Marshall, J.A.,
Kalarickal, G.J.,
Graves, E.B.,
Neural Model of Visual Stereomatching: Slant, Transparency and Clouds,
NetCompNeur(7), No. 4, November 1996, pp. 635-669.
9701
BibRef
Lee, J.J.[Jun Jae],
Shim, J.C.[Jae Chang],
Ha, Y.H.[Yeong Ho],
Stereo Correspondence Using the Hopfield Neural-Network of a
New Energy Function,
PR(27), No. 11, November 1994, pp. 1513-1522.
Elsevier DOI
BibRef
9411
de la Cruz, J.M.,
Pajares, G.,
Aranda, J.,
A Neural-Network Model in Stereovision Matching,
NeurNet(8), No. 5, 1995, pp. 805-813.
BibRef
9500
Pajares, G.,
de la Cruz, J.M.,
Aranda, J.,
Stereo Matching Based on the Self-Organizing Feature-Mapping Algorithm,
PRL(19), No. 3-4, March 1998, pp. 319-330.
9807
BibRef
Pajares, G.[Gonzalo],
de la Cruz, J.M.[Jesús M.],
Aranda, J.[Joaquin],
Relaxation by Hopfield Network in Stereo Image Matching,
PR(31), No. 5, May 1998, pp. 561-574.
Elsevier DOI
9805
BibRef
Pajares, G.[Gonzalo],
de la Cruz, J.M.[Jesús Manuel],
López-Orozco, J.A.[José Antonio],
Relaxation labeling in stereo image matching,
PR(33), No. 1, January 2000, pp. 53-68.
Elsevier DOI
0005
See also Fuzzy Cognitive Maps for stereovision matching.
BibRef
Pajares, G.[Gonzalo],
López-Orozco, J.A.[José Antonio],
de la Cruz, J.M.[Jesús Manuel],
Improving Stereovision Matching through Supervised Learning,
PAA(1), No. 2, 1998, pp. 105-120.
BibRef
9800
Pajares, G.[Gonzalo],
de la Cruz, J.M.[Jesús Manuel],
Stereovision matching through support vector machines,
PRL(24), No. 15, November 2003, pp. 2575-2583.
Elsevier DOI
0308
Local matching.
BibRef
Pajares, G.[Gonzalo],
de la Cruz, J.M.[Jesús Manuel],
On Combining Support Vector Machines and Simulated Annealing in
Stereovision Matching,
SMC-B(34), No. 4, August 2004, pp. 1646-1657.
IEEE Abstract.
0409
BibRef
Pajares, G.[Gonzalo],
de la Cruz, J.M.[Jesus M.],
Pattern Recognition Learning Applied to Stereovision Matching,
ICPR98(PRP1).
9808
Not online.
BibRef
Pajares, G.[Gonzalo],
de la Cruz, J.M.[Jesús M.],
Fuzzy Cognitive Maps for stereovision matching,
PR(39), No. 11, November 2006, pp. 2101-2114.
Elsevier DOI
0608
Fuzzy Cognitive Maps; Fuzzy Clustering; Relaxation; Fuzzy;
Stereovision; Matching; Similarity; Smoothness; Ordering; Epipolar;
Uniqueness
See also Relaxation labeling in stereo image matching.
BibRef
Herrera, P.J.[P. Javier],
Pajares, G.[Gonzalo],
Guijarro, M.[María],
Ruz, J.J.[J. Jaime],
de la Cruz, J.M.[Jesús M.],
Combination of Attributes in Stereovision Matching for Fish-Eye Lenses
in Forest Analysis,
ACIVS09(277-287).
Springer DOI
0909
BibRef
Pajares, G.[Gonzalo],
de la Cruz, J.M.[Jesús M.],
Local stereovision matching through the ADALINE neural network,
PRL(22), No. 14, December 2001, pp. 1457-1473.
