Finding vanishing points via point alignments in image primal and dual domains

José LEZAMA; Rafael GROMPONE VON GIOI; Gregory RANDALL; Jean-Michel MOREL

doi:10.1109/CVPR.2014.72

Finding vanishing points via point alignments in image primal and dual domains

José LEZAMA^*, Rafael GROMPONE VON GIOI, Gregory RANDALL, Jean-Michel MOREL

^*Corresponding author for this work

Research output: Book Chapters | Papers in Conference Proceedings › Conference paper (refereed) › Research › peer-review

84 Citations (Scopus)

Abstract

We present a novel method for automatic vanishing point detection based on primal and dual point alignment detection. The very same point alignment detection algorithm is used twice: First in the image domain to group line segment endpoints into more precise lines. Second, it is used in the dual domain where converging lines become aligned points. The use of the recently introduced PClines dual spaces and a robust point alignment detector leads to a very accurate algorithm. Experimental results on two public standard datasets show that our method significantly advances the state-of-the-art in the Manhattan world scenario, while producing state-of-the-art performances in non-Manhattan scenes.

Original language	English
Title of host publication	Proceedings of the 2014 IEEE Computer Society Conference on Computer Vision and Pattern Recognition
Publisher	IEEE
Pages	509-515
Number of pages	7
ISBN (Electronic)	9781479951185
DOIs	https://doi.org/10.1109/CVPR.2014.72
Publication status	Published - 2014
Externally published	Yes
Event	27th IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2014 - Columbus, United States Duration: 23 Jun 2014 → 28 Jun 2014

Publication series

Name	Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition
ISSN (Print)	1063-6919

Conference

Conference	27th IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2014
Country/Territory	United States
City	Columbus
Period	23/06/14 → 28/06/14

Bibliographical note

Publisher Copyright:
© 2014 IEEE.

Keywords

2d point alignments
line-to-point mapping
vanishing point detection

Access to Document

10.1109/CVPR.2014.72

Cite this

LEZAMA, J., GROMPONE VON GIOI, R., RANDALL, G., & MOREL, J.-M. (2014). Finding vanishing points via point alignments in image primal and dual domains. In Proceedings of the 2014 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (pp. 509-515). Article 6909466 (Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition). IEEE. https://doi.org/10.1109/CVPR.2014.72

LEZAMA, José ; GROMPONE VON GIOI, Rafael ; RANDALL, Gregory et al. / Finding vanishing points via point alignments in image primal and dual domains. Proceedings of the 2014 IEEE Computer Society Conference on Computer Vision and Pattern Recognition. IEEE, 2014. pp. 509-515 (Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition).

@inproceedings{3ba4b5e0cdb14e5aa03dc19576b35194,

title = "Finding vanishing points via point alignments in image primal and dual domains",

abstract = "We present a novel method for automatic vanishing point detection based on primal and dual point alignment detection. The very same point alignment detection algorithm is used twice: First in the image domain to group line segment endpoints into more precise lines. Second, it is used in the dual domain where converging lines become aligned points. The use of the recently introduced PClines dual spaces and a robust point alignment detector leads to a very accurate algorithm. Experimental results on two public standard datasets show that our method significantly advances the state-of-the-art in the Manhattan world scenario, while producing state-of-the-art performances in non-Manhattan scenes.",

keywords = "2d point alignments, line-to-point mapping, vanishing point detection",

author = "Jos{\'e} LEZAMA and \{GROMPONE VON GIOI\}, Rafael and Gregory RANDALL and Jean-Michel MOREL",

note = "Publisher Copyright: {\textcopyright} 2014 IEEE.; 27th IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2014 ; Conference date: 23-06-2014 Through 28-06-2014",

year = "2014",

doi = "10.1109/CVPR.2014.72",

language = "English",

series = "Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition",

publisher = "IEEE",

pages = "509--515",

booktitle = "Proceedings of the 2014 IEEE Computer Society Conference on Computer Vision and Pattern Recognition",

}

LEZAMA, J, GROMPONE VON GIOI, R, RANDALL, G & MOREL, J-M 2014, Finding vanishing points via point alignments in image primal and dual domains. in Proceedings of the 2014 IEEE Computer Society Conference on Computer Vision and Pattern Recognition., 6909466, Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, IEEE, pp. 509-515, 27th IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2014, Columbus, United States, 23/06/14. https://doi.org/10.1109/CVPR.2014.72

Finding vanishing points via point alignments in image primal and dual domains. / LEZAMA, José; GROMPONE VON GIOI, Rafael; RANDALL, Gregory et al.
Proceedings of the 2014 IEEE Computer Society Conference on Computer Vision and Pattern Recognition. IEEE, 2014. p. 509-515 6909466 (Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition).

Research output: Book Chapters | Papers in Conference Proceedings › Conference paper (refereed) › Research › peer-review

TY - GEN

T1 - Finding vanishing points via point alignments in image primal and dual domains

AU - LEZAMA, José

AU - GROMPONE VON GIOI, Rafael

AU - RANDALL, Gregory

AU - MOREL, Jean-Michel

PY - 2014

Y1 - 2014

N2 - We present a novel method for automatic vanishing point detection based on primal and dual point alignment detection. The very same point alignment detection algorithm is used twice: First in the image domain to group line segment endpoints into more precise lines. Second, it is used in the dual domain where converging lines become aligned points. The use of the recently introduced PClines dual spaces and a robust point alignment detector leads to a very accurate algorithm. Experimental results on two public standard datasets show that our method significantly advances the state-of-the-art in the Manhattan world scenario, while producing state-of-the-art performances in non-Manhattan scenes.

AB - We present a novel method for automatic vanishing point detection based on primal and dual point alignment detection. The very same point alignment detection algorithm is used twice: First in the image domain to group line segment endpoints into more precise lines. Second, it is used in the dual domain where converging lines become aligned points. The use of the recently introduced PClines dual spaces and a robust point alignment detector leads to a very accurate algorithm. Experimental results on two public standard datasets show that our method significantly advances the state-of-the-art in the Manhattan world scenario, while producing state-of-the-art performances in non-Manhattan scenes.

KW - 2d point alignments

KW - line-to-point mapping

KW - vanishing point detection

UR - https://www.scopus.com/pages/publications/84911448925

U2 - 10.1109/CVPR.2014.72

DO - 10.1109/CVPR.2014.72

M3 - Conference paper (refereed)

AN - SCOPUS:84911448925

T3 - Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition

SP - 509

EP - 515

BT - Proceedings of the 2014 IEEE Computer Society Conference on Computer Vision and Pattern Recognition

PB - IEEE

T2 - 27th IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2014

Y2 - 23 June 2014 through 28 June 2014

ER -

LEZAMA J, GROMPONE VON GIOI R, RANDALL G, MOREL JM. Finding vanishing points via point alignments in image primal and dual domains. In Proceedings of the 2014 IEEE Computer Society Conference on Computer Vision and Pattern Recognition. IEEE. 2014. p. 509-515. 6909466. (Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition). doi: 10.1109/CVPR.2014.72

Finding vanishing points via point alignments in image primal and dual domains

Abstract

Publication series

Conference

Bibliographical note

Keywords

Access to Document

Other files and links

Fingerprint

Cite this