10.1.2 Stereo Analysis: Point Matching, Low Level Feature Matching

Cottafava, G., and LeMoli, G.,
The Automated Contour Map,
CACM(12), No. 7, July 1969, pp. 386-391. BibRef 6907

Mori, K., Kidode, M., and Asada, H.,
An Iterative Prediction and Correction Method for Automatic Stereo Comparison,
CGIP(2), 1973, pp. 393-401. BibRef 7300

Dev, P.,
Perception of Depth Surfaces in Random-Dot Stereograms: A Neural Model,
MMS(7), 1978, pp. 511-528. BibRef 7800

Grimson, W.E.L.,
Computational Experiments with a Feature Based Stereo Algorithm,
PAMI(7), No. 1, January 1985, pp. 17-34. BibRef 8501
And: MIT AI Memo-762, January 1984. Stereo, Grimson. Description of the new Grimson algorithm that uses the modifications suggested by the work since 1980. The major changes are in the area of matching. Very detailed description of the algorithm and the implementation.
See also From Images to Surfaces: A Computational Study of the Human Early Visual System. BibRef

Drumheller, M., and Poggio, T.A.,
On Parallel Stereo,
CRA87(527-538). BibRef 8700
And: Same title. CRA86(1439-1448). BibRef

Drumheller, M.,
Connection Machine Stereomatching,
AAAI-86(748-753). BibRef 8600

White, S.,
Stereo Using the Displacement Representation,
DARPA92(391-399). Scale space extraction of disparities. BibRef 9200

Prazdny, K.,
Detection of Binocular Disparities,
BioCyber(52), 1985, pp. 93-99. BibRef 8500
And: RCV87(73-79). Relaxation. Generate all possible disparities, then use a relaxation procedure to get some neighborhood consistency. BibRef

Prazdny, K.,
The Role of Eye Position Information in Algorithms for Stereoscopic Matching,
AAAI-82(1-4). BibRef 8200

Jones, D.G.[David G.], Malik, J.[Jitendra],
Computational Framework for Determining Stereo Correspondence from a Set of Linear Spatial Filters,
IVC(10), No. 10, December 1992, pp. 699-708.
Elsevier DOI BibRef 9212
Earlier: ECCV92(395-410).
Springer DOI BibRef
And: UCBCSD-91-655, October 1991. BibRef

Gerstenberger, J.S.[Jeffrey S.],
Mechanism for determining parallax between digital images,
US_Patent5,220,441, Jun 15, 1993
WWW Link. Correlation matching. BibRef 9306

Kara, A.[Atsushi], Wilkes, D.M.[D. Mitchell], Kawamura, K.[Kazuhiko],
3D Structure Reconstruction from Point Correspondences between Two Perspective Projections,
CVGIP(60), No. 3, November 1994, pp. 392-397.
DOI Link BibRef 9411

Reimann, D., Haken, H.,
Stereo Vision by Self-Organization,
BioCyber(71), No. 1, 1994, pp. 17-26. Disparity at each point, relaxation process. BibRef 9400

Agouris, P., Schenk, T.,
Automated Aerotriangulation Using Multiple Image Multipoint Matching,
PhEngRS(62), No. 6, June 1996, pp. 703-710. 9606
BibRef

Toth, C.K., Krupnik, A.,
Concept, Implementation, and Results of an Automatic Aerotriangulation System,
PhEngRS(62), No. 6, June 1996, pp. 711-717. 9606
BibRef

Toth, C.K.[Charles K.], Schenk, T.[Toni],
Multiple image matching in an automatic aerotriangulation system,
CAIP93(750-758).
Springer DOI 9309
BibRef

March, R.,
Computation of Stereo Disparity Using Regularization,
PRL(8), 1988, pp. 181-187. BibRef 8800

March, R.,
A Regularization Model for Stereo Vision with Controlled Continuity,
PRL(10), 1989, pp. 259-263. BibRef 8900

Baillard, C., Dissard, O., Jamet, O., Maitre, H.,
Extraction and Textural Characterization of Aboveground Areas from Aerial Stereo Pairs: A Quality Assessment,
PandRS(53), No. 2, April 1998, pp. 130-141. 9805

See also Above-Ground Objects in Urban Scenes from Medium Scale Aerial Imagery. BibRef

