Chen, T.W.[Tsu Wang], and
Lin, W.C.[Wei Chung],
Chen, C.T.,
Artificial Neural Networks for 3-D Motion Analysis I: Rigid Motion,
TNN(6), No. 6, November 1995, pp. 1386-1393.
BibRef
9511
And:
Artificial Neural Networks for 3-D Motion Analysis II: Nonrigid Motion,
TNN(6), No. 6, November 1995, pp. 1394-1401.
See also Neural-Network Approach to CSG-Based 3-D Object Recognition, A.
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Wang, R.Y.,
A Network Model for the Optic Flow Computation of the MST Neurons,
NeurNet(9), No. 3, April 1996, pp. 411-426.
9605
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Gray, W.S.,
Nabet, B.,
Volterra Series Analysis and Synthesis of a Neural Network for Velocity
Estimation,
SMC-B(29), No. 2, April 1999, pp. 190.
BibRef
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Mayer, N.[Nikolaus],
Ilg, E.[Eddy],
Fischer, P.[Philipp],
Hazirbas, C.[Caner],
Cremers, D.[Daniel],
Dosovitskiy, A.[Alexey],
Brox, T.[Thomas],
What Makes Good Synthetic Training Data for Learning Disparity and
Optical Flow Estimation?,
IJCV(126), No. 9, September 2018, pp. 942-960.
Springer DOI
1809
BibRef
Dosovitskiy, A.,
Fischery, P.,
Ilg, E.,
Hausser, P.,
Hazirbas, C.,
Golkov, V.,
van de Smagt, P.,
Cremers, D.,
Brox, T.,
FlowNet: Learning Optical Flow with Convolutional Networks,
ICCV15(2758-2766)
IEEE DOI
1602
Computer architecture
BibRef
Ilg, E.[Eddy],
Saikia, T.[Tonmoy],
Keuper, M.[Margret],
Brox, T.[Thomas],
Occlusions, Motion and Depth Boundaries with a Generic Network for
Disparity, Optical Flow or Scene Flow Estimation,
ECCV18(XII: 626-643).
Springer DOI
1810
BibRef
Schrodi, S.[Simon],
Saikia, T.[Tonmoy],
Brox, T.[Thomas],
Towards Understanding Adversarial Robustness of Optical Flow Networks,
CVPR22(8906-8914)
IEEE DOI
2210
Codes, Computational modeling, Estimation, Network architecture,
Apertures, Robustness, Motion and tracking, Adversarial attack and defense
BibRef
Ilg, E.[Eddy],
Mayer, N.[Nikolaus],
Saikia, T.[Tonmoy],
Keuper, M.[Margret],
Dosovitskiy, A.[Alexey],
Brox, T.[Thomas],
FlowNet 2.0: Evolution of Optical Flow Estimation with Deep Networks,
CVPR17(1647-1655)
IEEE DOI
1711
Adaptive optics, Computer architecture, Estimation,
Optical imaging, Schedules, Stacking, Training
BibRef
Wannenwetsch, A.S.,
Keuper, M.,
Roth, S.,
ProbFlow: Joint Optical Flow and Uncertainty Estimation,
ICCV17(1182-1191)
IEEE DOI
1802
Bayes methods, entropy, image sequences, motion estimation,
energy minimization approach, energy-based formulations,
Uncertainty
BibRef
Makansi, O.[Osama],
Ilg, E.[Eddy],
Cicek, O.[Ozgun],
Brox, T.[Thomas],
Overcoming Limitations of Mixture Density Networks: A Sampling and
Fitting Framework for Multimodal Future Prediction,
CVPR19(7137-7146).
IEEE DOI
2002
BibRef
Ilg, E.[Eddy],
Çiçek, Ö.[Özgün],
Galesso, S.[Silvio],
Klein, A.[Aaron],
Makansi, O.[Osama],
Hutter, F.[Frank],
Brox, T.[Thomas],
Uncertainty Estimates and Multi-hypotheses Networks for Optical Flow,
ECCV18(VII: 677-693).
