10.3 Error Analysis, Evaluation, Performance Analysis of Computation Methods

Binford, T.O.,
Stereo Vision: Complexity and Constraints,
RR-IS84(475-487). BibRef 8400

McKee, S., Levi, D., Brown, S.,
The Imprecision of Stereopsis,
Vision Research(30), No. 11, 1990, pp. 1763-1779. BibRef 9000

Das, S.[Subhodev], Ahuja, N.[Narendra],
Performance Analysis of Stereo, Vergence, and Focus as Depth Cues for Active Vision,
PAMI(17), No. 12, December 1995, pp. 1213-1219.
IEEE Abstract.
IEEE DOI BibRef 9512
Earlier: A2, A1:
Performance Analysis of Depth Cues for Active Vision,
ARPA94(II:1041-1047). BibRef
Earlier: A1, A2:
A Comparative Study of Stereo, Vergence, and Focus as Depth Cues for Active Vision,
CVPR93(194-199).
IEEE DOI Evaluation, Depth. Active Vision, Evaluation. Different techniques perform better or worse under different configurations (focus is good for very close range, less so for greater distances.
See also Active Surface Estimation: Integrating Coarse-to-Fine Image Acquisition and Estimation from Multiple Cues. BibRef

Day, T., Muller, J.P.,
Digital Elevation Model Production by Stereo-Matching Spot Image Pairs: A Comparison of Two Algorithms,
IVC(7), No. 2, May 1989, pp. 95-101.
Elsevier DOI BibRef 8905

Thayer, S.[Scott], Trivedi, M.M.[Mohan M.], Thayer, S., Trivedi, M.,
Residual Uncertainty in 3-Dimensional Reconstruction Using 2-Planes Calibration and Stereo Methods,
PR(28), No. 7, July 1995, pp. 1073-1082.
Elsevier DOI BibRef 9507

Belhumeur, P.N.[Peter N.],
A Bayesian-approach to Binocular Stereopsis,
IJCV(19), No. 3, August 1996, pp. 237-260.
Springer DOI Computational Vision. Disparity Gradient. Explicit model of geometry and imaging. Model requires depth, orientation, object boundaries and surface creases. BibRef 9608

Belhumeur, P.N., and Mumford, D.,
A Bayesian Treatment of the Stereo Correspondence Problem Using Half-Occluded Regions,
CVPR92(506-512).
IEEE DOI Analyze the points that are only in one image. BibRef 9200

Belhumeur, P.N.,
A Binocular Stereo Algorithm for Reconstructing Sloping, Creased, and Broken Surfaces in the Presence of Half-Occlusion,
ICCV93(431-438).
IEEE DOI BibRef 9300

Belhumeur, P.N.,
Global Priors For Binocular Stereopsis,
ICIP94(II: 730-734).
IEEE DOI 9411
BibRef

Bolles, R.C., Baker, H.H., Hannah, M.J.,
The JISCT Stereo Evaluation,
DARPA93(263-274). Tests of JPL, INRIA, SRI, CMU, and Teleos stereo systems. The initial phase of testing the algorithms. BibRef 9300

Wolf, L.B.[Lior B.], Boult, T.E.,
Using Line Correspondence Stereo to Measure Surface Orientation,
IJCAI89(1655-1660). BibRef 8900
And: A1 only:
Accurate Measurement of Orientation from Stereo Using Line Correspondence,
CVPR89(410-415).
IEEE DOI Errors are reduced when 3-D orientations of lines are computed using intersections of planes for each camera. BibRef

McVey, E.S., and Lee, J.W.,
Some Accuracy and Resolution Aspects of Computer Vision Distance Measurements,
PAMI(4), No. 6, November 1982, pp. 646-649. Stereo, Evaluation. Error analysis for stereo systems. How does subpixel accuracy matching affect the results? BibRef 8211

