IEEE Signal Processing Letters
JF - IEEE Signal Processing Letters
M1 - 9511830
ER -