Bringing 3D models together: Mining video liaisons in crowdsourced reconstructions

Ke Wang, Enrique Dunn, Mikel Rodriguez, Jan Michael Frahm

Research output: Chapter in Book/Report/Conference proceedingChapter

Abstract

The recent advances in large-scale scene modeling have enabled the automatic 3D reconstruction of landmark sites from crowdsourced photo collections. Here, we address the challenge of leveraging crowdsourced video collections to identify connecting visual observations that enable the alignment and subsequent aggregation, of disjoint 3D models. We denote these connecting image sequences as video liaisons and develop a data-driven framework for fully unsupervised extraction and exploitation. Towards this end, we represent video contents in terms of a histogram representation of iconic imagery contained within existing 3D models attained from a photo collection. We then use this representation to efficiently identify and prioritize the analysis of individual videos within a large-scale video collection, in an effort to determine camera motion trajectories connecting different landmarks. Results on crowdsourced data illustrate the efficiency and effectiveness of our proposed approach.

LanguageEnglish (US)
Title of host publicationComputer Vision - 13th Asian Conference on Computer Vision, ACCV 2016, Revised Selected Papers
PublisherSpringer Verlag
Pages408-423
Number of pages16
ISBN (Print)9783319541891
DOIs
StatePublished - Jan 1 2017

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume10114 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Fingerprint

Landmarks
3D Model
Mining
3D Reconstruction
Image Sequence
Data-driven
Exploitation
Histogram
Aggregation
Disjoint
Alignment
Agglomeration
Camera
Cameras
Trajectories
Trajectory
Denote
Motion
Modeling
Vision

ASJC Scopus subject areas

  • Theoretical Computer Science
  • Computer Science(all)

Cite this

Wang, K., Dunn, E., Rodriguez, M., & Frahm, J. M. (2017). Bringing 3D models together: Mining video liaisons in crowdsourced reconstructions. In Computer Vision - 13th Asian Conference on Computer Vision, ACCV 2016, Revised Selected Papers (pp. 408-423). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 10114 LNCS). Springer Verlag. DOI: 10.1007/978-3-319-54190-7_25

Bringing 3D models together : Mining video liaisons in crowdsourced reconstructions. / Wang, Ke; Dunn, Enrique; Rodriguez, Mikel; Frahm, Jan Michael.

Computer Vision - 13th Asian Conference on Computer Vision, ACCV 2016, Revised Selected Papers. Springer Verlag, 2017. p. 408-423 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 10114 LNCS).

Research output: Chapter in Book/Report/Conference proceedingChapter

Wang, K, Dunn, E, Rodriguez, M & Frahm, JM 2017, Bringing 3D models together: Mining video liaisons in crowdsourced reconstructions. in Computer Vision - 13th Asian Conference on Computer Vision, ACCV 2016, Revised Selected Papers. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 10114 LNCS, Springer Verlag, pp. 408-423. DOI: 10.1007/978-3-319-54190-7_25
Wang K, Dunn E, Rodriguez M, Frahm JM. Bringing 3D models together: Mining video liaisons in crowdsourced reconstructions. In Computer Vision - 13th Asian Conference on Computer Vision, ACCV 2016, Revised Selected Papers. Springer Verlag. 2017. p. 408-423. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)). Available from, DOI: 10.1007/978-3-319-54190-7_25
Wang, Ke ; Dunn, Enrique ; Rodriguez, Mikel ; Frahm, Jan Michael. / Bringing 3D models together : Mining video liaisons in crowdsourced reconstructions. Computer Vision - 13th Asian Conference on Computer Vision, ACCV 2016, Revised Selected Papers. Springer Verlag, 2017. pp. 408-423 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)).
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