Blostein, S.D., and Huang, T.S.,
Error Analysis in Stereo Determination of 3-D Point Positions,
PAMI(9), No. 6, November 1987, pp. 752-766. BibRef 8711
And: Correction: PAMI(10), No. 5, September 1988, p. 765. BibRef
And:
Quantization Errors in Stereo Triangulation,
ICCV87(325-334). Stereo, Evaluation. This gives errors assuming that the matching points have been found (i.e. the limits). BibRef

Mohan, R., Medioni, G.G., and Nevatia, R.,
Stereo Error Detection, Correction, and Evaluation,
PAMI(11), No. 2, February 1989, pp. 113-120.
IEEE Abstract.
IEEE DOI BibRef 8902 USC Computer Vision BibRef
Earlier: ICCV87(315-324). Stereo, Evaluation. A post processor for any edgel based stereo matching system, identifies the disparity errors based on figural continuity. BibRef

Mohan, R.,
Error Detection and Correction for Stereo,
DARPA85(433-442). BibRef 8500

Alvertos, N., Brzakovic, D., and Gonzalez, R.C.,
Camera Geometries for Image Matching in 3-D Machine Vision,
PAMI(11), No. 9, September 1989, pp. 897-915.
IEEE Abstract.
IEEE DOI The optimal direction, for matching, is axial motion. BibRef 8909

Kiang, S.M., Chou, R.J., and Aggarwal, J.K.,
Triangulation Errors in Stereo Algorithms,
CVWS87(72-78). Same material as above BibRef 8700 PAMI(9), paper? BibRef

Rodriguez, J.J., and Aggarwal, J.K.,
Stochastic Analysis of Stereo Quantization Error,
PAMI(12), No. 5, May 1990, pp. 467-470.
IEEE Abstract.
IEEE DOI BibRef 9005
Earlier:
Quantization Error in Stereo Imaging,
CVPR88(153-158).
IEEE DOI Stereo, Evaluation. Relative range error is better to specify range resolution. BibRef

Lew, M.S., Huang, T.S., Wong, K.W.,
Learning and Feature Selection in Stereo Matching,
PAMI(16), No. 9, September 1994, pp. 869-881.
IEEE Abstract.
IEEE DOI BibRef 9409
Earlier: DARPA93(993-1004). Refine the feature set by eliminating incorrect matches. BibRef

Lew, M.S., Wong, K.W., Huang, T.S.,
Multi-Scale Stereo Matching,
ICPR92(I:620-623).
IEEE DOI BibRef 9200

Lew, M.S.[Michael S.], Huang, T.S.[Thomas S.],
Optimal Multi-Scale Matching,
CVPR99(I: 88-93).
IEEE Abstract.
IEEE DOI BibRef 9900
Earlier:
Image compression and matching,
ICIP94(II: 720-724).
IEEE DOI 9411
BibRef

Sebe, N.[Nicu], Lew, M.S.[Michael S.],
Maximum Likelihood Stereo Matching,
ICPR00(Vol I: 900-903).
IEEE DOI 0009
BibRef

Lew, M.S.[Michael S.], Sebe, N.[Nicu], Huang, T.S.[Thomas S.],
Improving Visual Matching,
CVPR00(II: 58-65).
IEEE Abstract.
IEEE DOI 0005
BibRef

Sahabi, H., Basu, A.,
Analysis of Error in Depth Perception with Vergence and Spatially Varying Sensing,
CVIU(63), No. 3, May 1996, pp. 447-461.
DOI Link 9606
BibRef

Basu, A.[Anup], Sahabi, H.[Hossein],
Analysis of Depth Estimation Error for Cylindrical Stereo Imaging,
PR(35), No. 11, November 2002, pp. 2549-2558.
Elsevier DOI 0208
BibRef
Earlier:
Analysis of Cylindrical Stereo Imaging,
ICPR00(Vol I: 366-369).
IEEE DOI 0009
Depth using 2 rotating linear CCD cameras. Analyze errors. BibRef

