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| Type | Conference or Workshop Paper |
| Scope | Discipline-based scholarship |
| Published in Proceedings | Yes |
| Title | Focus Is All You Need: Loss Functions for Event-Based Vision |
| 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 | 978-1-7281-3293-8 |
| Page Range | 12272 - 12281 |
| Event Title | 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) |
| Event Type | conference |
| Event Location | Long Beach, CA, USA |
| Event Start Date | July 15 - 2019 |
| Event End Date | July 20 - 2019 |
| Publisher | IEEE |
| Abstract Text | Event cameras are novel vision sensors that output pixel-level brightness changes ("events") instead of traditional video frames. These asynchronous sensors offer several advantages over traditional cameras, such as, high temporal resolution, very high dynamic range, and no motion blur. To unlock the potential of such sensors, motion compensation methods have been recently proposed. We present a collection and taxonomy of twenty two objective functions to analyze event alignment in motion compensation approaches. We call them focus loss functions since they have strong connections with functions used in traditional shape-from-focus applications. The proposed loss functions allow bringing mature computer vision tools to the realm of event cameras. We compare the accuracy and runtime performance of all loss functions on a publicly available dataset, and conclude that the variance, the gradient and the Laplacian magnitudes are among the best loss functions. The applicability of the loss functions is shown on multiple tasks: rotational motion, depth and optical flow estimation. The proposed focus loss functions allow to unlock the outstanding properties of event cameras. |
| Digital Object Identifier | 10.1109/cvpr.2019.01256 |
| Other Identification Number | merlin-id:20290 |
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