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| Type | Conference or Workshop Paper |
| Scope | Discipline-based scholarship |
| Published in Proceedings | Yes |
| Title | A Unifying Contrast Maximization Framework for Event Cameras, with Applications to Motion, Depth, and Optical Flow Estimation |
| Organization Unit | |
| Authors |
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| Presentation Type | paper |
| Item Subtype | Original Work |
| Refereed | Yes |
| Status | Published in final form |
| Language |
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| ISBN | 978-1-5386-6420-9 |
| Page Range | 3867 - 3876 |
| Event Title | 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) |
| Event Type | conference |
| Event Location | Salt Lake City, UT |
| Event Start Date | July 18 - 2018 |
| Event End Date | July 23 - 2018 |
| Publisher | IEEE |
| Abstract Text | We present a unifying framework to solve several computer vision problems with event cameras: motion, depth and optical flow estimation. The main idea of our framework is to find the point trajectories on the image plane that are best aligned with the event data by maximizing an objective function: the contrast of an image of warped events. Our method implicitly handles data association between the events, and therefore, does not rely on additional appearance information about the scene. In addition to accurately recovering the motion parameters of the problem, our framework produces motion-corrected edge-like images with high dynamic range that can be used for further scene analysis. The proposed method is not only simple, but more importantly, it is, to the best of our knowledge, the first method that can be successfully applied to such a diverse set of important vision tasks with event cameras. |
| Official URL | http://rpg.ifi.uzh.ch/docs/CVPR18_Gallego.pdf |
| Digital Object Identifier | 10.1109/cvpr.2018.00407 |
| Other Identification Number | merlin-id:18683 |
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