Scharstein, D.[Daniel], Szeliski, R.S.[Richard S.],
Stereo Matching With Nonlinear Diffusion,
IJCV(28), No. 2, June-July 1998, pp. 155-174.
DOI Link 9808
BibRef
Earlier: CVPR96(343-350).
IEEE DOI BibRef
And: CornellComputer Science, TR96-1575, March 1996. Code, Stereo. Code:
HTML Version. Point matching using Sum of Squared Differences (SSD). BibRef

Sinha, S.N.[Sudipta Narayan], Scharstein, D.[Daniel], Szeliski, R.S.[Richard S.],
Efficient High-Resolution Stereo Matching Using Local Plane Sweeps,
CVPR14(1582-1589)
IEEE DOI 1409
plane sweep;semi-global matching;stereo matching BibRef

Scharstein, D.,
Matching Images by Comparing Their Gradient Fields,
ICPR94(A:572-575).
IEEE DOI BibRef 9400

Szeliski, R.S.[Richard S.], Scharstein, D.,
Symmetric Sub-Pixel Stereo Matching,
ECCV02(II: 525 ff.).
Springer DOI 0205
BibRef

Chen, T.Y., Bovik, A.C.[Alan C.], Cormack, L.K.[Lawrence K.],
Stereoscopic Ranging by Matching Image Modulations,
IP(8), No. 6, June 1999, pp. 785-797.
IEEE DOI BibRef 9906

Liu, Y., Cormack, L.K.[Lawrence K.], Bovik, A.C.[Alan C.],
Statistical Modeling of 3-D Natural Scenes With Application to Bayesian Stereopsis,
IP(20), No. 9, September 2011, pp. 2515-2530.
IEEE DOI 1109
BibRef

Su, C.C.[Che-Chun], Cormack, L.K.[Lawrence K.], Bovik, A.C.[Alan C.],
Color and Depth Priors in Natural Images,
IP(22), No. 6, 2013, pp. 2259-2274.
IEEE DOI stereo image processing; image discontinuity; Gabor filter bank 1307
BibRef
Earlier: A1, A3, A2:
Statistical model of color and disparity with application to Bayesian stereopsis,
Southwest12(169-172).
IEEE DOI 1205
BibRef
Earlier: A1, A3, A2:
Natural scene statistics of color and range,
ICIP11(257-260).
IEEE DOI 1201
BibRef

Su, C.C.[Che-Chun], Cormack, L.K.[Lawrence K.], Bovik, A.C.[Alan C.],
Closed-Form Correlation Model of Oriented Bandpass Natural Images,
SPLetters(22), No. 1, January 2015, pp. 21-25.
IEEE DOI 1410
image processing BibRef

Su, C.C.[Che-Chun], Cormack, L.K.[Lawrence K.], Bovik, A.C.[Alan C.],
Oriented Correlation Models of Distorted Natural Images With Application to Natural Stereopair Quality Evaluation,
IP(24), No. 5, May 2015, pp. 1685-1699.
IEEE DOI 1504
Computational modeling BibRef

Birchfield, S.T.[Stan T.], Tomasi, C.[Carlo],
Depth Discontinuities by Pixel-to-Pixel Stereo,
IJCV(35), No. 3, December 1999, pp. 269-293.
DOI Link BibRef 9912
Earlier: ICCV98(1073-1080).
IEEE DOI BibRef
And: STAN-CS--TR-96-1573, Stanford Univ. July 1996. BibRef

Mühlmann, K.[Karsten], Maier, D.[Dennis], Hesser, J.[Jürgen], Männer, R.[Reinhard],
Calculating Dense Disparity Maps from Color Stereo Images, an Efficient Implementation,
IJCV(47), No. 1-3, April-June 2002, pp. 79-88.
DOI Link 0203
BibRef
Earlier: SMBV01(xx-yy). 0110
BibRef

Wiora, G.[Georg], Babrou, P.[Pavel], Männer, R.[Reinhard],
Real Time High Speed Measurement of Photogrammetric Targets,
DAGM04(562-569).
Springer DOI 0505
BibRef

