Event-based, 6-DOF Camera Tracking from Photometric Depth Maps
July 12, 2016 Β· Declared Dead Β· π IEEE Transactions on Pattern Analysis and Machine Intelligence
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Authors
Guillermo Gallego, Jon E. A. Lund, Elias Mueggler, Henri Rebecq, Tobi Delbruck, Davide Scaramuzza
arXiv ID
1607.03468
Category
cs.CV: Computer Vision
Cross-listed
cs.RO
Citations
212
Venue
IEEE Transactions on Pattern Analysis and Machine Intelligence
Last Checked
3 months ago
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.
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