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

Anil Armagan, Martin Hirzer, Peter M. Roth, Vincent Lepetit

Publikation: Beitrag in Buch/Bericht/KonferenzbandBeitrag in einem KonferenzbandBegutachtung


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.
TitelProceedings of the British Machine Vision Conference (BMVC)
PublikationsstatusVeröffentlicht - 2017
Veranstaltung28th British Machine Vision Conference: BMVC 2017 - London, Großbritannien / Vereinigtes Königreich
Dauer: 4 Sep 20177 Apr 2018


Konferenz28th British Machine Vision Conference
KurztitelBMVC 2017
Land/GebietGroßbritannien / Vereinigtes Königreich


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