Okutomi, M.[Masatoshi], Katayama, Y.[Yasuhiro], Oka, S.[Setsuko],
A Simple Stereo Algorithm to Recover Precise Object Boundaries and Smooth Surfaces,
IJCV(47), No. 1-3, April-June 2002, pp. 261-273.
DOI Link 0203
BibRef
Earlier: CVPR01(II:138-144).
IEEE DOI 0110
BibRef
And: A1, A2 only:
Simple Stereo Algorithm to Recover Precise Object Boundaries and Smooth Surfaces,
SMBV01(xx-yy). 0110
Area based matching. Adaptive. Use multiple pairs and multiple windows to limit area matching errors. BibRef

Lhuillier, M.[Maxime], Quan, L.[Long],
Match Propagation for Image-Based Modeling and Rendering,
PAMI(24), No. 8, August 2002, pp. 1140-1146.
IEEE Abstract. 0208
Image Based Rendering. Start from sparse set of seed matches, propagate to neighbors. The procedure can generate inbetween images (blended images). BibRef

Lhuillier, M.[Maxime], Quan, L.[Long],
A Quasi-Dense Approach to Surface Reconstruction from Uncalibrated Images,
PAMI(27), No. 3, March 2005, pp. 418-433.
IEEE Abstract. 0501
BibRef
Earlier:
Quasi-Dense Reconstruction from Image Sequence,
ECCV02(II: 125 ff.).
Springer DOI 0205
BibRef
Earlier:
Robust Dense Matching Using Local and Global Geometric Constraints,
ICPR00(Vol I: 968-972).
IEEE DOI 0009
Structure from resampled dense matches rather than just feature points. BibRef

Zeng, G.[Gang], Paris, S.[Sylvain], Quan, L.[Long], Lhuillier, M.[Maxime],
Surface Reconstruction by Propagating 3D Stereo Data in Multiple 2D Images,
ECCV04(Vol I: 163-174).
Springer DOI 0405

See also Accurate and Scalable Surface Representation and Reconstruction from Images. BibRef

Lhuillier, M.,
Efficient Dense Matching for Textured Scenes using Region Growing,
BMVC98(xx-yy). BibRef 9800

Xiao, J.X.[Jian-Xiong], Chen, J.N.[Jing-Ni], Yeung, D.Y.[Dit-Yan], Quan, L.[Long],
Learning Two-View Stereo Matching,
ECCV08(III: 15-27).
Springer DOI 0810
BibRef

Binaghi, E.[Elisabetta], Gallo, I.[Ignazio], Marino, G.[Giuseppe], Raspanti, M.[Mario],
Neural adaptive stereo matching,
PRL(25), No. 15, November 2004, pp. 1743-1758.
Elsevier DOI 0411
BibRef

Binaghi, E.[Elisabetta], Gallo, I.[Ignazio], Guidali, A., Raspanti, M.[Mario], Salvini, G.,
Adaptive Neural Regularization Assignment for Semi-Blind Biomedical Image Restoration,
IMVIP07(207-207).
IEEE DOI 0709
BibRef

Sarkar, I., Bansal, M.,
A Wavelet-Based Multiresolution Approach to Solve the Stereo Correspondence Problem Using Mutual Information,
SMC-B(37), No. 4, August 2007, pp. 1009-1014.
IEEE DOI 0707
BibRef

Sizintsev, M.[Mikhail], Wildes, R.P.[Richard P.],
Coarse-to-fine stereo vision with accurate 3D boundaries,
IVC(28), No. 3, March 2010, pp. 352-366.
Elsevier DOI 1001
BibRef
Earlier:
Efficient Stereo with Accurate 3-D Boundaries,
BMVC06(I:237).
PDF File. 0609
Stereo; Coarse-to-fine; Occlusions; Real-time algorithms
See also Spatiotemporal Stereo and Scene Flow via Stequel Matching. BibRef

Sizintsev, M.[Mikhail],
Hierarchical Stereo with Thin Structures and Transparency,
CRV08(97-104).
IEEE DOI 0805
BibRef

Shi, C.B.[Chen-Bo], Wang, G.J.[Gui-Jin], Pei, X.K.[Xiao-Kang], He, B.[Bei], Lin, X.G.[Xing-Gang],
Stereo Matching Using Local Plane Fitting in Confidence-Based Support Window,
IEICE(E95-D), No. 2, February 2012, pp. 699-702.
WWW Link. 1202
BibRef

