TY - GEN
T1 - Sparse dynamic 3D reconstruction from unsynchronized videos
AU - Zheng, Enliang
AU - Ji, Dinghuang
AU - Dunn, Enrique
AU - Frahm, Jan Michael
N1 - Publisher Copyright:
© 2015 IEEE.
PY - 2015/2/17
Y1 - 2015/2/17
N2 - We target the sparse 3D reconstruction of dynamic objects observed by multiple unsynchronized video cameras with unknown temporal overlap. To this end, we develop a framework to recover the unknown structure without sequencing information across video sequences. Our proposed compressed sensing framework poses the estimation of 3D structure as the problem of dictionary learning. Moreover, we define our dictionary as the temporally varying 3D structure, while we define local sequencing information in terms of the sparse coefficients describing a locally linear 3D structural interpolation. Our formulation optimizes a biconvex cost function that leverages a compressed sensing formulation and enforces both structural dependency coherence across video streams, as well as motion smoothness across estimates from common video sources. Experimental results demonstrate the effectiveness of our approach in both synthetic data and captured imagery.
AB - We target the sparse 3D reconstruction of dynamic objects observed by multiple unsynchronized video cameras with unknown temporal overlap. To this end, we develop a framework to recover the unknown structure without sequencing information across video sequences. Our proposed compressed sensing framework poses the estimation of 3D structure as the problem of dictionary learning. Moreover, we define our dictionary as the temporally varying 3D structure, while we define local sequencing information in terms of the sparse coefficients describing a locally linear 3D structural interpolation. Our formulation optimizes a biconvex cost function that leverages a compressed sensing formulation and enforces both structural dependency coherence across video streams, as well as motion smoothness across estimates from common video sources. Experimental results demonstrate the effectiveness of our approach in both synthetic data and captured imagery.
UR - http://www.scopus.com/inward/record.url?scp=84973882965&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=84973882965&partnerID=8YFLogxK
U2 - 10.1109/ICCV.2015.504
DO - 10.1109/ICCV.2015.504
M3 - Conference contribution
AN - SCOPUS:84973882965
T3 - Proceedings of the IEEE International Conference on Computer Vision
SP - 4435
EP - 4443
BT - 2015 International Conference on Computer Vision, ICCV 2015
T2 - 15th IEEE International Conference on Computer Vision, ICCV 2015
Y2 - 11 December 2015 through 18 December 2015
ER -