TY - GEN
T1 - 3D reconstruction of dynamic textures in crowd sourced data
AU - Ji, Dinghuang
AU - Dunn, Enrique
AU - Frahm, Jan Michael
PY - 2014
Y1 - 2014
N2 - We propose a framework to automatically build 3D models for scenes containing structures not amenable for photo-consistency based reconstruction due to having dynamic appearance. We analyze the dynamic appearance elements of a given scene by leveraging the imagery contained in Internet image photo-collections and online video sharing websites. Our approach combines large scale crowd sourced SfM techniques with image content segmentation and shape from silhouette techniques to build an iterative framework for 3D shape estimation. The developed system not only enables more complete and robust 3D modeling, but it also enables more realistic visualizations through the identification of dynamic scene elements amenable to dynamic texture mapping. Experiments on crowd sourced image and video datasets illustrate the effectiveness of our automated data-driven approach.
AB - We propose a framework to automatically build 3D models for scenes containing structures not amenable for photo-consistency based reconstruction due to having dynamic appearance. We analyze the dynamic appearance elements of a given scene by leveraging the imagery contained in Internet image photo-collections and online video sharing websites. Our approach combines large scale crowd sourced SfM techniques with image content segmentation and shape from silhouette techniques to build an iterative framework for 3D shape estimation. The developed system not only enables more complete and robust 3D modeling, but it also enables more realistic visualizations through the identification of dynamic scene elements amenable to dynamic texture mapping. Experiments on crowd sourced image and video datasets illustrate the effectiveness of our automated data-driven approach.
UR - http://www.scopus.com/inward/record.url?scp=84906483035&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=84906483035&partnerID=8YFLogxK
U2 - 10.1007/978-3-319-10590-1_10
DO - 10.1007/978-3-319-10590-1_10
M3 - Conference contribution
AN - SCOPUS:84906483035
SN - 9783319105895
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 143
EP - 158
BT - Computer Vision, ECCV 2014 - 13th European Conference, Proceedings
T2 - 13th European Conference on Computer Vision, ECCV 2014
Y2 - 6 September 2014 through 12 September 2014
ER -