Elsevier DOI
0110
BibRef
Wang, Z.[Zhi],
Zhu, S.Q.[Shi-Qiang],
Li, Y.H.[Yue-Hua],
Cui, Z.Z.[Zheng-Zhe],
Convolutional neural network based deep conditional random fields for
stereo matching,
JVCIR(40, Part B), No. 1, 2016, pp. 739-750.
Elsevier DOI
1610
Stereo matching
BibRef
Brandao, P.[Patrick],
Mazomenos, E.[Evangelos],
Stoyanov, D.[Danail],
Widening siamese architectures for stereo matching,
PRL(120), 2019, pp. 75-81.
Elsevier DOI
1904
Stereo matching, Convolutional neural network, Disparity
BibRef
Nguyen, T.P.[Tien Phuoc],
Jeon, J.W.[Jae Wook],
Wide context learning network for stereo matching,
SP:IC(78), 2019, pp. 263-273.
Elsevier DOI
1909
Stereo matching, 3D reconstruction, Matching cost,
Cost aggregation, Convolutional neural network
BibRef
Lei, J.F.[Jun-Feng],
Dong, Y.X.[Yu-Xuan],
Zhao, T.[Tao],
xiao, J.S.[Jin-Sheng],
Chen, Y.H.[Yun-Hua],
Li, B.J.[Bi-Jun],
Novel shrinking residual convolutional neural network for efficient
accurate stereo matching,
JVCIR(72), 2020, pp. 102872.
Elsevier DOI
2010
Stereo matching, Matching cost, Residual convolutional neural network
BibRef
Liang, Z.F.[Zheng-Fa],
Guo, Y.L.[Yu-Lan],
Feng, Y.L.[Yi-Liu],
Chen, W.[Wei],
Qiao, L.B.[Lin-Bo],
Zhou, L.[Li],
Zhang, J.F.[Jian-Feng],
Liu, H.Z.[Heng-Zhu],
Stereo Matching Using Multi-Level Cost Volume and Multi-Scale Feature
Constancy,
PAMI(43), No. 1, January 2021, pp. 300-315.
IEEE DOI
2012
Estimation, Feature extraction, Image reconstruction, Convolution,
Task analysis, Convolutional neural networks, Training, feature constancy
BibRef
Zhang, Y.[Yaru],
Li, Y.[Yaqian],
Wu, C.[Chao],
Liu, B.[Bin],
Attention-guided aggregation stereo matching network,
IVC(106), 2021, pp. 104088.
Elsevier DOI
2102
Convolutional neural network, Stereo matching,
Attention mechanism, Guided cost volume
BibRef
Sang, H.W.[Hai-Wei],
Yang, Z.L.[Zu-Liu],
Yang, X.W.[Xiao-Wei],
Zhao, Y.[Yong],
MPA-Net: multi-path attention stereo matching network,
IET-IPR(14), No. 17, 24 December 2020, pp. 4554-4562.
DOI Link
2104
BibRef
Cheng, X.J.[Xian-Jing],
Zhao, Y.[Yong],
Yang, W.B.[Wen-Bang],
Hu, Z.J.[Zhi-Jun],
Yu, X.M.[Xiao-Min],
Sang, H.W.[Hai-Wei],
Zhang, G.Y.[Gui-Ying],
LESC: Superpixel cut-based local expansion for accurate stereo
matching,
IET-IPR(16), No. 2, 2022, pp. 470-484.
DOI Link
2201
BibRef
Shao, X.T.[Xiao-Tao],
Zhang, W.[Wen],
Guo, M.K.[Ming-Kun],
Guo, S.Q.[Si-Qi],
Qian, M.Y.[Man-Yi],
CSNet: Cascade stereo matching network using multi-information cost
volume,
IET-ITS(15), No. 5, 2021, pp. 635-645.
