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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| Vydáno v: | International journal of computer vision Ročník 126; číslo 12; s. 1394 - 1414 |
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| Hlavní autoři: | , , , |
| Médium: | Journal Article |
| Jazyk: | angličtina |
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New York
Springer US
01.12.2018
Springer Nature B.V |
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| ISSN: | 0920-5691, 1573-1405 |
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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. |
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| 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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| Copyright | Springer Science+Business Media, LLC 2017 International Journal of Computer Vision is a copyright of Springer, (2017). All Rights Reserved. |
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| Title | EMVS: Event-Based Multi-View Stereo—3D Reconstruction with an Event Camera in Real-Time |
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