Optimal algorithms in multiview geometry

Richard Hartley; Fredrik Kahl

doi:10.1007/978-3-540-76386-4_2

Optimal algorithms in multiview geometry

Richard Hartley^*, Fredrik Kahl

^*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceeding › Conference Paper › peer-review

72 Citations (Scopus)

Abstract

This is a survey paper summarizing recent research aimed at finding guaranteed optimal algorithms for solving problems in Multiview Geometry. Many of the traditional problems in Multiview Geometry now have optimal solutions in terms of minimizing residual imageplane error. Success has been achieved in minimizing L₂ (least-squares) or L_∞ (smallest maximum error) norm. The main methods involve Second Order Cone Programming, or quasi-convex optimization, and Branch-and-bound. The paper gives an overview of the subject while avoiding as far as possible the mathematical details, which can be found in the original papers.

Original language	English
Title of host publication	Computer Vision - ACCV 2007 - 8th Asian Conference on Computer Vision, Proceedings
Publisher	Springer Verlag
Pages	13-34
Number of pages	22
Edition	PART 1
ISBN (Print)	9783540763857
DOIs	https://doi.org/10.1007/978-3-540-76386-4_2
Publication status	Published - 2007
Event	8th Asian Conference on Computer Vision, ACCV 2007 - Tokyo, Japan Duration: 18 Nov 2007 → 22 Nov 2007

Publication series

Name	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Number	PART 1
Volume	4843 LNCS
ISSN (Print)	0302-9743
ISSN (Electronic)	1611-3349

Conference

Conference	8th Asian Conference on Computer Vision, ACCV 2007
Country/Territory	Japan
City	Tokyo
Period	18/11/07 → 22/11/07

Access to Document

10.1007/978-3-540-76386-4_2

Cite this

Hartley, R., & Kahl, F. (2007). Optimal algorithms in multiview geometry. In Computer Vision - ACCV 2007 - 8th Asian Conference on Computer Vision, Proceedings (PART 1 ed., pp. 13-34). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 4843 LNCS, No. PART 1). Springer Verlag. https://doi.org/10.1007/978-3-540-76386-4_2

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title = "Optimal algorithms in multiview geometry",

abstract = "This is a survey paper summarizing recent research aimed at finding guaranteed optimal algorithms for solving problems in Multiview Geometry. Many of the traditional problems in Multiview Geometry now have optimal solutions in terms of minimizing residual imageplane error. Success has been achieved in minimizing L2 (least-squares) or L∞ (smallest maximum error) norm. The main methods involve Second Order Cone Programming, or quasi-convex optimization, and Branch-and-bound. The paper gives an overview of the subject while avoiding as far as possible the mathematical details, which can be found in the original papers.",

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Hartley, R & Kahl, F 2007, Optimal algorithms in multiview geometry. in Computer Vision - ACCV 2007 - 8th Asian Conference on Computer Vision, Proceedings. PART 1 edn, Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), no. PART 1, vol. 4843 LNCS, Springer Verlag, pp. 13-34, 8th Asian Conference on Computer Vision, ACCV 2007, Tokyo, Japan, 18/11/07. https://doi.org/10.1007/978-3-540-76386-4_2

Optimal algorithms in multiview geometry. / Hartley, Richard; Kahl, Fredrik.
Computer Vision - ACCV 2007 - 8th Asian Conference on Computer Vision, Proceedings. PART 1. ed. Springer Verlag, 2007. p. 13-34 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 4843 LNCS, No. PART 1).

Research output: Chapter in Book/Report/Conference proceeding › Conference Paper › peer-review

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AB - This is a survey paper summarizing recent research aimed at finding guaranteed optimal algorithms for solving problems in Multiview Geometry. Many of the traditional problems in Multiview Geometry now have optimal solutions in terms of minimizing residual imageplane error. Success has been achieved in minimizing L2 (least-squares) or L∞ (smallest maximum error) norm. The main methods involve Second Order Cone Programming, or quasi-convex optimization, and Branch-and-bound. The paper gives an overview of the subject while avoiding as far as possible the mathematical details, which can be found in the original papers.

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T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

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ER -

Optimal algorithms in multiview geometry

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