Semantic Segmentation for 3D Localization in Urban Environments

Anil Armagan, Martin Hirzer, Vincent Lepetit

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


We show how to use simple 2.5D maps of buildings and recent advances in image segmentation and machine learning to geo-localize an input image of an urban scene: We first extract the façades of the buildings and their edges from the image, and then look for the orientation and location that align a 3D rendering of the map with these segments. We discuss how to use a 3D tracking system to acquire the data required for training the segmentation method, the segmentation itself, and how we use the segmentations to evaluate the quality of the alignment.
TitelProceedings of the Joint Urban Remote Sensing Event (JURSE)
PublikationsstatusVeröffentlicht - 2017
VeranstaltungJoint Urban Remote Sensing Event 2017 - Dubai, Vereinigte Arabische Emirate
Dauer: 6 Mär 20178 Mär 2017


KonferenzJoint Urban Remote Sensing Event 2017
KurztitelJURSE 2017
Land/GebietVereinigte Arabische Emirate


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