DOI Link
2106
BibRef
Wang, L.G.[Long-Guang],
Guo, Y.L.[Yu-Lan],
Wang, Y.Q.[Ying-Qian],
Liang, Z.F.[Zheng-Fa],
Lin, Z.P.[Zai-Ping],
Yang, J.G.[Jun-Gang],
An, W.[Wei],
Parallax Attention for Unsupervised Stereo Correspondence Learning,
PAMI(44), No. 4, April 2022, pp. 2108-2125.
IEEE DOI
2203
Task analysis, Cameras, Correlation, Aggregates, Parallax attention,
stereo matching, image super-resolution, unsupervised learning,
stereo correspondence
BibRef
Chen, S.L.[Sheng-Lun],
Zhang, H.[Hong],
Sun, B.[Baoli],
Li, H.J.[Hao-Jie],
Ye, X.C.[Xin-Chen],
Wang, Z.H.[Zhi-Hui],
Feature enhancement network for stereo matching,
IVC(130), 2023, pp. 104614.
Elsevier DOI
2301
Stereo matching, Deep learning, Pyramid feature representation,
Cross-modal fusion, Disparity weight loss
BibRef
Han, Q.H.[Qi-Hui],
Jung, C.[Cheolkon],
Cross Spectral Disparity Estimation From VIS and NIR Paired Images
Using Disentangled Representation and Reversible Neural Networks,
ITS(24), No. 5, May 2023, pp. 5326-5336.
IEEE DOI
2305
Feature extraction, Estimation, Semantic segmentation, Neural networks,
Semantics, Couplings, Task analysis, Cross spectral, stereo matching
BibRef
Mao, Y.M.[Ya-Min],
Liu, Z.H.[Zhi-Hua],
Li, W.M.[Wei-Ming],
Dai, Y.C.[Yu-Chao],
Wang, Q.[Qiang],
Kim, Y.T.[Yun-Tae],
Lee, H.S.[Hong-Seok],
UASNet: Uncertainty Adaptive Sampling Network for Deep Stereo
Matching,
ICCV21(6291-6299)
IEEE DOI
2203
Adaptation models, Uncertainty, Costs, Adaptive systems,
Benchmark testing, Predictive models, Stereo,
Vision for robotics and autonomous vehicles
BibRef
Shamsafar, F.[Faranak],
Woerz, S.[Samuel],
Rahim, R.[Rafia],
Zell, A.[Andreas],
MobileStereoNet:
Towards Lightweight Deep Networks for Stereo Matching,
WACV22(677-686)
IEEE DOI
2202
Convolutional codes, Solid modeling,
Costs, Computational modeling, Vision Systems and Applications
BibRef
Rahim, R.[Rafia],
Shamsafar, F.[Faranak],
Zell, A.[Andreas],
Separable Convolutions for Optimizing 3D Stereo Networks,
ICIP21(3208-3212)
IEEE DOI
2201
Deep learning, Image processing, Computational complexity,
Stereo Matching, Separable Convolutions, Disparity Estimation, CNNs
BibRef
Hou, Y.X.[Yu-Xin],
Janjua, M.K.[Muhammad Kamran],
Kannala, J.H.[Ju-Ho],
Solin, A.[Arno],
Movement-induced Priors for Deep Stereo,
ICPR21(3628-3635)
IEEE DOI
2105
Training, Micromechanical devices, Handheld computers, Estimation,
Gaussian processes, Sensors
BibRef
Hirner, D.[Dominik],
Fraundorfer, F.[Friedrich],
FC-DCNN: A densely connected neural network for stereo estimation,
ICPR21(2482-2489)
IEEE DOI
2105
Matched filters, Image segmentation,
Filtering, Neural networks, Estimation, Benchmark testing
BibRef
Poggi, M.[Matteo],
Tosi, F.[Fabio],
Aleotti, F.[Filippo],
Mattoccia, S.[Stefano],
Leveraging a weakly adversarial paradigm for joint learning of
disparity and confidence estimation,
ICPR21(270-277)