Springer DOI
1810
BibRef
Paredes-Vallés, F.[Federico],
Scheper, K.Y.W.[Kirk Y. W.],
de Croon, G.C.H.E.[Guido C.H.E.],
Unsupervised Learning of a Hierarchical Spiking Neural Network for
Optical Flow Estimation: From Events to Global Motion Perception,
PAMI(42), No. 8, August 2020, pp. 2051-2064.
IEEE DOI
2007
Neurons, Visualization, Biomedical optical imaging,
Optical sensors, Biological system modeling,
unsupervised learning
BibRef
Hui, T.W.[Tak-Wai],
Tang, X.[Xiaoou],
Loy, C.C.[Chen Change],
A Lightweight Optical Flow CNN: Revisiting Data Fidelity and
Regularization,
PAMI(43), No. 8, August 2021, pp. 2555-2569.
IEEE DOI
2107
BibRef
Earlier:
LiteFlowNet: A Lightweight Convolutional Neural Network for Optical
Flow Estimation,
CVPR18(8981-8989)
IEEE DOI
1812
Optical imaging, Adaptive optics, Estimation, Optical computing,
Convolutional codes, Optical network units,
and warping.
Estimation, Feature extraction, Convolution,
Optical filters, Optical fiber networks
BibRef
Hui, T.W.[Tak-Wai],
Loy, C.C.[Chen Change],
LiteFlownet3: Resolving Correspondence Ambiguity for More Accurate
Optical Flow Estimation,
ECCV20(XX:169-184).
Springer DOI
2011
BibRef
Che, T.T.[Tong-Tong],
Zheng, Y.J.[Yuan-Jie],
Yang, Y.S.[Yun-Shuai],
Hou, S.J.[Su-Juan],
Jia, W.K.[Wei-Kuan],
Yang, J.[Jie],
Gong, C.[Chen],
SDOF-GAN: Symmetric Dense Optical Flow Estimation With Generative
Adversarial Networks,
IP(30), 2021, pp. 6036-6049.
IEEE DOI
2107
Estimation, Optical imaging, Adaptive optics,
Generative adversarial networks, Brightness,
semi-supervised learning
BibRef
Zheng, Y.J.[Yuan-Jie],
Sui, X.D.[Xiao-Dan],
Jiang, Y.[Yanyun],
Che, T.T.[Tong-Tong],
Zhang, S.T.[Shao-Ting],
Yang, J.[Jie],
Li, H.S.[Hong-Sheng],
SymReg-GAN: Symmetric Image Registration With Generative Adversarial
Networks,
PAMI(44), No. 9, September 2022, pp. 5631-5646.
IEEE DOI
2208
Image registration, Generative adversarial networks, Generators,
Estimation, Training, Magnetic resonance imaging, Image resolution,
multimodal image registration
BibRef
Liu, S.C.[Shuai-Cheng],
Luo, K.M.[Kun-Ming],
Ye, N.J.[Nian-Jin],
Wang, C.[Chuan],
Wang, J.[Jue],
Zeng, B.[Bing],
OIFlow: Occlusion-Inpainting Optical Flow Estimation by Unsupervised
Learning,
IP(30), 2021, pp. 6420-6433.
IEEE DOI
2107
Optical imaging, Feature extraction, Optical variables control,
Estimation, Optical fiber networks, Adaptive optics,
unsupervised learning
BibRef
Liu, S.C.[Shuai-Cheng],
Luo, K.M.[Kun-Ming],
Luo, A.[Ao],
Wang, C.[Chuan],
Meng, F.M.[Fan-Man],
Zeng, B.[Bing],
ASFlow: Unsupervised Optical Flow Learning With Adaptive Pyramid
Sampling,
CirSysVideo(32), No. 7, July 2022, pp. 4282-4295.
