Almatrafi, M.[Mohammed],
Baldwin, R.W.[R. Wes],
Aizawa, K.[Kiyoharu],
Hirakawa, K.[Keigo],
Distance Surface for Event-Based Optical Flow,
PAMI(42), No. 7, July 2020, pp. 1547-1556.
IEEE DOI
2006
Optical imaging, Optical sensors, Cameras, Voltage control,
Neuromorphics, Image edge detection, Surface treatment,
neuromorphic camera
BibRef
Baldwin, R.W.[R. Wes],
Almatrafi, M.[Mohammed],
Kaufman, J.R.[Jason R.],
Asari, V.[Vijayan],
Hirakawa, K.[Keigo],
Inceptive Event Time-Surfaces for Object Classification Using
Neuromorphic Cameras,
ICIAR19(II:395-403).
Springer DOI
1909
BibRef
Shedligeri, P.[Prasan],
Mitra, K.[Kaushik],
High frame rate optical flow estimation from event sensors via
intensity estimation,
CVIU(208-209), 2021, pp. 103208.
Elsevier DOI
2106
Event sensors, Optical flow, High dynamic range, High temporal resolution
BibRef
Brebion, V.[Vincent],
Moreau, J.[Julien],
Davoine, F.[Franck],
Real-Time Optical Flow for Vehicular Perception With Low- and
High-Resolution Event Cameras,
ITS(23), No. 9, September 2022, pp. 15066-15078.
IEEE DOI
2209
Optical sensors, Optical imaging, Image edge detection, Cameras,
Sensors, Real-time systems, High-speed optical techniques,
real-time applications
BibRef
Lin, X.H.[Xiu-Hong],
Yang, C.H.[Chen-Hui],
Bian, X.S.[Xue-Sheng],
Liu, W.Q.[Wei-Quan],
Wang, C.[Cheng],
EAGAN: Event-based attention generative adversarial networks for
optical flow and depth estimation,
IET-CV(16), No. 7, 2022, pp. 581-595.
DOI Link
2210
BibRef
Liu, M.[Min],
Delbruck, T.[Tobi],
EDFLOW: Event Driven Optical Flow Camera With Keypoint Detection and
Adaptive Block Matching,
CirSysVideo(32), No. 9, September 2022, pp. 5776-5789.
IEEE DOI
2209
Optical sensors, Voltage control, Cameras, Optical saturation,
Optical feedback, Hardware, Estimation, Dynamic vision sensor, FPGA, near-sensor processing
BibRef
Pan, L.,
Liu, M.,
Hartley, R.I.,
Single Image Optical Flow Estimation With an Event Camera,
CVPR20(1669-1678)
IEEE DOI
2008
Estimation, Cameras, Optical imaging, Brightness,
Image reconstruction, Optical variables control, Image restoration
BibRef
Wan, Z.X.[Zhe-Xiong],
Dai, Y.C.[Yu-Chao],
Mao, Y.X.[Yu-Xin],
Learning Dense and Continuous Optical Flow From an Event Camera,
IP(31), 2022, pp. 7237-7251.
IEEE DOI
2212
Optical flow, Estimation, Image motion analysis, Cameras,
Correlation, Brightness, Event camera, event-based vision,
multimodal learning
BibRef
Zhang, Z.L.[Ze-Lin],
Yezzi, A.J.[Anthony J.],
Gallego, G.[Guillermo],
Formulating Event-Based Image Reconstruction as a Linear Inverse
Problem With Deep Regularization Using Optical Flow,
PAMI(45), No. 7, July 2023, pp. 8372-8389.
IEEE DOI
2306
Brightness, Image reconstruction, Cameras, Visualization,
Inverse problems, Estimation, Mathematical models, Event cameras,
ADMM
See also Event-Based, 6-DOF Camera Tracking from Photometric Depth Maps.
BibRef
Gu, D.X.[Da-Xin],
Li, J.[Jia],
Zhu, L.[Lin],
Zhang, Y.[Yu],
Ren, J.S.[Jimmy S.],
Reliable Event Generation With Invertible Conditional Normalizing
Flow,
PAMI(46), No. 2, February 2024, pp. 927-943.
IEEE DOI
2401
Conditional normalizing flow, contrast threshold, event camera,
event generation, event noise rate
BibRef
Gehrig, M.[Mathias],
Muglikar, M.[Manasi],
Scaramuzza, D.[Davide],
Dense Continuous-Time Optical Flow From Event Cameras,
PAMI(46), No. 7, July 2024, pp. 4736-4746.
IEEE DOI
2406
Trajectory, Optical flow, Correlation, Cameras, Task analysis,
Feature extraction, Estimation, Motion estimation, optical flow, event cameras
BibRef
Schnider, Y.[Yannick],
Wozniak, S.[Stanislaw],
Gehrig, M.[Mathias],
Lecomte, J.[Jules],
von Arnim, A.[Axel],
Benini, L.[Luca],
Scaramuzza, D.[Davide],
Pantazi, A.[Angeliki],
Neuromorphic Optical Flow and Real-time Implementation with Event
Cameras,
EventVision23(4129-4138)
IEEE DOI
2309
BibRef
Gehrig, M.[Mathias],
Millhäusler, M.[Mario],
Gehrig, D.[Daniel],
Scaramuzza, D.[Davide],
E-RAFT: Dense Optical Flow from Event Cameras,
3DV21(197-206)
IEEE DOI
HTML Version.
