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Stereo matching; Occlusion; Disparity space; Neural networks
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Dense Stereo Matching with Growing Aggregation and Neural Learning,
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Dense Stereo Matching over the Panum Band,
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Panum Band: human stereo works over a narrow band of disparities.
Using narrower range of disparities improves computation times.
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1404
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Implementing An Adaptive Approach For Dense Stereo-matching,
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BibRef
Stentoumis, C.,
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Kalisperakis, I.,
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1407
Bayes methods
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Joglekar, J.,
Buddhiraju, K.M.,
Dense Stereo Matching Based on Multiobjective Fitness Function: A
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GeoRS(57), No. 6, June 2019, pp. 3341-3353.
IEEE DOI
1906
Optimization, Genetic algorithms, Image matching, Sociology,
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stereo vision
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Liu, J.,
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Wang, R.,
Dense Stereo Matching Strategy for Oblique Images That Considers the
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GeoRS(58), No. 7, July 2020, pp. 5109-5116.
IEEE DOI
2006
Distortion, Urban areas, Machine learning, Image reconstruction,
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Zhang, C.X.[Cong-Xuan],
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2106
Stereo matching, Cost volume, Multiscale features,
Dense convolutional neural network
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Mao, W.,
Wang, M.,
Zhou, J.,
Gong, M.,
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WACV19(1588-1597)
IEEE DOI
1904
convolutional neural nets, image matching,
learning (artificial intelligence), stereo image processing,
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Tan, P.,
Chambolle, A.,
Monasse, P.,
Occlusion detection in dense stereo estimation with convex
optimization,
ICIP17(2543-2547)
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1803
Cameras, Estimation, Image color analysis, Minimization, Robustness,
TV, Weight measurement, Stereo-matching, convex relaxation,
variational method
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Poggi, M.[Matteo],
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Evaluation of variants of the SGM algorithm aimed at implementation
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Semi Global Matching.
Dense depth from stereo.
computer vision
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Dense disparity estimation based on feature matching and IGMRF
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ICPR16(3804-3809)
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1705
BibRef
Earlier:
A regularization framework for stereo matching using IGMRF prior and
sparseness learned from autoencoder,
ICIP16(3434-3438)
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Earlier:
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NCVPRIPG13(1-4)
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Gaussian processes.
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Generalized dynamic object removal for dense stereo vision based
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Cameras
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Chuang, T.Y.,
Ting, H.W.,
Jaw, J.J.,
Hybrid-based Dense Stereo Matching,
ISPRS16(B3: 495-501).
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Monteiro, N.B.[Nuno Barroso],
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Dense Lightfield Disparity Estimation Using Total Variation
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ICIAR16(462-469).
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1608
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Ma, R.[Rui],
Au, O.C.,
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Sun, W.X.[Wen-Xiu],
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Solving dense stereo matching via quadratic programming,
VCIP14(370-373)
IEEE DOI
1504
computational complexity
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Ha, J.[Jeong_Mok],
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Cost Aggregation Table: Cost Aggregation Method Using Summed Area Table
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ISVC14(I: 815-826).
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1501
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Irschara, A.,
Rumpler, M.,
Meixner, P.,
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Li, Y.J.[Yu-Jun],
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0906
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1011
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1011
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Earlier: A1, A2, A3, A4, Only:
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Earlier: A2, A1:
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CRV07(216-226).
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0705
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Dense stereo matching using kernel maximum likelihood estimation,
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0409
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Fast and robust dense stereo correspondence by column segmentation,
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Koschan, A.F.,
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Towards Real-Time Stereo Employing Parallel Algorithms for
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Dense stereo correspondence using polychromatic block matching,
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Chapter on Stereo: Three Dimensional Descriptions from Two or More Views, Binocular, Trinocular continues in
Matching for Stereo, Occlusion, Discontinuity Analysis .