IEEE DOI
2105
Training, Wide area networks, Uncertainty, Estimation,
Deep architecture, Training data, Pattern recognition
BibRef
Zhang, J.Y.[Jing-Yang],
Yao, Y.[Yao],
Luo, Z.X.[Zi-Xin],
Li, S.[Shiwei],
Shen, T.W.[Tian-Wei],
Fang, T.[Tian],
Quan, L.[Long],
Learning Stereo Matchability in Disparity Regression Networks,
ICPR21(1611-1618)
IEEE DOI
2105
Training, Refining, Benchmark testing,
Attenuation, Entropy, Pattern recognition
BibRef
Beaupre, D.A.[David-Alexandre],
Bilodeau, G.A.[Guillaume-Alexandre],
Domain Siamese CNNs for Sparse Multispectral Disparity Estimation,
ICPR21(3667-3674)
IEEE DOI
2105
Visualization, Correlation, Merging, Estimation, Feature extraction,
Robustness, Surface texture
BibRef
Xing, J.B.[Jia-Bin],
Qi, Z.[Zhi],
Dong, J.Y.[Ji-Ying],
Cai, J.X.[Jia-Xuan],
Liu, H.[Hao],
Mabnet: A Lightweight Stereo Network Based on Multibranch Adjustable
Bottleneck Module,
ECCV20(XXVIII:340-356).
Springer DOI
2011
BibRef
Zhang, F.H.[Fei-Hu],
Qi, X.J.[Xiao-Juan],
Yang, R.G.[Rui-Gang],
Prisacariu, V.[Victor],
Wah, B.W.[Benjamin W.],
Torr, P.H.S.[Philip H.S.],
Domain-invariant Stereo Matching Networks,
ECCV20(II:420-439).
Springer DOI
2011
BibRef
Chen, Y.[Yang],
Lu, Z.Q.[Zong-Qing],
Zhang, X.C.[Xue-Chen],
Chen, L.[Lei],
Liao, Q.M.[Qing-Min],
Noise-Sampling Cross Entropy Loss:
Improving Disparity Regression Via Cost Volume Aware Regularizer,
ICIP20(2780-2784)
IEEE DOI
2011
Entropy, Estimation, Laplace equations, Neural networks,
Machine learning, Coherence, Training, Stereo matching, cost volume,
noise-sampling
BibRef
Liu, R.,
Yang, C.,
Sun, W.,
Wang, X.,
Li, H.,
StereoGAN: Bridging Synthetic-to-Real Domain Gap by Joint
Optimization of Domain Translation and Stereo Matching,
CVPR20(12754-12763)
IEEE DOI
2008
Training, Optimization, Estimation, Neural networks, Correlation,
Pattern matching, Distortion
BibRef
Xu, H.F.[Hao-Fei],
Zhang, J.Y.[Ju-Yong],
AANet: Adaptive Aggregation Network for Efficient Stereo Matching,
CVPR20(1956-1965)
IEEE DOI
2008
Feature extraction, Adaptive systems, Correlation, Neural networks
BibRef
Renteria-Vidales, O.I.,
Cuevas-Tello, J.C.,
Reyes-Figueroa, A.,
Rivera, M.,
Modulenet: A Convolutional Neural Network for Stereo Vision,
MCPR20(219-228).
Springer DOI
2007
BibRef
Zhang, F.H.[Fei-Hu],
Prisacariu, V.[Victor],
Yang, R.G.[Rui-Gang],
Torr, P.H.S.[Philip H.S.],
GA-Net: Guided Aggregation Net for End-To-End Stereo Matching,
CVPR19(185-194).
IEEE DOI
2002
BibRef
Wu, Z.,
Wu, X.,
Zhang, X.,
Wang, S.,
Ju, L.,
Semantic Stereo Matching With Pyramid Cost Volumes,
ICCV19(7483-7492)
IEEE DOI
2004
convolutional neural nets, feature extraction, image matching,
image segmentation, learning (artificial intelligence), Optical imaging
BibRef
Chen, C.,
Chen, X.,
Cheng, H.,
On the Over-Smoothing Problem of CNN Based Disparity Estimation,
ICCV19(8996-9004)
IEEE DOI
2004
Code, Stereo.