IEEE DOI
2207
Optical imaging, Optical variables control, Optical losses,
Feature extraction, Interpolation, Adaptive systems,
pyramid downsampling
BibRef
Luo, K.M.[Kun-Ming],
Wang, C.[Chuan],
Liu, S.C.[Shuai-Cheng],
Fan, H.Q.[Hao-Qiang],
Wang, J.[Jue],
Sun, J.[Jian],
UPFlow: Upsampling Pyramid for Unsupervised Optical Flow Learning,
CVPR21(1045-1054)
IEEE DOI
2111
Optical losses, Interpolation, Estimation,
Benchmark testing, Pattern recognition, Optical flow
BibRef
Zhao, R.[Rui],
Xiong, R.Q.[Rui-Qin],
Ding, Z.[Ziluo],
Fan, X.P.[Xiao-Peng],
Zhang, J.[Jian],
Huang, T.J.[Tie-Jun],
MRDFlow: Unsupervised Optical Flow Estimation Network With
Multi-Scale Recurrent Decoder,
CirSysVideo(32), No. 7, July 2022, pp. 4639-4652.
IEEE DOI
2207
Optical flow, Decoding, Estimation, Optical losses,
Image motion analysis, Correlation, recurrent decoder
BibRef
Liu, P.P.[Peng-Peng],
Lyu, M.R.[Michael R.],
King, I.[Irwin],
Xu, J.[Jia],
Learning by Distillation:
A Self-Supervised Learning Framework for Optical Flow Estimation,
PAMI(44), No. 9, September 2022, pp. 5026-5041.
IEEE DOI
2208
Optical imaging, Predictive models, Optical variables control,
Estimation, Optical computing, Training, Data models, Optical flow,
and stereo matching
BibRef
de Jong, D.B.[David B.],
Paredes-Vallés, F.[Federico],
de Croon, G.C.H.E.[Guido C. H. E.],
How Do Neural Networks Estimate Optical Flow?
A Neuropsychology-Inspired Study,
PAMI(44), No. 11, November 2022, pp. 8290-8305.
IEEE DOI
2210
Optical imaging, Optical fiber networks, Optical sensors,
Optical computing, Estimation, Biomedical optical imaging, neuropsychology
BibRef
Chi, C.[Cheng],
Hao, T.Y.[Tian-Yu],
Wang, Q.J.[Qing-Jie],
Guo, P.[Peng],
Yang, X.[Xin],
Subspace-PnP: A Geometric Constraint Loss for Mutual Assistance of
Depth and Optical Flow Estimation,
IJCV(130), No. 12, December 2022, pp. 3054-3069.
Springer DOI
2211
BibRef
Chi, C.[Cheng],
Wang, Q.J.[Qing-Jie],
Hao, T.Y.[Tian-Yu],
Guo, P.[Peng],
Yang, X.[Xin],
Feature-Level Collaboration: Joint Unsupervised Learning of Optical
Flow, Stereo Depth and Camera Motion,
CVPR21(2463-2473)
IEEE DOI
2111
Optical losses, Motion estimation,
Collaboration, Cameras, Feature extraction, Task analysis
BibRef
Savian, S.[Stefano],
Elahi, M.[Mehdi],
Janes, A.A.[Andrea A.],
Tillo, T.[Tammam],
Benchmarking equivariance for Deep Learning based optical flow
estimators,
SP:IC(111), 2023, pp. 116892.
Elsevier DOI
2301
Optical flow, Deep Learning, Equivariance, Benchmark
BibRef
Kong, L.T.[Ling-Tong],
Yang, J.[Jie],
MDFlow: Unsupervised Optical Flow Learning by Reliable Mutual
Knowledge Distillation,
CirSysVideo(33), No. 2, February 2023, pp. 677-688.
IEEE DOI
2302
Reliability, Knowledge engineering, Costs, Estimation,
Biomedical optical imaging, Task analysis, Optical flow,
real time
BibRef
Xiang, X.Z.[Xue-Zhi],
Abdein, R.[Rokia],
Lv, N.[Ning],
Yang, J.[Jie],
Self-Supervised Learning of Scene Flow with Occlusion Handling
Through Feature Masking,
PR(139), 2023, pp. 109487.
Elsevier DOI
2304
BibRef
Earlier: A2, A1, A3, Only:
Self-Supervised Learning of Optical Flow, Depth, Camera Pose and
Rigidity Segmentation with Occlusion Handling,
ICIP22(6-10)
IEEE DOI
2211
Optical flow, Depth estimation, Camera pose,
Rigidity segmentation, Occlusion handling, Deformable decoder.
