Header

UZH-Logo

Maintenance Infos

Event-based, 6-DOF camera tracking from photometric depth maps


Gallego, Guillermo; Lund, Jon E A; Mueggler, Elias; Rebecq, Henri; Delbruck, Tobi; Scaramuzza, Davide (2017). Event-based, 6-DOF camera tracking from photometric depth maps. IEEE Transactions on Pattern Analysis and Machine Intelligence, PP(99):n/a.

Abstract

Event cameras are bio-inspired vision sensors that output pixel-level brightness changes instead of standard intensity frames. These cameras do not suffer from motion blur and have a very high dynamic range, which enables them to provide reliable visual information during high-speed motions or in scenes characterized by high dynamic range. These features, along with a very low power consumption, make event cameras an ideal complement to standard cameras for VR/AR and video game applications. With these applications in mind, this paper tackles the problem of accurate, low-latency tracking of an event camera from an existing photometric depth map (i.e., intensity plus depth information) built via classic dense reconstruction pipelines. Our approach tracks the 6-DOF pose of the event camera upon the arrival of each event, thus virtually eliminating latency. We successfully evaluate the method in both indoor and outdoor scenes and show that---because of the technological advantages of the event camera---our pipeline works in scenes characterized by high-speed motion, which are still unaccessible to standard cameras.

Abstract

Event cameras are bio-inspired vision sensors that output pixel-level brightness changes instead of standard intensity frames. These cameras do not suffer from motion blur and have a very high dynamic range, which enables them to provide reliable visual information during high-speed motions or in scenes characterized by high dynamic range. These features, along with a very low power consumption, make event cameras an ideal complement to standard cameras for VR/AR and video game applications. With these applications in mind, this paper tackles the problem of accurate, low-latency tracking of an event camera from an existing photometric depth map (i.e., intensity plus depth information) built via classic dense reconstruction pipelines. Our approach tracks the 6-DOF pose of the event camera upon the arrival of each event, thus virtually eliminating latency. We successfully evaluate the method in both indoor and outdoor scenes and show that---because of the technological advantages of the event camera---our pipeline works in scenes characterized by high-speed motion, which are still unaccessible to standard cameras.

Statistics

Citations

Dimensions.ai Metrics

Altmetrics

Additional indexing

Item Type:Journal Article, refereed, original work
Communities & Collections:07 Faculty of Science > Institute of Neuroinformatics
Dewey Decimal Classification:570 Life sciences; biology
Language:English
Date:2017
Deposited On:01 Mar 2018 12:38
Last Modified:21 Sep 2018 03:11
Publisher:Institute of Electrical and Electronics Engineers
Series Name:IEEE Transactions on Pattern Analysis and Machine Intelligence
ISSN:0098-5589
OA Status:Closed
Publisher DOI:https://doi.org/10.1109/TPAMI.2017.2769655

Download

Full text not available from this repository.
View at publisher

Get full-text in a library