WWW Link. convolutional neural nets, estimation theory, image segmentation,
learning (artificial intelligence), probability, Entropy
BibRef
Liang, Z.,
Feng, Y.,
Guo, Y.,
Liu, H.,
Chen, W.,
Qiao, L.,
Zhou, L.,
Zhang, J.,
Learning for Disparity Estimation Through Feature Constancy,
CVPR18(2811-2820)
IEEE DOI
1812
Estimation, Convolution, Correlation, Feature extraction,
Image reconstruction, Network architecture
BibRef
Seabright, M.,
Streeter, L.,
Cree, M.,
Duke, M.,
Tighe, R.,
Simple Stereo Matching Algorithm for Localising Keypoints in a
Restricted Search Space,
IVCNZ18(1-6)
IEEE DOI
1902
Cameras, Geometry, Calibration,
Approximation algorithms, Search problems, Yield estimation,
convolutional neural networks
BibRef
Mao, W.,
Gong, M.,
Disparity Filtering with 3D Convolutional Neural Networks,
CRV18(246-253)
IEEE DOI
1812
Training, Optimization, 3D CNNs
BibRef
Moskvyak, O.[Olga],
Maire, F.[Frederic],
Learning Geometric Equivalence between Patterns Using Embedding
Neural Networks,
DICTA17(1-8)
IEEE DOI
1804
Equivalence: 2 dfifferent views of the same object.
biology computing, cameras, ecology,
equivalence classes, geometry, image recognition,
Training
BibRef
Pang, J.,
Sun, W.,
Ren, J.S.,
Yang, C.,
Yan, Q.,
Cascade Residual Learning:
A Two-Stage Convolutional Neural Network for Stereo Matching,
DeepLearn-G17(878-886)
IEEE DOI
1802
Convolution, Estimation, Optimization, Training
BibRef
Zhou, C.,
Zhang, H.,
Shen, X.,
Jia, J.,
Unsupervised Learning of Stereo Matching,
ICCV17(1576-1584)
IEEE DOI
1802
convolution, image matching, iterative methods, neural nets,
random processes, stereo image processing, unsupervised learning,
Unsupervised learning
BibRef
Seki, A.[Akihito],
Pollefeys, M.[Marc],
SGM-Nets: Semi-Global Matching with Neural Networks,
CVPR17(6640-6649)
IEEE DOI
1711
BibRef
And:
Patch Based Confidence Prediction for Dense Disparity Map,
BMVC16(xx-yy).
HTML Version.
1805
Semi-Global Matching.
Estimation, Image edge detection, Neural networks,
Pipelines, Standards, Testing
BibRef
Yuan, W.,
Fan, Z.,
Yuan, X.,
Gong, J.,
Shibasaki, R.,
Unsupervised Multi-constraint Deep Neural Network for Dense Image
Matching,
ISPRS20(B2:163-167).
DOI Link
2012
BibRef
Yuan, W.[Wei],
Chen, S.Y.[Shi-Yu],
Zhang, Y.[Yong],
Gong, J.Y.[Jian-Ya],
Shibasaki, R.[Ryosuke],
An Aerial-image Dense Matching Approach Based On Optical Flow Field,
ISPRS16(B3: 543-548).
DOI Link
1610
BibRef
Zbontar, J.[Jure],
Le Cun, Y.L.[Yann L.],
Computing the stereo matching cost with a convolutional neural
network,
CVPR15(1592-1599)
IEEE DOI
1510
BibRef
Thomas, B.,
Yegnanarayana, B.,
Das, S.,
Stereo-correspondence using Gabor logons and neural networks,
ICIP95(II: 386-389).
IEEE DOI
9510
BibRef
Chapter on Stereo: Three Dimensional Descriptions from Two or More Views, Binocular, Trinocular continues in
Multi-Scale Matching for Stereo .