Training, Integrated optics, Image motion analysis, Correlation,
Self-supervised learning, Cameras, Optical flow estimation
BibRef
Lin, Z.W.[Zhi-Wei],
Liang, T.T.[Ting-Ting],
Xiao, T.H.[Tai-Hong],
Wang, Y.T.[Yong-Tao],
Yang, M.H.[Ming-Hsuan],
FlowNAS: Neural Architecture Search for Optical Flow Estimation,
IJCV(132), No. 4, April 2024, pp. 1055-1074.
Springer DOI
2404
BibRef
Xiao, T.H.[Tai-Hong],
Yuan, J.W.[Jin-Wei],
Sun, D.Q.[De-Qing],
Wang, Q.F.[Qi-Fei],
Zhang, X.Y.[Xin-Yu],
Xu, K.H.[Ke-Han],
Yang, M.H.[Ming-Hsuan],
Learnable Cost Volume Using the Cayley Representation,
ECCV20(IX:483-499).
Springer DOI
2011
deep models for optical flow estimation.
BibRef
Lu, Y.W.[Ya-Wen],
Wang, Q.F.[Qi-Fan],
Ma, S.Q.[Si-Qi],
Geng, T.[Tong],
Chen, Y.J.V.[Ying-Jie Victor],
Chen, H.[Huaijin],
Liu, D.F.[Dong-Fang],
TransFlow: Transformer as Flow Learner,
CVPR23(18063-18073)
IEEE DOI
2309
BibRef
Jung, H.[Hyunyoung],
Hui, Z.[Zhuo],
Luo, L.[Lei],
Yang, H.T.[Hai-Tao],
Liu, F.[Feng],
Yoo, S.J.[Sung-Joo],
Ranjan, R.[Rakesh],
Demandolx, D.[Denis],
AnyFlow: Arbitrary Scale Optical Flow with Implicit Neural
Representation,
CVPR23(5455-5465)
IEEE DOI
2309
BibRef
Shi, X.Y.[Xiao-Yu],
Huang, Z.Y.[Zhao-Yang],
Li, D.[Dasong],
Zhang, M.[Manyuan],
Cheung, K.C.[Ka Chun],
See, S.[Simon],
Qin, H.W.[Hong-Wei],
Dai, J.F.[Ji-Feng],
Li, H.S.[Hong-Sheng],
FlowFormer++: Masked Cost Volume Autoencoding for Pretraining Optical
Flow Estimation,
CVPR23(1599-1610)
IEEE DOI
2309
BibRef
Marsal, R.[Rémi],
Chabot, F.[Florian],
Loesch, A.[Angélique],
Sahbi, H.[Hichem],
BrightFlow:
Brightness-Change-Aware Unsupervised Learning of Optical Flow,
WACV23(2060-2069)
IEEE DOI
2302
Training, Optical losses, Source coding, Brightness, Estimation,
Radiometry, Reflection
BibRef
Min, C.[Chaerin],
Kim, T.[Taehyun],
Lim, J.W.[Jong-Woo],
Meta-Learning for Adaptation of Deep Optical Flow Networks,
WACV23(2144-2153)
IEEE DOI
2302
Training, Optical losses, Neural networks, Estimation, Training data,
Reliability, Labeling, and algorithms (including transfer)
BibRef
Schelling, M.[Michael],
Hermosilla, P.[Pedro],
Ropinski, T.[Timo],
Weakly-Supervised Optical Flow Estimation for Time-of-Flight,
WACV23(2134-2143)
IEEE DOI
2302
Training, Optical losses, Optical variables measurement,
Motion compensation, Motion measurement, 3D computer vision
BibRef
Savian, S.[Stefano],
Morerio, P.[Pietro],
del Bue, A.[Alessio],
Janes, A.A.[Andrea A.],
Tillo, T.[Tammam],
Towards Equivariant Optical Flow Estimation with Deep Learning,
WACV23(5077-5086)
IEEE DOI
2302
Training, Measurement, Deep learning, Optical losses, Codes,
Computational modeling
BibRef
Chen, Y.H.[Yong-Hu],
Zhu, D.C.[Dong-Chen],
Shi, W.J.[Wen-Jun],
Zhang, G.H.[Guang-Hui],
Zhang, T.Y.[Tian-Yu],
Zhang, X.L.[Xiao-Lin],
Li, J.[Jiamao],
MFCFlow: A Motion Feature Compensated Multi-Frame Recurrent Network
for Optical Flow Estimation,
WACV23(5057-5066)
IEEE DOI
2302
Optical filters, Geometry, Matched filters, Correlation,
Computational modeling, Estimation, Coherence,
Low-level and physics-based vision
BibRef
Chong, X.Y.[Xiao-Ya],
Zhou, N.[Niyun],
Li, Q.[Qing],
Leung, H.[Howard],
NoiseFlow: Learning Optical Flow from Low SNR Cryo-EM Movie,
ICPR22(3471-3477)
IEEE DOI
2212
Deep learning, Correlation, Computational modeling, Stacking,
Noise reduction, Estimation, Motion pictures
BibRef
Bhandari, K.[Keshav],
Duan, B.[Bin],
Liu, G.[Gaowen],
Latapie, H.[Hugo],
Zong, Z.L.[Zi-Liang],
Yan, Y.[Yan],
Learning Omnidirectional Flow in 360°
Video via Siamese Representation,
ECCV22(VIII:557-574).