Shi, C.B.[Chen-Bo], Wang, G.J.[Gui-Jin], Pei, X.K.[Xiao-Kang], He, B.[Bei], Lin, X.G.[Xing-Gang],
An Interleaving Updating Framework of Disparity and Confidence Map for Stereo Matching,
IEICE(E95-D), No. 5, May 2012, pp. 1552-1555.
WWW Link. 1202

See also High-Accuracy Stereo Matching Based on Adaptive Ground Control Points. BibRef

Kumar, S.[Suryansh], Dai, Y.C.[Yu-Chao], Li, H.D.[Hong-Dong],
Superpixel Soup: Monocular Dense 3D Reconstruction of a Complex Dynamic Scene,
PAMI(43), No. 5, May 2021, pp. 1705-1717.
IEEE DOI 2104
BibRef
Earlier:
Monocular Dense 3D Reconstruction of a Complex Dynamic Scene from Two Perspective Frames,
ICCV17(4659-4667)
IEEE DOI 1802
Dynamics, Image reconstruction, Heuristic algorithms, Cameras, Motion segmentation, structure from motion. approximation theory, image motion analysis, image segmentation, image sequences, Vehicle dynamics BibRef

Wen, S.[Shuhuan], Liu, X.[Xin], Zhang, H.[Hong], Sun, F.C.[Fu-Chun], Sheng, M.[Miao], Fan, S.K.[Shao-Kang],
Dense point cloud map construction based on stereo VINS for mobile vehicles,
PandRS(178), 2021, pp. 328-344.
Elsevier DOI 2108
Visual-Inertial System, Depth filter, Dense map, SLAM BibRef

Jeon, H.G.[Hae-Gon], Im, S.H.[Sung-Hoon], Choe, J.[Jaesung], Kang, M.J.[Min-Jun], Lee, J.Y.[Joon-Young], Hebert, M.[Martial],
CMSNet: Deep Color and Monochrome Stereo,
IJCV(130), No. 3, March 2022, pp. 652-668.
Springer DOI 2203
Stereo matching with color and monochrome cameras. BibRef

Chen, C.H.[Chang-Hao], Song, B.F.[Bi-Feng], Bu, S.H.[Shu-Hui], He, L.[Lei],
An improved point feature-based sparse stereo vision,
IET-IPR(16), No. 12, 2022, pp. 3284-3299.
DOI Link 2209
BibRef

Yao, G.B.[Guo-Biao], Zhang, J.[Jin], Zhu, F.Q.[Feng-Qi], Gong, J.Y.[Jian-Ya], Jin, F.X.[Feng-Xiang], Fu, Q.Q.[Qing-Qing], Ren, X.F.[Xiao-Fang],
Quasi-Dense Matching for Oblique Stereo Images through Semantic Segmentation and Local Feature Enhancement,
RS(16), No. 4, 2024, pp. 632.
DOI Link 2402
BibRef

Mathew, A.[Alwyn], Patra, A.P.[Aditya Prakash], Mathew, J.[Jimson],
Self-Attention Dense Depth Estimation Network for Unrectified Video Sequences,
ICIP20(2810-2814)
IEEE DOI 2011
Cameras, Estimation, Distortion, Training, Optical distortion, Pipelines, unrectified image BibRef

Kang, J., Chen, L., Deng, F., Heipke, C.,
Improving Disparity Estimation Based on Residual Cost Volume And Reconstruction Error Volume,
ISPRS20(B2:135-142).
DOI Link 2012
BibRef

Chou, Y., Lee, D.J., Zhang, D., Hill, K.,
A parallel convolutional neural network architecture for stereo vision estimation,
ICIP17(2508-2512)
IEEE DOI 1803
Estimation, Feature extraction, Measurement, Parallel architectures, Stereo vision, Strips, CNN, Deep Learning, Stereo Vision BibRef

Liu, Y., Pan, J., Su, Z.,
Deep feature matching for dense correspondence,
ICIP17(795-799)
IEEE DOI 1803
Estimation, Feature extraction, Image matching, Integrated optics, Optical imaging, Robustness, Deep feature, scene matching BibRef

