Gallo, I.[Ignazio],
Binaghi, E.[Elisabetta],
Raspanti, M.[Mario],
Neural disparity computation for dense two-frame stereo correspondence,
PRL(29), No. 5, 1 April 2008, pp. 673-687.
Elsevier DOI
0802
Stereo matching; Occlusion; Disparity space; Neural networks
BibRef
Vanetti, M.[Marco],
Gallo, I.[Ignazio],
Binaghi, E.[Elisabetta],
Dense Two-Frame Stereo Correspondence by Self-organizing Neural Network,
CIAP09(1035-1042).
Springer DOI
0909
BibRef
Gallo, I.[Ignazio],
Binaghi, E.[Elisabetta],
Dense Stereo Matching with Growing Aggregation and Neural Learning,
VISAPP06(343-353).
Springer DOI
0711
BibRef
Agarwal, A.[Ankur],
Blake, A.[Andrew],
Dense Stereo Matching over the Panum Band,
PAMI(32), No. 3, March 2010, pp. 416-430.
IEEE DOI
1002
BibRef
Earlier:
The Panum Proxy Algorithm for Dense Stereo Matching over a Volume of
Interest,
CVPR06(II: 2339-2346).
IEEE DOI
0606
Panum Band: human stereo works over a narrow band of disparities.
Using narrower range of disparities improves computation times.
BibRef
Stentoumis, C.,
Grammatikopoulos, L.,
Kalisperakis, I.,
Karras, G.E.,
On accurate dense stereo-matching using a local adaptive multi-cost
approach,
PandRS(91), No. 1, 2014, pp. 29-49.
Elsevier DOI
1404
BibRef
Earlier:
Implementing An Adaptive Approach For Dense Stereo-matching,
ISPRS12(XXXIX-B5:309-314).
DOI Link
1209
BibRef
Stentoumis, C.,
Grammatikopoulos, L.,
Kalisperakis, I.,
Petsa, E.,
Karras, G.E.,
A Local Adaptive Approach for Dense Stereo Matching in Architectural
Scene Reconstruction,
3DARCH13(219-226).
DOI Link
1308
BibRef
Kalisperakis, I.[Ilias],
Karras, G.E.[George E.],
Grammatikopoulos, L.[Lazaros],
3D Aspects of 2D Epipolar Geometry,
PCV06(xx-yy).
PDF File.
0609
BibRef
Joglekar, J.,
Gedam, S.S.,
Mohan, B.K.,
Image Matching Using SIFT Features and Relaxation Labeling Technique:
A Constraint Initializing Method for Dense Stereo Matching,
GeoRS(52), No. 9, September 2014, pp. 5643-5652.
IEEE DOI
1407
Bayes methods
BibRef
Mahato, M.,
Gedam, S.,
Joglekar, J.,
Buddhiraju, K.M.,
Dense Stereo Matching Based on Multiobjective Fitness Function: A
Genetic Algorithm Optimization Approach for Stereo Correspondence,
GeoRS(57), No. 6, June 2019, pp. 3341-3353.
IEEE DOI
1906
Optimization, Genetic algorithms, Image matching, Sociology,
Statistics, Remote sensing, Geometry, Feature extraction,
stereo vision
BibRef
Gonzalez-Huitron, V.[Victor],
Ponomaryov, V.[Volodymyr],
Ramos-Diaz, E.[Eduardo],
Sadovnychiy, S.[Sergiy],
Parallel framework for dense disparity map estimation using Hamming
distance,
SIViP(12), No. 2, February 2018, pp. 231-238.
Springer DOI
1802
BibRef
Liu, J.,
Zhang, L.,
Wang, Z.,
Wang, R.,
Dense Stereo Matching Strategy for Oblique Images That Considers the
Plane Directions in Urban Areas,
GeoRS(58), No. 7, July 2020, pp. 5109-5116.
IEEE DOI
2006
Distortion, Urban areas, Machine learning, Image reconstruction,
Robustness, Buildings, Image resolution, Dense matching,
oblique images
BibRef
Han, Y.L.[Yi-Long],
Liu, W.[Wei],
Huang, X.[Xu],
Wang, S.G.[Shu-Gen],
Qin, R.J.[Rong-Jun],
Stereo Dense Image Matching by Adaptive Fusion of Multiple-Window
Matching Results,
RS(12), No. 19, 2020, pp. xx-yy.
DOI Link
2010
BibRef
Zhang, C.X.[Cong-Xuan],
Wu, J.J.[Jun-Jie],
Chen, Z.[Zhen],
Liu, W.[Wen],
Li, M.[Ming],
Jiang, S.F.[Shao-Feng],
Dense-CNN: Dense convolutional neural network for stereo matching
using multiscale feature connection,
SP:IC(95), 2021, pp. 116285.
