TY - GEN
T1 - Spatio-temporally consistent correspondence for dense dynamic scene modeling
AU - Ji, Dinghuang
AU - Dunn, Enrique
AU - Frahm, Jan Michael
N1 - Publisher Copyright:
© Springer International Publishing AG 2016.
PY - 2016
Y1 - 2016
N2 - We address the problem of robust two-view correspondence estimation within the context of dynamic scene modeling. To this end, we investigate the use of local spatio-temporal assumptions to both identify and refine dense low-level data associations in the absence of prior dynamic content models. By developing a strictly data-driven approach to correspondence search, based on bottom-up local 3D motion cues of local rigidity and non-local coherence, we are able to robustly address the higher-order problems of video synchronization and dynamic surface modeling. Our findings suggest an important relationship between these two tasks, in that maximizing spatial coherence of surface points serves as a direct metric for the temporal alignment of local image sequences.The obtained results for these two problems on multiple publicly available dynamic reconstruction datasets illustrate both the effectiveness and generality of our proposed approach.
AB - We address the problem of robust two-view correspondence estimation within the context of dynamic scene modeling. To this end, we investigate the use of local spatio-temporal assumptions to both identify and refine dense low-level data associations in the absence of prior dynamic content models. By developing a strictly data-driven approach to correspondence search, based on bottom-up local 3D motion cues of local rigidity and non-local coherence, we are able to robustly address the higher-order problems of video synchronization and dynamic surface modeling. Our findings suggest an important relationship between these two tasks, in that maximizing spatial coherence of surface points serves as a direct metric for the temporal alignment of local image sequences.The obtained results for these two problems on multiple publicly available dynamic reconstruction datasets illustrate both the effectiveness and generality of our proposed approach.
KW - Motion consistency
KW - Two-View correspondences
UR - http://www.scopus.com/inward/record.url?scp=84990052983&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=84990052983&partnerID=8YFLogxK
U2 - 10.1007/978-3-319-46466-4_1
DO - 10.1007/978-3-319-46466-4_1
M3 - Conference contribution
AN - SCOPUS:84990052983
SN - 9783319464657
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 3
EP - 18
BT - Computer Vision - 14th European Conference, ECCV 2016, Proceedings
A2 - Leibe, Bastian
A2 - Matas, Jiri
A2 - Sebe, Nicu
A2 - Welling, Max
T2 - 14th European Conference on Computer Vision, ECCV 2016
Y2 - 8 October 2016 through 16 October 2016
ER -