Xu, Z.L.[Zong-Lin], Kamata, S.I.[Sei-Ichiro], Zhang, Q.S.[Qie-Shi],
Two-stage cross-based stereo disparity refinement,
MVA17(420-423)
DOI Link 1708
Algorithm design and analysis, Color, Complexity theory, Image edge detection, Information filtering, Matched filters, Organizations BibRef

Chen, X.G.[Xiao-Guang], Li, D.[Dan], Zou, J.C.[Jian-Cheng],
Depth estimation of stereo matching based on microarray camera,
ICIVC17(108-112)
IEEE DOI 1708
Algorithm design and analysis, Calibration, Cameras, Estimation, Image edge detection, Lenses, Minimization, camera calibration, depth estimation, markov random field, microarray cameras, stereo, matching BibRef

Berjón, D.[Daniel], Pagés, R.[Rafael], Morán, F.[Francisco],
Fast feature matching for detailed point cloud generation,
IPTA16(1-6)
IEEE DOI 1703
computer graphics BibRef

Mizukami, Y.[Yoshiki], Okada, K.[Koichi], Nomura, A.[Atsushi], Nakanishi, S.[Shinya], Tadamura, K.[Katsumi],
Sub-pixel disparity search for binocular stereo vision,
ICPR12(364-367).
WWW Link. 1302
BibRef

Zhang, H.F.[Hao-Feng], Zhao, C.X.[Chun-Xia], Tang, Z.M.[Zhen-Min], Yang, J.Y.[Jing-Yu],
Particle swarm based stereo algorithm and disparity map evaluation,
ICARCV08(1511-1514).
IEEE DOI 1109
BibRef

Einecke, N.[Nils], Eggert, J.[Julian],
Evaluation of Direct Plane Fitting for Depth and Parameter Estimation,
DICTA10(492-497).
IEEE DOI 1012
BibRef
And:
A Two-Stage Correlation Method for Stereoscopic Depth Estimation,
DICTA10(227-234).
IEEE DOI 1012
BibRef

Miyazawa, K.[Kazuyuki], Aoki, T.[Takafumi],
A robot-based 3D body scanning system using passive stereo vision,
ICIP08(305-308).
IEEE DOI 0810
BibRef

Shibahara, T.[Takuma], Aoki, T.[Takafumi], Nakajima, H.[Hiroshi], Kobayashi, K.[Koji],
A Sub-Pixel Stereo Correspondence Technique Based on 1D Phase-only Correlation,
ICIP07(V: 221-224).
IEEE DOI 0709
BibRef

Galarza, L., Candocia, F.M.,
Optimal and Dense Small Baseline Stereo Image Correspondence,
ICIP06(1037-1040).
IEEE DOI 0610
BibRef

Zhou, W.[Wei], Kambhamettu, C.[Chandra],
Binocular Stereo Dense Matching in the Presence of Specular Reflections,
CVPR06(II: 2363-2370).
IEEE DOI 0606
BibRef

Schlesinger, D.[Dmitrij], Flach, B.[Boris], Shekhovtsov, A.[Alexander],
A Higher Order MRF-Model for Stereo-Reconstruction,
DAGM04(440-446).
Springer DOI 0505
BibRef

Falkenhagen, L.[Lutz], Wedi, T.[Thomas],
Improving Block-Based Disparity Estimation by Considering the Non-uniform Distribution of the Estimation Error,
SMILE98(xx-yy). BibRef 9800

El Zaart, A., Ziou, D.[Djemel], Dubeau, F.[Francois],
Phase-Based Disparity Estimation: a Spatial Approach,
ICIP97(III: 244-247).
IEEE DOI BibRef 9700

Maimone, M.W.[Mark W.], and Shafer, S.A.[Steven A.],
Modeling Foreshortening in Stereo Vision using Local Spatial Frequency,
IROS95(xx). BibRef 9500
And: CMU-CS-TR-95-104, January 1995. Gabor Filter. Phase-Based.
HTML Version. (Html based) and
PS File. (postscript). Plus Errata:
PS File. BibRef

Maimone, M.W.[Mark W.],
Characterizing Stereo Matching Problems using Local Spatial Frequency,
CMU-CS-TR-96-125, May 1996. BibRef 9605 Ph.D.Thesis.
PS File. BibRef

