Robust and Accurate Line- and/or Point-Based Pose Estimation without Manhattan Assumptions - École des Ponts ParisTech Access content directly
Conference Papers Year : 2016

Robust and Accurate Line- and/or Point-Based Pose Estimation without Manhattan Assumptions

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. Last, we provide quantitative experimental data that illustrate failure cases of other methods and that show how our approach outperforms them, both in robustness and accuracy.
Fichier principal
Vignette du fichier
eccv2016submission.pdf (963.52 Ko) Télécharger le fichier
Origin : Files produced by the author(s)
Loading...

Dates and versions

hal-01397813 , version 1 (16-11-2016)

Identifiers

Cite

Yohann Salaün, Renaud Marlet, Pascal Monasse. Robust and Accurate Line- and/or Point-Based Pose Estimation without Manhattan Assumptions. 14th European Conference on Computer Vision (ECCV 2016), Oct 2016, Amsterdam, Netherlands. pp.801 - 818, ⟨10.1007/978-3-319-46478-7_49⟩. ⟨hal-01397813⟩
293 View
483 Download

Altmetric

Share

Gmail Facebook X LinkedIn More