Stewart, C.V.[Charles V.], Flatland, R.Y.[Robin Y.], and Bubna, K.[Kishore],
Geometric Constraints and Stereo Disparity Computation,
IJCV(20), No. 3, 1996, pp. 143-168. BibRef 9600

Stewart, C.V.,
On the Derivation of Geometric Constraints in Stereo,
CVPR92(769-772).
IEEE DOI BibRef 9200
And:
An Analysis of the Probability of Disparity Changes in Stereo Matching and a New Algorithm Based on the Analysis,
CVPR91(670-671).
IEEE DOI BibRef

Stewart, C.V., and MacCrone, J.K.,
Experimental Analysis of a Number of Stereo Matching Components Using LMA,
ICPR90(I: 254-258).
IEEE DOI BibRef 9000

Giles, P.T., Franklin, S.E.,
Comparison of Derivative Topographic Surfaces of a DEM Generated from Stereoscopic Spot Images with Field-Measurements,
PhEngRS(62), No. 10, October 1996, pp. 1165-1171. 9611
BibRef

Smith, S.[Stephen],
Note on Small Angle Approximations for Stereo Disparity,
IVC(11), No. 6, July-August 1993, pp. 395-398.
Elsevier DOI Tilt, gaze, verge cameras. BibRef 9307

Zhao, W.Y.[Wen-Yi], Nandhakumar, N.,
Effects of Camera Alignment Errors on Stereoscopic Depth Estimates,
PR(29), No. 12, December 1996, pp. 2115-2126.
Elsevier DOI 9701
Camera Calibration. BibRef

Jones, G.A.,
Constraint, Optimization, and Hierarchy: Reviewing Stereoscopic Correspondence of Complex Features,
CVIU(65), No. 1, January 1997, pp. 57-78.
DOI Link 9702
BibRef

Wu, M.S., and Leou, J.J.,
A Bipartite Matching Approach to Feature Correspondence in Stereo Vision,
PRL(16), 1995, pp. 23-31. BibRef 9500

de la Cruz, J.M., Pajares, G., Aranda, J., and Vindel, J.L.F.,
Stereo Matching Technique Based on the Perceptron Criterion Function,
PRL(16), 1995, pp. 933-944. BibRef 9500

Basu, A.,
Optimal Discretization for Stereo Reconstruction,
PRL(13), 1992, pp. 813-820. BibRef 9200

Basu, A., Sahabi, H.,
Optimal Non-Uniform Discretization for Stereo Reconstruction,
ICPR96(I: 755-759).
IEEE DOI 9608
(Univ. of Alberta, CDN) BibRef

Matthies, L.H., Grandjean, P.,
Stochastic Performance Modeling and Evaluation of Obstacle Detectability with Imaging Range Sensors,
RA(10), No. 6, December 1994, pp. 783-792.
PS File. BibRef 9412
Earlier: CVPR93(657-658).
IEEE DOI Continuing evaluation of obstacle detection. BibRef

Matthies, L.H.,
Toward Stochastic Modeling of Obstacle Detectability in Passive Stereo Range Imagery,
CVPR92(765-768).
IEEE DOI How much you can really see- holes are hard, rocks are easy. BibRef 9200

Cozzi, A., Crespi, B., Valentinotti, F., Worgotter, F.,
Performance of Phase-Based Algorithms for Disparity Estimation,
MVA(9), No. 5-6, 1997, pp. 334-340.
Springer DOI 9705
BibRef

Sengupta, S.,
Effects of Unequal Focal Lengths in Stereo Imaging,
PRL(18), No. 4, April 1997, pp. 395-400. 9708
BibRef

McIvor, A.M., Valkenburg, R.J.,
A Comparison of Local Surface Geometry Estimation Methods,
MVA(10), No. 1, 1997, pp. 17-26.
Springer DOI 9705
BibRef
And: Industrial Research LimitedMarch 1996. BibRef

Benslima, M., Konrad, J., Barwicz, A.,
Improvement of Stereo Disparity Estimation Through Balanced Filtering: The Sliding-Block Approach,
CirSysVideo(7), No. 6, December 1997, pp. 913-920.
IEEE Top Reference. 9712
BibRef

