3D Localization in Urban Environments from Single Images

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

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

Abstract

In this paper, we tackle the problem of geolocalization in urban environments overcoming the limitations in terms of accuracy of sensors like GPS, compass and accelerometer. For that purpose, we adopt recent findings in image segmentation and machine learning and combine them with the valuable information given by 2.5D maps of buildings. In particular, we first extract the façades of buildings and their edges and use this information to estimate the orientation and location that best align an input image to a 3D rendering of the given 2.5D map. As this step builds on a learned semantic segmentation procedure, rich training data is required. Thus, we also discuss how the required training data can be efficiently generated via a 3D tracking system.
Originalspracheenglisch
TitelProceedings of the OAGM/AAPR & ARW Joint Workshop (OAGM/AAPR & ARW)
PublikationsstatusVeröffentlicht - 2017
VeranstaltungOAGM/AAPR & ARW Joint Workshop 2017 - Wien, Österreich
Dauer: 10 Mai 201712 Mai 2017

Konferenz

KonferenzOAGM/AAPR & ARW Joint Workshop 2017
KurztitelOAGM/AAPR ARW 2017
LandÖsterreich
OrtWien
Zeitraum10/05/1712/05/17

Fingerprint Untersuchen Sie die Forschungsthemen von „3D Localization in Urban Environments from Single Images“. Zusammen bilden sie einen einzigartigen Fingerprint.

Dieses zitieren