Robust and accurate line- and/or point-based pose estimation without manhattan assumptions

20Citations
Citations of this article
34Readers
Mendeley users who have this article in their library.

This article is free to access.

Abstract

Usual Structure from Motion techniques based on feature points have a hard time on scenes with little texture or presenting a single plane, as in indoor environments. Line segments are more robust features in this case. We propose a novel geometrical criterion for two-view pose estimation using lines, that does not assume a Manhattan world. We also define a parameterless (a contrario) RANSAC-like method to discard calibration outliers and provide more robust pose estimations, possibly using points as well when available. Finally, we provide quantitative experimental data that illustrate failure cases of other methods and that show how our approach outperforms them, both in robustness and precision.

References Powered by Scopus

Distinctive image features from scale-invariant keypoints

49451Citations
N/AReaders
Get full text

An efficient solution to the five-point relative pose problem

1730Citations
N/AReaders
Get full text

A generalized solution of the orthogonal procrustes problem

1487Citations
N/AReaders
Get full text

Cited by Powered by Scopus

PPGNET: Learning point-pair graph for line segment detection

67Citations
N/AReaders
Get full text

Minimal Case Relative Pose Computation Using Ray-Point-Ray Features

18Citations
N/AReaders
Get full text

Line-Based Robust SfM with Little Image Overlap

17Citations
N/AReaders
Get full text

Register to see more suggestions

Mendeley helps you to discover research relevant for your work.

Already have an account?

Cite

CITATION STYLE

APA

Salaün, Y., Marlet, R., & Monasse, P. (2016). Robust and accurate line- and/or point-based pose estimation without manhattan assumptions. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 9911 LNCS, pp. 801–818). Springer Verlag. https://doi.org/10.1007/978-3-319-46478-7_49

Readers' Seniority

Tooltip

PhD / Post grad / Masters / Doc 22

85%

Researcher 3

12%

Lecturer / Post doc 1

4%

Readers' Discipline

Tooltip

Computer Science 18

67%

Engineering 7

26%

Biochemistry, Genetics and Molecular Bi... 1

4%

Social Sciences 1

4%

Save time finding and organizing research with Mendeley

Sign up for free