Hannah, M.J.,
Computer Matching of Areas in Stereo Imagery,
Ph.D.Thesis (CS), 1978. BibRef 7800 Stanford AIMemo 239 BibRef
And: Stanford CS Memo STAN-CS-74-438, July 1978. Precursor of Gennery and follower of Quam. Stereo analysis with arbitrary initial camera locations. Computes the camera model by least squares error analysis from a set of matching points. Extends the area of matching points by growing regions which have the same or close disparities. BibRef

Hannah, M.J.,
Bootstrap Stereo,
AAAI-80(38-40). BibRef 8000
And: DARPA80(201-208). BibRef
And:
Bootstrap Stereo Error Simulations,
DARPA81(131-135). A sequence of images is used to generate motion and depth information. The first pair is used to provide an estimate of the camera changes and to give some 3-D information to be used in the sequence. The new areas of the images are analyzed to find new feature points for the next image in the sequence. BibRef

Hannah, M.J.,
SRI's Baseline Stereo System,
DARPA85(149-155). Find matches at different scales using lower levels to guide the higher resolution matches. BibRef 8500

Hannah, M.J.,
Test Results from SRI's Stereo System,
DARPA88(740-744). Results on the photogrammetry data set for the stereo program. BibRef 8800

Hannah, M.J.,
Detection of Errors in Match Disparities,
DARPA82(283-285). BibRef 8200

Gennery, D.B.,
Modelling the Environment of an Exploring Vehicle by Means of Stereo Vision,
Ph.D.June 1980. BibRef 8006 Stanford BibRef

Gennery, D.B.,
Object Detection and Measurement Using Stereo Vision,
DARPA80(161-167). BibRef 8000
And: IJCAI79(320-327). BibRef
Earlier:
A Stereo Vision System for an Autonomous Vehicle,
IJCAI77(576-582). BibRef
And:
A Stereo Vision System,
DARPAO77(31-46).
See also Visual Tracking of Known Three-Dimensional Objects. BibRef

Arnold, R.D., and Binford, T.O.,
Geometric Constraints in Stereo Vision,
SPIE(238), San Diego, CA, July 1980, pp. 281-292. BibRef 8007

Arnold, R.D.,
Automated Stereo Perception,
Ph.D.Thesis (cs), 1983. BibRef 8300 Stanford AIMemo 351 BibRef
And: Stanford CS Memo STAN-CS-83-961. BibRef
Earlier:
Local Context in Matching Edges for Stereo Vision,
DARPA78(65-72). BibRef
Earlier:
Spatial Understanding,
DARPA77(1-4). Combines the work of Moravec and Gennery to do a match using corner type features. BibRef

Szeliski, R.S.[Richard S.], and Hinton, G.E.[Geoffrey E.],
Solving Random-Dot Stereograms Using the Heat Equation,
CVPR85(284-288). Simplification of the new work of Prazdny, by implementing as Heat equation. BibRef 8500

Essafi, H., Mazzoni, C., Julien, P.,
Satellite Digital Elevation Model on the Heterogeneous OPENVISION Parallel Computer,
CAMP95(xx). BibRef 9500

Shah, J.,
A Nonlinear Diffusion Model for Discontinuous Disparity and Half-Occlusions in Stereo,
CVPR93(34-40).
IEEE DOI An approach to stereo correspondence. BibRef 9300

Xie, M.[Ming], Thonnat, M.[Monique],
A theory of 3D reconstruction of heterogeneous edge primitives from two perspective views,
ECCV92(715-719).
Springer DOI 9205
BibRef

Bandopadhy, A.,
Interest Points, Disparities and Correspondence,
DARPA84(184-187). (U. of Rochester) BibRef 8400

Shizawa, M.,
Direct Estimation of Multiple Disparities for Transparent Multiple Surfaces in Binocular Stereo,
ICCV93(447-454).
IEEE DOI Developed from work in motion section on transparent surfaces.
See also On visual ambiguities due to transparency in motion and stereo. BibRef 9300

Gennert, M.A.,
Brightness-Based Stereo Matching,
ICCV88(139-143).
IEEE DOI BibRef 8800

Sato, T.,
Automotive Stereo Vision Using Deconvolution Technique,
IJCAI79(763-765). BibRef 7900

Chapter on Stereo: Three Dimensional Descriptions from Two or More Views, Binocular, Trinocular continues in
Edge Based Stereo Analysis: Scan Line Oriented .