McNerney, P.J., Konrad, J., Betke, M.,
Block-Based MAP Disparity Estimation Under Alpha-Channel Constraints,
CirSysVideo(17), No. 6, June 2007, pp. 785-789.
IEEE DOI 0706
BibRef

Cheong, L.F.[Loong-Fah], Fermüller, C.[Cornelia], Aloimonos, Y.[Yiannis],
Effects of Errors in the Viewing Geometry on Shape Estimation,
CVIU(71), No. 3, September 1998, pp. 356-372.
DOI Link BibRef 9809
Earlier:
Interaction Between 3D Shape and Motion: Theory and Applications,
UMDTR-3480, June 1996.
WWW Link. BibRef

Cheong, L.F.[Loong Fah], Aloimonos, Y.,
Iso-Distortion Contours and Egomotion Estimation,
SCV95(55-60).
IEEE DOI University of Maryland. Error analysis of egomotion computations. BibRef 9500

Francisco, A., Bergholm, F.,
On The Importance Of Being Asymmetric In Stereopsis: Or Why We Should Use Skewed Parallel Cameras,
IJCV(29), No. 3, September 1998, pp. 181-202.
DOI Link 9811
BibRef

Elnagar, A.[Ashraf],
Optimal error discretization under depth and range constraints,
PRL(19), No. 9, 31 July 1998, pp. 879-888. BibRef 9807

Chan, M.W.[Moses W.], Pizlo, Z.[Zygmunt], Chelberg, D.M.[David M.],
Binocular Shape Reconstruction: Psychological Plausibility of the 8-Point Algorithm,
CVIU(74), No. 2, May 1999, pp. 121-137.
DOI Link BibRef 9905

Nagel, H.H., Heimes, F., Fleischer, K., Haag, M., Leuck, H., Noltemeier, S.,
Quantitative comparison between trajectory estimates obtained from a binocular camera setup within a moving road vehicle and from the outside by a stationary monocular camera,
IVC(18), No. 5, April 2000, pp. 435-444.
Elsevier DOI 0003
BibRef

Ijsselsteijn, W.A., de Ridder, H., Vliegen, J.,
Subjective Evaluation of Stereoscopic Images: Effects of Camera Parameters and Display Duration,
CirSysVideo(10), No. 2, March 2000, pp. 225.
IEEE Top Reference. 0003
BibRef

Sanders-Reed, J.N.[John N.],
Error Propagation in two-sensor 3D position estimation,
OptEng(40), No. 4, April, 2001, pp.
PDF File. Tracking. Errors in 3D triangulation from angle-only sensors (direction). Optimum geometry is a 90deg separation between sensors. Applied to multiple camera tracking. BibRef 0104

Sanders-Reed, J.N.[John N.],
Impact of tracking system knowledge on multi-sensor 3D triangulation,
SPIE(4714), April, 2002, pp. xx-yy.
PDF File. BibRef 0204

Sanders-Reed, J.N.[John N.],
Triangulation Position Error Analysis for Closely Spaced Imagers,
SAESafety Test Methodology, SP-1664, March, 2002, pp. xx-yy.
PDF File. BibRef 0203

Davis, C.H., Jiang, H.[Hai], Wang, X.Y.[Xiang-Yun],
Modeling and estimation of the spatial variation of elevation error in high resolution dems from stereo-image processing,
GeoRS(39), No. 11, November 2001, pp. 2483-2489.
IEEE Top Reference. 0111
BibRef

Scharstein, D.[Daniel], Szeliski, R.S.[Richard S.],
A Taxonomy and Evaluation of Dense Two-Frame Stereo Correspondence Algorithms,
IJCV(47), No. 1-3, April-June 2002, pp. 7-42.
DOI Link 0203
Code, Stereo. Dataset, Stereo. The data sets and code are also available:
WWW Link. Award, Everingham. for 2015 BibRef

