Sparse dynamic 3D reconstruction from unsynchronized videos

Enliang Zheng, Dinghuang Ji, Enrique Dunn, Jan Michael Frahm

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

20 Scopus citations

Abstract

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.

Original languageEnglish
Title of host publication2015 International Conference on Computer Vision, ICCV 2015
Pages4435-4443
Number of pages9
ISBN (Electronic)9781467383912
DOIs
StatePublished - 17 Feb 2015
Event15th IEEE International Conference on Computer Vision, ICCV 2015 - Santiago, Chile
Duration: 11 Dec 201518 Dec 2015

Publication series

NameProceedings of the IEEE International Conference on Computer Vision
Volume2015 International Conference on Computer Vision, ICCV 2015
ISSN (Print)1550-5499

Conference

Conference15th IEEE International Conference on Computer Vision, ICCV 2015
Country/TerritoryChile
CitySantiago
Period11/12/1518/12/15

Fingerprint

Dive into the research topics of 'Sparse dynamic 3D reconstruction from unsynchronized videos'. Together they form a unique fingerprint.

Cite this