Elsevier DOI
2106
Stereo matching, Cost volume, Multiscale features,
Dense convolutional neural network
BibRef
Peng, Y.P.[Ye-Ping],
Xu, J.R.[Jian-Rui],
Cao, G.Z.[Guang-Zhong],
Zeng, R.H.[Run-Hao],
Binocular-Separated Modeling for Efficient Binocular Stereo Matching,
ITS(26), No. 3, March 2025, pp. 3028-3038.
IEEE DOI
2503
Feature extraction, Costs, Accuracy, Correlation, Training,
Computational modeling, Estimation, Data mining,
multi-dilation fusion
BibRef
Giang, K.T.[Khang Truong],
Song, S.[Soohwan],
Jo, S.[Sungho],
Learning to Produce Semi-Dense Correspondences for Visual
Localization,
CVPR24(19468-19478)
IEEE DOI Code:
WWW Link.
2410
Location awareness, Visualization, Solid modeling, Accuracy,
Pose estimation, visual localization, 3D reconstruction
BibRef
Edstedt, J.[Johan],
Sun, Q.Y.[Qi-Yu],
Bökman, G.[Georg],
Wadenbäck, M.[Mårten],
Felsberg, M.[Michael],
RoMa: Robust Dense Feature Matching,
CVPR24(19790-19800)
IEEE DOI
2410
Codes, Computational modeling, Benchmark testing, Transformers,
Robustness, image matching, feature matching, geometry estimation
BibRef
Mehltretter, M.[Max],
Kleinschmidt, S.P.[Sebastian P.],
Wagner, B.[Bernardo],
Heipke, C.[Christian],
Multimodal Dense Stereo Matching,
GCPR18(407-421).
Springer DOI
1905
BibRef
Mao, W.,
Wang, M.,
Zhou, J.,
Gong, M.,
Semi-Dense Stereo Matching Using Dual CNNs,
WACV19(1588-1597)
IEEE DOI
1904
convolutional neural nets, image matching,
learning (artificial intelligence), stereo image processing,
BibRef
Tan, P.,
Chambolle, A.,
Monasse, P.,
Occlusion detection in dense stereo estimation with convex
optimization,
ICIP17(2543-2547)
IEEE DOI
1803
Cameras, Estimation, Image color analysis, Minimization, Robustness,
TV, Weight measurement, Stereo-matching, convex relaxation,
variational method
BibRef
Poggi, M.[Matteo],
Mattoccia, S.[Stefano],
Evaluation of variants of the SGM algorithm aimed at implementation
on embedded or reconfigurable devices,
IC3D16(1-8)
IEEE DOI
1703
Semi Global Matching.
Dense depth from stereo.
computer vision
BibRef
Nahar, S.,
Joshi, M.V.,
Dense disparity estimation based on feature matching and IGMRF
regularization,
ICPR16(3804-3809)
IEEE DOI
1705
BibRef
Earlier:
A regularization framework for stereo matching using IGMRF prior and
sparseness learned from autoencoder,
ICIP16(3434-3438)
IEEE DOI
1610
BibRef
Earlier:
A learning based approach for dense stereo matching with IGMRF prior,
NCVPRIPG13(1-4)
IEEE DOI
1408
Gaussian processes.
Estimation, Feature extraction, Image edge detection,
Image segmentation, Minimization, Robustness, Training.
Discrete cosine transforms
BibRef
Hamilton, O.K.,
Breckon, T.P.[Toby P.],
Generalized dynamic object removal for dense stereo vision based
scene mapping using synthesised optical flow,
ICIP16(3439-3443)
IEEE DOI
1610
Cameras
BibRef
Chuang, T.Y.,
Ting, H.W.,
Jaw, J.J.,
Hybrid-based Dense Stereo Matching,
ISPRS16(B3: 495-501).
DOI Link
1610
BibRef
Monteiro, N.B.[Nuno Barroso],
Barreto, J.P.[João Pedro],
Gaspar, J.[José],
Dense Lightfield Disparity Estimation Using Total Variation
Regularization,
ICIAR16(462-469).
Springer DOI
1608
BibRef
Ma, R.[Rui],
Au, O.C.,
Wan, P.F.[Peng-Fei],
Sun, W.X.[Wen-Xiu],
Xu, L.F.[Ling-Feng],
Jia, L.H.[Lu-Heng],
Solving dense stereo matching via quadratic programming,
VCIP14(370-373)
IEEE DOI
1504
computational complexity
BibRef
Ha, J.[Jeong_Mok],
Jeon, J.[Jea_Young],
Bae, G.[Gi_Yeong],
Jo, S.[Sung_Yong],
Jeong, H.[Hong],
Cost Aggregation Table: Cost Aggregation Method Using Summed Area Table
Scheme for Dense Stereo Correspondence,
ISVC14(I: 815-826).
