The DOI matches its Crossref record; 29 of the 29 references it cites were found in a registry or an index.
EV-FlowNet: Self-Supervised Optical Flow Estimation for Event-based Cameras
Open accessAbstract
Event-based cameras have shown great promise in a variety of situations where frame based cameras suffer, such as high speed motions and high dynamic range scenes.However, developing algorithms for event measurements requires a new class of hand crafted algorithms.Deep learning has shown great success in providing model free solutions to many problems in the vision community, but existing networks have been developed with frame based images in mind, and there does not exist the wealth of labeled data for events as there does for images for supervised training.To these points, we present EV-FlowNet, a novel self-supervised deep learning pipeline for optical flow estimation for event based cameras.In particular, we introduce an image based representation of a given event stream, which is fed into a self-supervised neural network as the sole input.The corresponding grayscale images captured from the same camera at the same time as the events are then used as a supervisory signal to provide a loss function at training time, given the estimated flow from the network.We show that the resulting network is able to accurately predict optical flow from events only in a variety of different scenes, with performance competitive to image based networks.This method not only allows for accurate estimation of dense optical flow, but also provides a framework for the transfer of other self-supervised methods to the eventbased domain.
Overview
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Citations
Cites
U-Net: Convolutional Networks for Biomedical Image Segmentation
Ronneberger, O., Fischer, P., & Brox, T. (2015) · Lecture notes in computer science · doi:10.1007/978-3-319-24574-4_28 · Cited by 99,404
(1981) · Cited by 11,963
Stacked Hourglass Networks for Human Pose Estimation
Newell, A., Yang, K., & Deng, J. (2016) · Lecture Notes in Computer Science · doi:10.1007/978-3-319-46484-8_29 · Cited by 5,634
A Low Power, Fully Event-Based Gesture Recognition System
Amir, A., Taba, B., Berg, D., Melano, T., McKinstry, J., Di Nolfo, C., Nayak, T., Andreopoulos, A., Garreau, G., Mendoza, M., et al. (2017) · 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2017.781 · Cited by 1,017
Converting Static Image Datasets to Spiking Neuromorphic Datasets Using Saccades
Orchard, G., Jayawant, A., Cohen, G. K., & Thakor, N. (2015) · Frontiers in Neuroscience · doi:10.3389/fnins.2015.00437 · Cited by 882
Sun, D., Roth, S., & Black, M. J. (2014) · International Journal of Computer Vision · doi:10.1007/s11263-013-0644-x · Cited by 614
HOTS: A Hierarchy of Event-Based Time-Surfaces for Pattern Recognition
Lagorce, X., Orchard, G., Galluppi, F., Shi, B. E., & Benosman, R. B. (2017) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2016.2574707 · Cited by 612
Mueggler, E., Rebecq, H., Gallego, G., Delbruck, T., & Scaramuzza, D. (2017) · The International Journal of Robotics Research · doi:10.1177/0278364917691115 · Cited by 606
The Multivehicle Stereo Event Camera Dataset: An Event Camera Dataset for 3D Perception
Zhu, A. Z., Thakur, D., Ozaslan, T., Pfrommer, B., Kumar, V., & Daniilidis, K. (2018) · IEEE Robotics and Automation Letters · doi:10.1109/lra.2018.2800793 · Cited by 567
Self-citationUnFlow: Unsupervised Learning of Optical Flow With a Bidirectional Census Loss
Meister, S., Hur, J., & Roth, S. (2018) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v32i1.12276 · Cited by 520
Cited by
Gallego, G., Delbruck, T., Orchard, G., Bartolozzi, C., Taba, B., Censi, A., Leutenegger, S., Davison, A. J., Conradt, J., Daniilidis, K., et al. (2022) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2020.3008413 · Cited by 2,566
Self-citationEvents-To-Video: Bringing Modern Computer Vision to Event Cameras
Rebecq, H., Ranftl, R., Koltun, V., & Scaramuzza, D. (2019) · 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2019.00398 · Cited by 420
Event-Based Moving Object Detection and Tracking
Mitrokhin, A., Fermuller, C., Parameshwara, C., & Aloimonos, Y. (2018) · 2018 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) · doi:10.1109/iros.2018.8593805 · Cited by 365
Bringing a Blurry Frame Alive at High Frame-Rate With an Event Camera
Pan, L., Scheerlinck, C., Yu, X., Hartley, R., Liu, M., & Dai, Y. (2019) · 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2019.00698 · Cited by 308
Event-driven Re-Id: A New Benchmark and Method Towards Privacy-Preserving Person Re-Identification
Ahmad, S., Scarpellini, G., Morerio, P., & Bue, A. D. (2022) · 2022 IEEE/CVF Winter Conference on Applications of Computer Vision Workshops (WACVW) · doi:10.1109/wacvw54805.2022.00052 · Cited by 54
Metadata
- Authors
- Zhu, A., Yuan, L., Chaney, K., & Daniilidis, K.
- Venue
- Robotics: Science and Systems XIV
- Published
- 2018-06-26
- OpenAlex
- W2788172931
- Cited by
- 470
- References
- 29
- FWCI
- 12.00
- Type
- conference paper
- Language
- en
- Open access
- gold
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
How to cite
Cite CoChat for the checks on this page, and the paper for its own findings.
- This page
- CoChat. (2026). Citation check: EV-FlowNet: Self-Supervised Optical Flow Estimation for Event-based Cameras. https://app.cochat.ai/papers/10.15607/rss.2018.xiv.062
- The paper
- Zhu, A., Yuan, L., Chaney, K., & Daniilidis, K. (2018). EV-FlowNet: Self-Supervised Optical Flow Estimation for Event-based Cameras. Robotics: Science and Systems XIV. https://doi.org/10.15607/rss.2018.xiv.062
Data sources
Bibliographic data: Semantic Scholar, OpenAlex, Crossref.
Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.
