Spatio-temporally consistent correspondence for dense dynamic scene modeling

Dinghuang Ji, Enrique Dunn, Jan Michael Frahm

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

5 Scopus citations

Abstract

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.

Original languageEnglish
Title of host publicationComputer Vision - 14th European Conference, ECCV 2016, Proceedings
EditorsBastian Leibe, Jiri Matas, Nicu Sebe, Max Welling
Pages3-18
Number of pages16
DOIs
StatePublished - 2016
Event14th European Conference on Computer Vision, ECCV 2016 - Amsterdam, Netherlands
Duration: 8 Oct 201616 Oct 2016

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume9910 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Conference

Conference14th European Conference on Computer Vision, ECCV 2016
Country/TerritoryNetherlands
CityAmsterdam
Period8/10/1616/10/16

Keywords

  • Motion consistency
  • Two-View correspondences

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