EMVS: Event-Based Multi-View Stereo—3D Reconstruction with an Event Camera in Real-Time

Event cameras are bio-inspired vision sensors that output pixel-level brightness changes instead of standard intensity frames. They offer significant advantages over standard cameras, namely a very high dynamic range, no motion blur, and a latency in the order of microseconds. However, because the o...

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Published in:	International journal of computer vision Vol. 126; no. 12; pp. 1394 - 1414
Main Authors:	Rebecq, Henri, Gallego, Guillermo, Mueggler, Elias, Scaramuzza, Davide
Format:	Journal Article
Language:	English
Published:	New York Springer US 01.12.2018 Springer Nature B.V
Subjects:	Algorithms Artificial Intelligence Cameras Computer Imaging Computer Science Computer vision Image Processing and Computer Vision Image reconstruction Pattern Recognition Pattern Recognition and Graphics Real time Vision Multi-view stereo Event cameras 3D reconstruction Event-based vision
ISSN:	0920-5691, 1573-1405
Online Access:	Get full text
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Abstract	Event cameras are bio-inspired vision sensors that output pixel-level brightness changes instead of standard intensity frames. They offer significant advantages over standard cameras, namely a very high dynamic range, no motion blur, and a latency in the order of microseconds. However, because the output is composed of a sequence of asynchronous events rather than actual intensity images, traditional vision algorithms cannot be applied, so that a paradigm shift is needed. We introduce the problem of event-based multi-view stereo (EMVS) for event cameras and propose a solution to it. Unlike traditional MVS methods, which address the problem of estimating dense 3D structure from a set of known viewpoints, EMVS estimates semi-dense 3D structure from an event camera with known trajectory. Our EMVS solution elegantly exploits two inherent properties of an event camera: (1) its ability to respond to scene edges—which naturally provide semi-dense geometric information without any pre-processing operation—and (2) the fact that it provides continuous measurements as the sensor moves. Despite its simplicity (it can be implemented in a few lines of code), our algorithm is able to produce accurate, semi-dense depth maps, without requiring any explicit data association or intensity estimation. We successfully validate our method on both synthetic and real data. Our method is computationally very efficient and runs in real-time on a CPU.
AbstractList	Event cameras are bio-inspired vision sensors that output pixel-level brightness changes instead of standard intensity frames. They offer significant advantages over standard cameras, namely a very high dynamic range, no motion blur, and a latency in the order of microseconds. However, because the output is composed of a sequence of asynchronous events rather than actual intensity images, traditional vision algorithms cannot be applied, so that a paradigm shift is needed. We introduce the problem of event-based multi-view stereo (EMVS) for event cameras and propose a solution to it. Unlike traditional MVS methods, which address the problem of estimating dense 3D structure from a set of known viewpoints, EMVS estimates semi-dense 3D structure from an event camera with known trajectory. Our EMVS solution elegantly exploits two inherent properties of an event camera: (1) its ability to respond to scene edges—which naturally provide semi-dense geometric information without any pre-processing operation—and (2) the fact that it provides continuous measurements as the sensor moves. Despite its simplicity (it can be implemented in a few lines of code), our algorithm is able to produce accurate, semi-dense depth maps, without requiring any explicit data association or intensity estimation. We successfully validate our method on both synthetic and real data. Our method is computationally very efficient and runs in real-time on a CPU. Event cameras are bio-inspired vision sensors that output pixel-level brightness changes instead of standard intensity frames. They offer significant advantages over standard cameras, namely a very high dynamic range, no motion blur, and a latency in the order of microseconds. However, because the output is composed of a sequence of asynchronous events rather than actual intensity images, traditional vision algorithms cannot be applied, so that a paradigm shift is needed. We introduce the problem of event-based multi-view stereo (EMVS) for event cameras and propose a solution to it. Unlike traditional MVS methods, which address the problem of estimating dense 3D structure from a set of known viewpoints, EMVS estimates semi-dense 3D structure from an event camera with known trajectory. Our EMVS solution elegantly exploits two inherent properties of an event camera: (1) its ability to respond to scene edges—which naturally provide semi-dense geometric information without any pre-processing operation—and (2) the fact that it provides continuous measurements as the sensor moves. Despite its simplicity (it can be implemented in a few lines of code), our algorithm is able to produce accurate, semi-dense depth maps, without requiring any explicit data association or intensity estimation. We successfully validate our method on both synthetic and real data. Our method is computationally very efficient and runs in real-time on a CPU.
Author	Rebecq, Henri Gallego, Guillermo Mueggler, Elias Scaramuzza, Davide
Author_xml	– sequence: 1 givenname: Henri orcidid: 0000-0002-6577-9735 surname: Rebecq fullname: Rebecq, Henri email: rebecq@ifi.uzh.ch organization: Robotics and Perception Group, Department of Informatics, University of Zurich, Robotics and Perception Group, Department of Neuroinformatics, University of Zurich and ETH Zurich – sequence: 2 givenname: Guillermo orcidid: 0000-0002-2672-9241 surname: Gallego fullname: Gallego, Guillermo organization: Robotics and Perception Group, Department of Informatics, University of Zurich, Robotics and Perception Group, Department of Neuroinformatics, University of Zurich and ETH Zurich – sequence: 3 givenname: Elias orcidid: 0000-0002-8008-443X surname: Mueggler fullname: Mueggler, Elias organization: Robotics and Perception Group, Department of Informatics, University of Zurich, Robotics and Perception Group, Department of Neuroinformatics, University of Zurich and ETH Zurich – sequence: 4 givenname: Davide orcidid: 0000-0002-3831-6778 surname: Scaramuzza fullname: Scaramuzza, Davide organization: Robotics and Perception Group, Department of Informatics, University of Zurich, Robotics and Perception Group, Department of Neuroinformatics, University of Zurich and ETH Zurich
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Snippet	Event cameras are bio-inspired vision sensors that output pixel-level brightness changes instead of standard intensity frames. They offer significant...
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SubjectTerms	Algorithms Artificial Intelligence Cameras Computer Imaging Computer Science Computer vision Image Processing and Computer Vision Image reconstruction Pattern Recognition Pattern Recognition and Graphics Real time Vision
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Title	EMVS: Event-Based Multi-View Stereo—3D Reconstruction with an Event Camera in Real-Time
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