Springer DOI
1501
BibRef
Irschara, A.,
Rumpler, M.,
Meixner, P.,
Pock, T.,
Bischof, H.,
Efficient and Globally Optimal Multi View Dense Matching for Aerial
Images,
AnnalsPRS(I-3), No. 2012, pp. 227-232.
DOI Link
1209
BibRef
Hu, T.,
Wu, H.,
Dense Corresponding Pixel Matching Using A Fixed Window with RGB
Independent Information,
AnnalsPRS(I-4), No. 2012, pp. 89-93.
DOI Link
1209
BibRef
Li, Y.J.[Yu-Jun],
Au, O.C.[Oscar C.],
Xu, L.F.[Ling-Feng],
Sun, W.X.[Wen-Xiu],
Chui, S.H.[Sung-Him],
Kwok, C.W.[Chun-Wing],
A convex-optimization approach to dense stereo matching,
ICIP11(1005-1008).
IEEE DOI
1201
BibRef
Lang, H.T.[Hai-Tao],
Wang, Y.T.[Yong-Tian],
Qi, X.[Xin],
Pan, W.Q.[Wei-Qing],
Enhanced point descriptors for dense stereo matching,
IASP10(228-231).
IEEE DOI
1004
BibRef
Zhao, G.Q.[Gang-Qiang],
Chen, L.[Ling],
Chen, G.C.[Gen-Cai],
A Speeded-Up Local Descriptor for dense stereo matching,
ICIP09(2101-2104).
IEEE DOI
0911
BibRef
Somanath, G.[Gowri],
Rohith, M.V.,
Metaxas, D.N.[Dmitris N.],
Kambhamettu, C.[Chandra],
D-Clutter: Building object model library from unsupervised
segmentation of cluttered scenes,
CVPR09(2783-2789).
IEEE DOI
0906
BibRef
Rohith, M.V.,
Kambhamettu, C.[Chandra],
Learning Image Structures for Optimizing Disparity Estimation,
ACCV10(III: 627-640).
Springer DOI
1011
BibRef
Rohith, M.V.,
Somanath, G.[Gowri],
Kambhamettu, C.[Chandra],
Geiger, C.[Cathleen],
Finnegan, D.[David],
Modified Region Growing for Stereo of Slant and Textureless Surfaces,
ISVC10(I: 666-677).
Springer DOI
1011
BibRef
Earlier: A1, A2, A3, A4, Only:
Towards estimation of dense disparities from stereo images containing
large textureless regions,
ICPR08(1-5).
IEEE DOI
0812
BibRef
Payeur, P.[Pierre],
Desjardins, D.[Danick],
Structured Light Stereoscopic Imaging with Dynamic Pseudo-random
Patterns,
ICIAR09(687-696).
Springer DOI
0907
BibRef
Earlier: A2, A1:
Dense Stereo Range Sensing with Marching Pseudo-Random Patterns,
CRV07(216-226).
IEEE DOI
0705
BibRef
Jagmohan, A.,
Singh, M.,
Ahuja, N.,
Dense stereo matching using kernel maximum likelihood estimation,
ICPR04(III: 28-31).
IEEE DOI
0409
BibRef
Bovyrin, A.,
Eruhimov, V.,
Molinov, S.,
Mosyagin, V.,
Pisarevsky, V.,
Fast and robust dense stereo correspondence by column segmentation,
ICIP03(III: 1033-1036).
IEEE DOI
0312
BibRef
Kostkova, J.,
Sara, R.,
Stratified Dense Matching for Stereopsis in Complex Scenes,
BMVC03(xx-yy).
HTML Version.
0409
BibRef
Jin, K.,
Boufama, B.,
Towards a Fast and Reliable Dense Matching Algorithm,
VI02(178).
PDF File.
0208
BibRef
Koschan, A.F.,
Rodehorst, V.,
Spiller, K.,
Color Stereo Vision Using Hierarchical Block Matching and
Active Color Illumination,
ICPR96(I: 835-839).
IEEE DOI
9608
(Technical Univ. of Berlin, D)
BibRef
Koschan, A.F.,
Rodehorst, V.,
Towards Real-Time Stereo Employing Parallel Algorithms for
Edge-Based and Dense Stereo Matching,
CAMP95(xx).
BibRef
9500
Koschan, A.F.[Andreas F.],
Dense stereo correspondence using polychromatic block matching,
CAIP93(538-542).
Springer DOI
9309
BibRef
Chapter on Stereo: Three Dimensional Descriptions from Two or More Views, Binocular, Trinocular continues in
Matching for Stereo, Occlusion, Discontinuity Analysis .