Springer DOI
2211
BibRef
Chang, C.P.[Chih-Peng],
Chen, P.Y.[Peng-Yu],
Ho, Y.H.[Yung-Han],
Peng, W.H.[Wen-Hsiao],
Deep Incremental Optical Flow Coding For Learned Video Compression,
ICIP22(3988-3992)
IEEE DOI
2211
Image coding, Bit rate, Video compression, Motion compensation,
Encoding, Video codecs, Optical flow, Video Coding, Double Warping
BibRef
Im, W.B.[Woo-Bin],
Lee, S.[Sebin],
Yoon, S.E.[Sung-Eui],
Semi-supervised Learning of Optical Flow by Flow Supervisor,
ECCV22(XXXV:302-318).
Springer DOI
2211
BibRef
Li, Y.H.[Yi-Heng],
Barnes, C.[Connelly],
Huang, K.[Kun],
Zhang, F.L.[Fang-Lue],
Deep 360° Optical Flow Estimation Based on Multi-projection Fusion,
ECCV22(XXXV:336-352).
Springer DOI
2211
BibRef
Yuan, S.[Shuai],
Sun, X.[Xian],
Kim, H.[Hannah],
Yu, S.Z.[Shu-Zhi],
Tomasi, C.[Carlo],
Optical Flow Training Under Limited Label Budget via Active Learning,
ECCV22(XXII:410-427).
Springer DOI
2211
BibRef
Huang, Z.Y.[Zhao-Yang],
Shi, X.Y.[Xiao-Yu],
Zhang, C.[Chao],
Wang, Q.[Qiang],
Cheung, K.C.[Ka Chun],
Qin, H.W.[Hong-Wei],
Dai, J.F.[Ji-Feng],
Li, H.S.[Hong-Sheng],
FlowFormer: A Transformer Architecture for Optical Flow,
ECCV22(XVII:668-685).
Springer DOI
2211
BibRef
Le Guen, V.[Vincent],
Rambour, C.[Clément],
Thome, N.[Nicolas],
Complementing Brightness Constancy with Deep Networks for Optical Flow
Prediction,
ECCV22(XXI:121-138).
Springer DOI
2211
BibRef
Sun, D.Q.[De-Qing],
Herrmann, C.[Charles],
Reda, F.[Fitsum],
Rubinstein, M.[Michael],
Fleet, D.J.[David J.],
Freeman, W.T.[William T.],
Disentangling Architecture and Training for Optical Flow,
ECCV22(XXII:165-182).
