18.3.2 Opeical Flow, Learning, Neural Networks, GAN

Chapter Contents (Back)
Optical Flow. Learning. Neural Networks. GAN.

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. BibRef

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
BibRef

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 9904

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


Liang, Y.P.[Ying-Ping], Liu, J.M.[Jia-Ming], Zhang, D.[Debing], Fu, Y.[Ying],
MPI-Flow: Learning Realistic Optical Flow with Multiplane Images,
ICCV23(13811-13822)
IEEE DOI Code:
WWW Link. 2401
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 .


Last update:Apr 10, 2024 at 09:54:40