UPnP: An optimal O(n) solution to the absolute pose problem with universal applicability

Laurent Kneip; Hongdong Li; Yongduek Seo

doi:10.1007/978-3-319-10590-1_9

UPnP: An optimal O(n) solution to the absolute pose problem with universal applicability

Laurent Kneip, Hongdong Li, Yongduek Seo

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

152 Citations (Scopus)

Abstract

A large number of absolute pose algorithms have been presented in the literature. Common performance criteria are computational complexity, geometric optimality, global optimality, structural degeneracies, and the number of solutions. The ability to handle minimal sets of correspondences, resulting solution multiplicity, and generalized cameras are further desirable properties. This paper presents the first PnP solution that unifies all the above desirable properties within a single algorithm. We compare our result to state-of-the-art minimal, non-minimal, central, and non-central PnP algorithms, and demonstrate universal applicability, competitive noise resilience, and superior computational efficiency. Our algorithm is called Unified PnP (UPnP).

Original language	English
Title of host publication	Computer Vision, ECCV 2014 - 13th European Conference, Proceedings
Publisher	Springer Verlag
Pages	127-142
Number of pages	16
Edition	PART 1
ISBN (Print)	9783319105895
DOIs	https://doi.org/10.1007/978-3-319-10590-1_9
Publication status	Published - 2014
Event	13th European Conference on Computer Vision, ECCV 2014 - Zurich, Switzerland Duration: 6 Sept 2014 → 12 Sept 2014

Publication series

Name	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Number	PART 1
Volume	8689 LNCS
ISSN (Print)	0302-9743
ISSN (Electronic)	1611-3349

Conference

Conference	13th European Conference on Computer Vision, ECCV 2014
Country/Territory	Switzerland
City	Zurich
Period	6/09/14 → 12/09/14

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10.1007/978-3-319-10590-1_9

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Kneip, L., Li, H., & Seo, Y. (2014). UPnP: An optimal O(n) solution to the absolute pose problem with universal applicability. In Computer Vision, ECCV 2014 - 13th European Conference, Proceedings (PART 1 ed., pp. 127-142). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 8689 LNCS, No. PART 1). Springer Verlag. https://doi.org/10.1007/978-3-319-10590-1_9

Kneip, Laurent ; Li, Hongdong ; Seo, Yongduek. / UPnP : An optimal O(n) solution to the absolute pose problem with universal applicability. Computer Vision, ECCV 2014 - 13th European Conference, Proceedings. PART 1. ed. Springer Verlag, 2014. pp. 127-142 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); PART 1).

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title = "UPnP: An optimal O(n) solution to the absolute pose problem with universal applicability",

abstract = "A large number of absolute pose algorithms have been presented in the literature. Common performance criteria are computational complexity, geometric optimality, global optimality, structural degeneracies, and the number of solutions. The ability to handle minimal sets of correspondences, resulting solution multiplicity, and generalized cameras are further desirable properties. This paper presents the first PnP solution that unifies all the above desirable properties within a single algorithm. We compare our result to state-of-the-art minimal, non-minimal, central, and non-central PnP algorithms, and demonstrate universal applicability, competitive noise resilience, and superior computational efficiency. Our algorithm is called Unified PnP (UPnP).",

keywords = "DLS, Generalized absolute pose, Non-perspective PnP, PnP, geometric optimality, global optimality, linear complexity",

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Kneip, L, Li, H & Seo, Y 2014, UPnP: An optimal O(n) solution to the absolute pose problem with universal applicability. in Computer Vision, ECCV 2014 - 13th European Conference, 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. 8689 LNCS, Springer Verlag, pp. 127-142, 13th European Conference on Computer Vision, ECCV 2014, Zurich, Switzerland, 6/09/14. https://doi.org/10.1007/978-3-319-10590-1_9

UPnP: An optimal O(n) solution to the absolute pose problem with universal applicability. / Kneip, Laurent; Li, Hongdong; Seo, Yongduek.
Computer Vision, ECCV 2014 - 13th European Conference, Proceedings. PART 1. ed. Springer Verlag, 2014. p. 127-142 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 8689 LNCS, No. PART 1).

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

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N2 - A large number of absolute pose algorithms have been presented in the literature. Common performance criteria are computational complexity, geometric optimality, global optimality, structural degeneracies, and the number of solutions. The ability to handle minimal sets of correspondences, resulting solution multiplicity, and generalized cameras are further desirable properties. This paper presents the first PnP solution that unifies all the above desirable properties within a single algorithm. We compare our result to state-of-the-art minimal, non-minimal, central, and non-central PnP algorithms, and demonstrate universal applicability, competitive noise resilience, and superior computational efficiency. Our algorithm is called Unified PnP (UPnP).

AB - A large number of absolute pose algorithms have been presented in the literature. Common performance criteria are computational complexity, geometric optimality, global optimality, structural degeneracies, and the number of solutions. The ability to handle minimal sets of correspondences, resulting solution multiplicity, and generalized cameras are further desirable properties. This paper presents the first PnP solution that unifies all the above desirable properties within a single algorithm. We compare our result to state-of-the-art minimal, non-minimal, central, and non-central PnP algorithms, and demonstrate universal applicability, competitive noise resilience, and superior computational efficiency. Our algorithm is called Unified PnP (UPnP).

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KW - Generalized absolute pose

KW - Non-perspective PnP

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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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Kneip L, Li H, Seo Y. UPnP: An optimal O(n) solution to the absolute pose problem with universal applicability. In Computer Vision, ECCV 2014 - 13th European Conference, Proceedings. PART 1 ed. Springer Verlag. 2014. p. 127-142. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); PART 1). doi: 10.1007/978-3-319-10590-1_9

UPnP: An optimal O(n) solution to the absolute pose problem with universal applicability

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