2201
Code, Optical Flow. Paper, Video, Code:
Training, Image motion analysis, Correlation, Costs, Estimation,
Computer architecture, optical flow, event cameras
BibRef
Wan, Z.[Zengyu],
Tan, G.[Ganchao],
Wang, Y.[Yang],
Zhai, W.[Wei],
Cao, Y.[Yang],
Zha, Z.J.[Zheng-Jun],
Event-Based Optical Flow via Transforming Into Motion-Dependent View,
IP(33), 2024, pp. 5327-5339.
IEEE DOI
2410
Optical flow, Estimation, Feature extraction, Cameras,
Image motion analysis, Correlation, Optical flow estimation, event cameras
BibRef
Paredes-Vallés, F.[Federico],
Scheper, K.Y.W.[Kirk Y. W.],
de Wagter, C.[Christope],
de Croon, G.C.H.E.[Guido C. H. E.],
Taming Contrast Maximization for Learning Sequential, Low-latency,
Event-based Optical Flow,
ICCV23(9661-9671)
IEEE DOI
2401
BibRef
Ponghiran, W.[Wachirawit],
Liyanagedera, C.M.[Chamika Mihiranga],
Roy, K.[Kaushik],
Event-based Temporally Dense Optical Flow Estimation with Sequential
Learning,
ICCV23(9793-9802)
IEEE DOI
2401
BibRef
Luo, X.L.[Xing-Long],
Luo, A.[Ao],
Wang, Z.N.[Zheng-Ning],
Lin, C.Y.[Chun-Yu],
Zeng, B.[Bing],
Liu, S.C.[Shuai-Cheng],
Efficient Meshflow and Optical Flow Estimation from Event Cameras,
CVPR24(19198-19207)
IEEE DOI Code:
WWW Link.
2410
Image motion analysis, Accuracy, Runtime, Estimation, Cameras,
Event Camera, Meshflow, Optical Flow, Graghics Rendering
BibRef
Luo, X.L.[Xing-Long],
Luo, K.M.[Kun-Ming],
Luo, A.[Ao],
Wang, Z.N.[Zheng-Ning],
Tan, P.[Ping],
Liu, S.C.[Shuai-Cheng],
Learning Optical Flow from Event Camera with Rendered Dataset,
ICCV23(9813-9823)
IEEE DOI Code:
WWW Link.
2401
BibRef
Nagata, J.[Jun],
Sekikawa, Y.[Yusuke],
Tangentially Elongated Gaussian Belief Propagation for Event-Based
Incremental Optical Flow Estimation,
CVPR23(21940-21949)
IEEE DOI
2309
BibRef
And: A2, A1:
Live Demonstration: Tangentially Elongated Gaussian Belief
Propagation for Event-based Incremental Optical Flow Estimation,
EventVision23(3931-3932)
IEEE DOI
2309
BibRef
Dalgaty, T.[Thomas],
Mesquida, T.[Thomas],
Joubert, D.[Damien],
Sironi, A.[Amos],
Vivet, P.[Pascal],
Posch, C.[Christoph],
HUGNet: Hemi-Spherical Update Graph Neural Network applied to
low-latency event-based optical flow,
EventVision23(3953-3962)
IEEE DOI
2309
BibRef
Li, Z.Y.[Zhuo-Yan],
Shen, J.W.[Jia-Wei],
Liu, R.[Ruitao],
A Lightweight Network to Learn Optical Flow from Event Data,
ICPR21(1-7)
IEEE DOI
2105
Image motion analysis, Laplace equations,
Data integrity, Neural networks, Estimation, Computer architecture, CNN
BibRef
Lee, C.[Chankyu],
Kosta, A.K.[Adarsh Kumar],
Zhu, A.Z.[Alex Zihao],
Chaney, K.[Kenneth],
Daniilidis, K.[Kostas],
Roy, K.[Kaushik],
Spike-flownet: Event-based Optical Flow Estimation with
Energy-efficient Hybrid Neural Networks,
ECCV20(XXIX: 366-382).
Springer DOI
2010
BibRef
Zhu, A.Z.[Alex Zihao],
Yuan, L.Z.[Liang-Zhe],
Chaney, K.[Kenneth],
Daniilidis, K.[Kostas],
Unsupervised Event-Based Learning of Optical Flow, Depth, and Egomotion,
CVPR19(989-997).
IEEE DOI
2002
BibRef
Earlier:
Unsupervised Event-Based Optical Flow Using Motion Compensation,
OpticalFlow18(VI:711-714).
Springer DOI
1905
BibRef
Gallego, G.,
Rebecq, H.,
Scaramuzza, D.,
A Unifying Contrast Maximization Framework for Event Cameras, with
Applications to Motion, Depth, and Optical Flow Estimation,
CVPR18(3867-3876)
IEEE DOI
1812
Trajectory, Estimation, Cameras, Optical imaging, Brightness,
Image edge detection
BibRef
Ridwan, I.[Iffatur],
Cheng, H.[Howard],
An Event-Based Optical Flow Algorithm for Dynamic Vision Sensors,
ICIAR17(182-189).
Springer DOI
1706
BibRef
Chapter on Optical Flow Field Computations and Use continues in
Opeical Flow, Learning, Neural Networks, GAN .