Scharstein, D.[Daniel], Szeliski, R.S.[Richard S.],
Middlebury stereo vision page,
Online2007
WWW Link. Survey, Stereo. BibRef 0700

Scharstein, D.[Daniel], Szeliski, R.S.[Richard S.], Zabih, R.[Ramin],
A Taxonomy and Evaluation of Dense Two-Frame Stereo Methods,
SMBV01(xx-yy). 0110
BibRef

Szeliski, R.S.[Richard S.], Scharstein, D.[Daniel],
Sampling the Disparity Space Image,
PAMI(26), No. 3, March 2004, pp. 419-425.
IEEE Abstract. 0402
Propose several match criteria, evaluate. BibRef

Seitz, S.M.[Steven M.], Curless, B.[Brian], Diebel, J.[James], Scharstein, D.[Daniel], Szeliski, R.S.[Richard S.],
A Comparison and Evaluation of Multi-View Stereo Reconstruction Algorithms,
CVPR06(I: 519-528).
IEEE DOI 0606

See also TV Prior for High-Quality Local Multi-View Stereo Reconstruction, A. BibRef

Goesele, M.[Michael], Ackermann, J.[Jens], Fuhrmann, S.[Simon], Klowsky, R.[Ronny], Langguth, F.[Fabian], Mücke, P.[Patrick], Ritz, M.[Martin],
Scene Reconstruction from Community Photo Collections,
Computer(43), No. 6, June 2010, pp. 48-53.
IEEE DOI 1007

See also Photosynth. BibRef

Klowsky, R.[Ronny], Kuijper, A.[Arjan], Goesele, M.[Michael],
Weighted Patch-Based Reconstruction: Linking (Multi-view) Stereo to Scale Space,
SSVM13(234-245).
Springer DOI 1305
BibRef

Snavely, N., Simon, I., Goesele, M., Szeliski, R., Seitz, S.M.,
Scene Reconstruction and Visualization From Community Photo Collections,
PIEEE(98), No. 8, August 2010, pp. 1370-1390.
IEEE DOI 1008

See also Photosynth. More on the PhotoSynth project in the special section. BibRef

Szeliski, R.S.[Richard S.],
Weaving the World's Photos into a 3D Web,
3DPVT10(xx-yy).
WWW Link. 1005
BibRef

Goesele, M.[Michael], Snavely, N.[Noah], Curless, B.[Brian], Hoppe, H.[Hugues], Seitz, S.M.[Steven M.],
Multi-View Stereo for Community Photo Collections,
ICCV07(1-8).
IEEE DOI
WWW Link. 0710

See also Photosynth. BibRef

Goesele, M.[Michael], Curless, B.[Brian], Seitz, S.M.[Steven M.],
Multi-View Stereo Revisited,
CVPR06(II: 2402-2409).
IEEE DOI 0606
BibRef

Sun, M.[Min], Farhadi, A.[Ali], Taskar, B.[Ben], Seitz, S.M.[Steve M.],
Summarizing Unconstrained Videos Using Salient Montages,
PAMI(39), No. 11, November 2017, pp. 2256-2269.
IEEE DOI 1710
BibRef
Earlier:
Salient Montages from Unconstrained Videos,
ECCV14(VII: 472-488).
Springer DOI 1408
camera motion, frame melange, unconstrained video summarization. BibRef

Shan, Q.[Qi], Curless, B.[Brian], Furukawa, Y.[Yasutaka], Hernandez, C.[Carlos], Seitz, S.M.[Steven M.],
Photo Uncrop,
ECCV14(VI: 16-31).
Springer DOI 1408
extending the field of view of a photo using internet photography. BibRef

