Semantic Segmentation for 3D Localization in Urban Environments

Anil Armagan; Martin Hirzer; Vincent Lepetit

Semantic Segmentation for 3D Localization in Urban Environments

Anil Armagan, Martin Hirzer, Vincent Lepetit

Institute of Computer Graphics and Vision (7100)

Research output: Chapter in Book/Report/Conference proceeding › Conference paper › peer-review

Abstract

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.

Original language	English
Title of host publication	Proceedings of the Joint Urban Remote Sensing Event (JURSE)
Publication status	Published - 2017
Event	Joint Urban Remote Sensing Event 2017 - Dubai, United Arab Emirates Duration: 6 Mar 2017 → 8 Mar 2017

Conference

Conference	Joint Urban Remote Sensing Event 2017
Abbreviated title	JURSE 2017
Country/Territory	United Arab Emirates
City	Dubai
Period	6/03/17 → 8/03/17

Best Paper Award at JURSE 2017
Armagan, Anil (Recipient), Hirzer, Martin (Recipient) & Lepetit, Vincent (Recipient), 8 Mar 2017
Prize: Prizes / Medals / Awards

Cite this

@inproceedings{8df12c938ae14817b66a4b4b4e82d0e8,

title = "Semantic Segmentation for 3D Localization in Urban Environments",

abstract = "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 fa{\c c}ades 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.",

author = "Anil Armagan and Martin Hirzer and Vincent Lepetit",

year = "2017",

language = "English",

booktitle = "Proceedings of the Joint Urban Remote Sensing Event (JURSE)",

note = "Joint Urban Remote Sensing Event 2017, JURSE 2017 ; Conference date: 06-03-2017 Through 08-03-2017",

}

TY - GEN

T1 - Semantic Segmentation for 3D Localization in Urban Environments

AU - Armagan, Anil

AU - Hirzer, Martin

AU - Lepetit, Vincent

PY - 2017

Y1 - 2017

N2 - 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.

AB - 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.

M3 - Conference paper

BT - Proceedings of the Joint Urban Remote Sensing Event (JURSE)

T2 - Joint Urban Remote Sensing Event 2017

Y2 - 6 March 2017 through 8 March 2017

ER -

Semantic Segmentation for 3D Localization in Urban Environments

Abstract

Conference

Fingerprint

Prizes

Best Paper Award at JURSE 2017

Cite this