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|a 10.1109/TPAMI.2017.2769655
|2 doi
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|a eng
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| 100 |
1 |
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|a Gallego, Guillermo
|e verfasserin
|4 aut
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| 245 |
1 |
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|a Event-Based, 6-DOF Camera Tracking from Photometric Depth Maps
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| 264 |
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|c 2018
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|a Text
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|a ƒaComputermedien
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|a Date Revised 20.11.2019
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|a published: Print-Electronic
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|a Citation Status PubMed-not-MEDLINE
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| 520 |
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|a 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 inaccessible to standard cameras
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| 650 |
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|a Journal Article
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| 650 |
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|a Research Support, Non-U.S. Gov't
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| 700 |
1 |
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|a Lund, Jon E A
|e verfasserin
|4 aut
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| 700 |
1 |
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|a Mueggler, Elias
|e verfasserin
|4 aut
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| 700 |
1 |
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|a Rebecq, Henri
|e verfasserin
|4 aut
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| 700 |
1 |
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|a Delbruck, Tobi
|e verfasserin
|4 aut
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| 700 |
1 |
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|a Scaramuzza, Davide
|e verfasserin
|4 aut
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| 773 |
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|i Enthalten in
|t IEEE transactions on pattern analysis and machine intelligence
|d 1979
|g 40(2018), 10 vom: 28. Okt., Seite 2402-2412
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|g volume:40
|g year:2018
|g number:10
|g day:28
|g month:10
|g pages:2402-2412
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|u http://dx.doi.org/10.1109/TPAMI.2017.2769655
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