Springer DOI
2211
BibRef
Xu, H.F.[Hao-Fei],
Zhang, J.[Jing],
Cai, J.F.[Jian-Fei],
Rezatofighi, H.[Hamid],
Tao, D.C.[Da-Cheng],
GMFlow: Learning Optical Flow via Global Matching,
CVPR22(8111-8120)
IEEE DOI
2210
Image motion analysis, Correlation, Costs, Pipelines, Estimation,
Transformers, Motion and tracking, Low-level vision
BibRef
Luo, A.[Ao],
Yang, F.[Fan],
Li, X.[Xin],
Nie, L.[Lang],
Lin, C.Y.[Chun-Yu],
Fan, H.Q.[Hao-Qiang],
Liu, S.C.[Shuai-Cheng],
GAFlow: Incorporating Gaussian Attention into Optical Flow,
ICCV23(9608-9617)
IEEE DOI Code:
WWW Link.
2401
BibRef
Luo, A.[Ao],
Yang, F.[Fan],
Li, X.[Xin],
Liu, S.C.[Shuai-Cheng],
Learning Optical Flow with Kernel Patch Attention,
CVPR22(8896-8905)
IEEE DOI
2210
Image motion analysis, Motion estimation, Benchmark testing,
Pattern recognition, Kernel, Task analysis, Motion and tracking,
Video analysis and understanding
BibRef
Bai, S.J.[Shao-Jie],
Geng, Z.Y.[Zheng-Yang],
Savani, Y.[Yash],
Kolter, J.Z.[J. Zico],
Deep Equilibrium Optical Flow Estimation,
CVPR22(610-620)
IEEE DOI
2210
Training, Degradation, Image motion analysis,
Computational modeling, Memory management, Estimation, Machine learning
BibRef
Hu, L.W.[Li-Wen],
Zhao, R.[Rui],
Ding, Z.[Ziluo],
Ma, L.[Lei],
Shi, B.X.[Bo-Xin],
Xiong, R.Q.[Rui-Qin],
Huang, T.J.[Tie-Jun],
Optical Flow Estimation for Spiking Camera,
CVPR22(17823-17832)
IEEE DOI
2210
Deep learning, Training, Photography, Tracking, Pipelines, Estimation,
Cameras, Computational photography, Motion and tracking
BibRef
Ammar, A.[Anis],
Chebbah, A.[Amani],
Fredj, H.B.[Hana Ben],
Souani, C.[Chokri],
Comparative Study of latest CNN based Optical Flow Estimation,
ISCV22(1-6)
IEEE DOI
2208
Deep learning, Systematics, Image processing,
Motion estimation, Estimation, Signal processing algorithms,
deep learning
BibRef
Zhang, F.H.[Fei-Hu],
Woodford, O.J.[Oliver J.],
Prisacariu, V.[Victor],
Torr, P.H.S.[Philip H. S.],
Separable Flow: Learning Motion Cost Volumes for Optical Flow
Estimation,
ICCV21(10787-10797)
IEEE DOI
2203
Knowledge engineering, Image motion analysis, Costs, Correlation,
Estimation, Benchmark testing, Motion and tracking,
BibRef
Li, H.P.[Hai-Peng],
Luo, K.M.[Kun-Ming],
Liu, S.C.[Shuai-Cheng],
GyroFlow: Gyroscope-Guided Unsupervised Optical Flow Learning,
ICCV21(12849-12858)
IEEE DOI
2203
Optical fibers, Measurement, Rain, Codes, Fuses, Brightness,
Motion and tracking, Datasets and evaluation,
Vision + other modalities
BibRef
Jeny, A.A.[Afsana Ahsan],
Islam, M.B.[Md Baharul],
Aydin, T.[Tarkan],
DeepPyNet: A Deep Feature Pyramid Network for Optical Flow Estimation,
IVCNZ21(1-6)
IEEE DOI
2201
Visualization, Image motion analysis, Correlation, Refining,
Neural networks, Estimation, Feature extraction,
Iterative recurrent unit
BibRef
Sun, D.[Deqing],
Vlasic, D.[Daniel],
Herrmann, C.[Charles],
Jampani, V.[Varun],
Krainin, M.[Michael],
Chang, H.[Huiwen],
Zabih, R.[Ramin],
Freeman, W.T.[William T.],
Liu, C.[Ce],
AutoFlow: Learning a Better Training Set for Optical Flow,
CVPR21(10088-10097)
IEEE DOI
2111
Training, Adaptation models, Technological innovation,
Solid modeling, Shape, Training data
