E-RAFT: Dense Optical Flow from Event Cameras Article Swipe
Mathias Gehrig
,
Mario Millhäusler
,
Daniel Gehrig
,
Davide Scaramuzza
·
YOU?
·
· 2021
· Open Access
·
· DOI: https://doi.org/10.1109/3dv53792.2021.00030
YOU?
·
· 2021
· Open Access
·
· DOI: https://doi.org/10.1109/3dv53792.2021.00030
We propose to incorporate feature correlation and sequential processing into dense optical flow estimation from event cameras. Modern frame-based optical flow methods heavily rely on matching costs computed from feature correlation. In contrast, there exists no optical flow method for event cameras that explicitly computes matching costs. Instead, learning-based approaches using events usually resort to the U-Net architecture to estimate optical flow sparsely. Our key finding is that the introduction of correlation features significantly improves results compared to previous methods that solely rely on convolution layers. Compared to the state-of-the-art, our proposed approach computes dense optical flow and reduces the end-point error by 23% on MVSEC. Furthermore, we show that all existing optical flow methods developed so far for event cameras have been evaluated on datasets with very small displacement fields with a maximum flow magnitude of 10 pixels. Based on this observation, we introduce a new real-world dataset that exhibits displacement fields with magnitudes up to 210 pixels and 3 times higher camera resolution. Our proposed approach reduces the end-point error on this dataset by 66%.
Related Topics
Concepts
Optical flow
Computer science
Pixel
Feature (linguistics)
Matching (statistics)
Displacement (psychology)
Event (particle physics)
Artificial intelligence
Flow (mathematics)
Computer vision
Frame (networking)
Point (geometry)
Key (lock)
Adaptive optics
Algorithm
Image (mathematics)
Mathematics
Optics
Physics
Statistics
Geometry
Psychotherapist
Psychology
Linguistics
Computer security
Philosophy
Telecommunications
Quantum mechanics
Metadata
- Type
- preprint
- Language
- en
- Landing Page
- https://doi.org/10.1109/3dv53792.2021.00030
- OA Status
- green
- Cited By
- 3
- References
- 52
- Related Works
- 20
- OpenAlex ID
- https://openalex.org/W3207432009
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W3207432009Canonical identifier for this work in OpenAlex
- DOI
-
https://doi.org/10.1109/3dv53792.2021.00030Digital Object Identifier
- Title
-
E-RAFT: Dense Optical Flow from Event CamerasWork title
- Type
-
preprintOpenAlex work type
- Language
-
enPrimary language
- Publication year
-
2021Year of publication
- Publication date
-
2021-12-01Full publication date if available
- Authors
-
Mathias Gehrig, Mario Millhäusler, Daniel Gehrig, Davide ScaramuzzaList of authors in order
- Landing page
-
https://doi.org/10.1109/3dv53792.2021.00030Publisher landing page
- Open access
-
YesWhether a free full text is available
- OA status
-
greenOpen access status per OpenAlex
- Concepts
-
Optical flow, Computer science, Pixel, Feature (linguistics), Matching (statistics), Displacement (psychology), Event (particle physics), Artificial intelligence, Flow (mathematics), Computer vision, Frame (networking), Point (geometry), Key (lock), Adaptive optics, Algorithm, Image (mathematics), Mathematics, Optics, Physics, Statistics, Geometry, Psychotherapist, Psychology, Linguistics, Computer security, Philosophy, Telecommunications, Quantum mechanicsTop concepts (fields/topics) attached by OpenAlex
- Cited by
-
3Total citation count in OpenAlex
- Citations by year (recent)
-
2024: 2, 2023: 1Per-year citation counts (last 5 years)
- References (count)
-
52Number of works referenced by this work
- Related works (count)
-
20Other works algorithmically related by OpenAlex
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