Accurate Camera Registration in Urban Environments Using High-Level Feature Matching

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

We propose a method for accurate camera pose estimation in urban environments from single images and 2D maps made of the surrounding buildings’ outlines. Our approach bridges the gap between learning-based approaches and geometric approaches: We use recent semantic segmentation techniques for extracting the buildings’ edges and the façades’ normals in the images and minimal solvers [14] to compute the camera pose accurately and robustly. We propose two such minimal solvers: one based on three correspondences of buildings’ corners from the image and the 2D map and another one based on two corner correspondences plus one façade correspondence. We show on a challenging dataset that, compared to recent state-of-the-art [1], this approach is both, faster and more accurate.
LanguageEnglish
Title of host publicationProceedings of the British Machine Vision Conference
StatusPublished - 2017
EventBritish Machine Vision Conference 2017 - London, United Kingdom
Duration: 4 Sep 20177 Sep 2017

Conference

ConferenceBritish Machine Vision Conference 2017
Abbreviated titleBMVC 2017
CountryUnited Kingdom
CityLondon
Period4/09/177/09/17

Fingerprint

Cameras
Bridge approaches
Facades
Semantics

Cite this

Accurate Camera Registration in Urban Environments Using High-Level Feature Matching. / Armagan, Anil; Hirzer, Martin; Roth, Peter M.; Lepetit, Vincent.

Proceedings of the British Machine Vision Conference. 2017.

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Armagan, A, Hirzer, M, Roth, PM & Lepetit, V 2017, Accurate Camera Registration in Urban Environments Using High-Level Feature Matching. in Proceedings of the British Machine Vision Conference. British Machine Vision Conference 2017, London, United Kingdom, 4/09/17.
@inproceedings{0a5d1c54f48e4877ac7a296b90eae54c,
title = "Accurate Camera Registration in Urban Environments Using High-Level Feature Matching",
abstract = "We propose a method for accurate camera pose estimation in urban environments from single images and 2D maps made of the surrounding buildings’ outlines. Our approach bridges the gap between learning-based approaches and geometric approaches: We use recent semantic segmentation techniques for extracting the buildings’ edges and the fa{\cc}ades’ normals in the images and minimal solvers [14] to compute the camera pose accurately and robustly. We propose two such minimal solvers: one based on three correspondences of buildings’ corners from the image and the 2D map and another one based on two corner correspondences plus one fa{\cc}ade correspondence. We show on a challenging dataset that, compared to recent state-of-the-art [1], this approach is both, faster and more accurate.",
author = "Anil Armagan and Martin Hirzer and Roth, {Peter M.} and Vincent Lepetit",
year = "2017",
language = "English",
booktitle = "Proceedings of the British Machine Vision Conference",

}

TY - GEN

T1 - Accurate Camera Registration in Urban Environments Using High-Level Feature Matching

AU - Armagan,Anil

AU - Hirzer,Martin

AU - Roth,Peter M.

AU - Lepetit,Vincent

PY - 2017

Y1 - 2017

N2 - We propose a method for accurate camera pose estimation in urban environments from single images and 2D maps made of the surrounding buildings’ outlines. Our approach bridges the gap between learning-based approaches and geometric approaches: We use recent semantic segmentation techniques for extracting the buildings’ edges and the façades’ normals in the images and minimal solvers [14] to compute the camera pose accurately and robustly. We propose two such minimal solvers: one based on three correspondences of buildings’ corners from the image and the 2D map and another one based on two corner correspondences plus one façade correspondence. We show on a challenging dataset that, compared to recent state-of-the-art [1], this approach is both, faster and more accurate.

AB - We propose a method for accurate camera pose estimation in urban environments from single images and 2D maps made of the surrounding buildings’ outlines. Our approach bridges the gap between learning-based approaches and geometric approaches: We use recent semantic segmentation techniques for extracting the buildings’ edges and the façades’ normals in the images and minimal solvers [14] to compute the camera pose accurately and robustly. We propose two such minimal solvers: one based on three correspondences of buildings’ corners from the image and the 2D map and another one based on two corner correspondences plus one façade correspondence. We show on a challenging dataset that, compared to recent state-of-the-art [1], this approach is both, faster and more accurate.

M3 - Conference contribution

BT - Proceedings of the British Machine Vision Conference

ER -