BibRef
Poggi, M.[Matteo],
Aleotti, F.[Filippo],
Mattoccia, S.[Stefano],
Sensor-Guided Optical Flow,
ICCV21(7888-7898)
IEEE DOI
2203
Geometry, Image motion analysis, Optical variables measurement,
Prediction algorithms, Sensors, Reliability,
Vision for robotics and autonomous vehicles
BibRef
Aleotti, F.[Filippo],
Poggi, M.[Matteo],
Mattoccia, S.[Stefano],
Learning optical flow from still images,
CVPR21(15196-15206)
IEEE DOI
2111
Training, Computational modeling, Training data,
Estimation, Data models, Pattern recognition
BibRef
Jiao, Y.[Yang],
Shi, G.M.[Guang-Ming],
Tran, T.D.[Trac D.],
Optical Flow Estimation Via Motion Feature Recovery,
ICIP21(2558-2562)
IEEE DOI
2201
Solid modeling, Adaptive learning, Adaptation models, Correlation,
Estimation, Benchmark testing, CNN, Optical Flow, Cost Volume,
Motion Consistency
BibRef
Jiao, Y.[Yang],
Tran, T.D.[Trac D.],
Shi, G.M.[Guang-Ming],
EffiScene: Efficient Per-Pixel Rigidity Inference for Unsupervised
Joint Learning of Optical Flow, Depth, Camera Pose and Motion
Segmentation,
CVPR21(5534-5543)
IEEE DOI
2111
Couplings, Image motion analysis,
Motion segmentation, Estimation, Benchmark testing, Cameras
BibRef
Jiang, S.H.[Shi-Hao],
Lu, Y.[Yao],
Li, H.D.[Hong-Dong],
Hartley, R.I.[Richard I.],
Learning Optical Flow from a Few Matches,
CVPR21(16587-16595)
IEEE DOI
2111
Training, Solid modeling, Correlation, Limiting,
Computational modeling, Memory management, Estimation
BibRef
Artizzu, C.O.[Charles-Olivier],
Zhang, H.Z.[Hao-Zhou],
Allibert, G.[Guillaume],
Demonceaux, C.[Cédric],
OmniFlowNet: a Perspective Neural Network Adaptation for Optical Flow
Estimation in Omnidirectional Images,
ICPR21(2657-2662)
IEEE DOI
2105
Training, Convolution, Image processing, Neural networks, Estimation,
Optical distortion, Distortion
BibRef
Yu, S.J.[Suihan-Jin],
Zhang, Y.M.[You-Min],
Wang, C.[Chen],
Bai, X.[Xiao],
Zhang, L.[Liang],
Hancock, E.R.[Edwin R.],
HMFlow: Hybrid Matching Optical Flow Network for Small and
Fast-Moving Objects,
ICPR21(1197-1204)
IEEE DOI
2105
Image resolution, Estimation, Search problems, Pattern recognition,
Task analysis, Optical flow
BibRef
Xie, S.,
Lai, P.K.,
Laganiere, R.,
Lang, J.,
Effective Convolutional Neural Network Layers in Flow Estimation for
Omni-Directional Images,
3DV19(671-680)
IEEE DOI
1911
Estimation, Optical imaging, Convolution, Optical computing,
Adaptive optics, Optical fiber networks, Neural networks, neural network
BibRef
Lu, Y.[Yao],
Valmadre, J.[Jack],
Wang, H.[Heng],
Kannala, J.H.[Ju-Ho],
Harandi, M.[Mehrtash],
Torr, P.H.S.[Philip H. S.],
Devon: Deformable Volume Network for Learning Optical Flow,
WACV20(2694-2702)
IEEE DOI
2006
BibRef
Earlier:
OpticalFlow18(VI:673-677).
Springer DOI
1905
Optical imaging, Optical computing, Optical distortion,
Neural networks, Optical fiber networks, Optical propagation, Estimation
BibRef
Yang, Y.C.[Yan-Chao],
Soatto, S.[Stefano],
Conditional Prior Networks for Optical Flow,
ECCV18(XV: 282-298).
Springer DOI
1810
BibRef
Chapter on Optical Flow Field Computations and Use continues in
Scene Flow, Depth Image Flow, RGB-D .