Egnal, G.[Geoffrey], Wildes, R.P.[Richard P.],
Detecting Binocular Half-Occlusions: Empirical Comparisons of Five Approaches,
PAMI(24), No. 8, August 2002, pp. 1127-1133.
IEEE Abstract. 0208
BibRef
Earlier:
Detecting Binocular Half-Occlusions: Empirical Comparisons of Four Approaches,
CVPR00(II: 466-473).
IEEE Abstract.
IEEE DOI 0005
Bimodality (
See also Direct Recovery of Three-Dimensional Scene Geometry from Binocular Stereo Disparity.
See also Early Detection of Motion Boundaries, The. or
See also Direct Evidence for Occlusion in Stereo and Motion. ) Match Goodness Jumps (
See also Toward a General Theory of Stereopsis: Binocular Matching, Occluding Contours and Fusion. or
See also Stereo Processing of Aerial, Urban Images. ) Left-Right Checking (
See also Matching Two Perspective Views.
See also On an Analysis of Static Occlusion in Stereo Vision.
See also Intensity-Based Cooperative Bidirectional Stereo Matching with Simultaneous Detection of Discontinuities and Occlusions, An. ) Ordering (
See also Depth from Edge and Intensity Based Stereo.
See also Generalized Ordering Constraint for Stereo Correspondence, A.
See also Stereo by Intra- and Inter-scanline Search Using Dynamic Programming.
See also Occlusions and Binocular Stereo. or
See also Bayesian-approach to Binocular Stereopsis, A. ) Occlusion Constraint (
See also Occlusions and Binocular Stereo. or
See also Large Occlusion Stereo. ). Overall, no single system works best. Need a combination of them. BibRef

Mulligan, J.[Jane], Zabulis, X.[Xenophon], Kelshikar, N., Daniilidis, K.[Kostas],
Stereo-based environment scanning for immersive telepresence,
CirSysVideo(14), No. 3, March 2004, pp. 304-320.
IEEE Abstract. 0407
BibRef

Mulligan, J.[Jane], Isler, V.[Volkan], Daniilidis, K.[Kostas],
Performance Evaluation of Stereo for Tele-presence,
ICCV01(II: 558-565).
IEEE DOI Or:
PDF File. 0106
Evaluation in context. BibRef

Egnal, G.[Geoffrey], Mintz, M.[Max], Wildes, R.P.[Richard P.],
A Stereo Confidence Metric Using Single View Imagery with Comparison to Five Alternative Approaches,
IVC(22), No. 12, 1 October 2004, pp. 943-957.
Elsevier DOI 0409
BibRef
Earlier: A1, A2 only:
A Stereo Confidence Metric Using Single View Imagery,
VI02(162).
PDF File. 0208
Stereo evaluation. Try the match on two, supposedly identical images (from the same view). This indicates where errors are likely. BibRef

van der Mark, W., Gavrila, D.M.,
Real-time dense stereo for intelligent vehicles,
ITS(7), No. 1, March 2006, pp. 38-50.
IEEE DOI
PDF File. 0604
BibRef

Sunyoto, H., van der Mark, W., Gavrila, D.M.,
A comparative study of fast dense stereo vision algorithms,
IVS04(319-324).
IEEE DOI 0411
BibRef

Mayoral, R.[Rafael], Lera, G.[Gabriel], Pérez-Ilzarbe, M.J.[María José],
Evaluation of correspondence errors for stereo,
IVC(24), No. 12, 1 December 2006, pp. 1288-1300.
Elsevier DOI 0610
Stereo; Correspondence problem; Evaluation BibRef

Mayoral, R., Aurnhammer, M.,
Evaluation of correspondence errors for stereo,
ICPR04(IV: 104-107).
IEEE DOI 0409
BibRef

Teshima, Y., Iwasaki, A.,
Correction of Attitude Fluctuation of Terra Spacecraft Using ASTER/SWIR Imagery With Parallax Observation,
GeoRS(46), No. 1, January 2008, pp. 222-227.
IEEE DOI 0712
BibRef

Wan, D., Zhou, J.,
Multiresolution and Wide-Scope Depth Estimation Using a Dual-PTZ-Camera System,
IP(18), No. 3, March 2009, pp. 677-682.
IEEE DOI 0903
Analysis of different PTZ settings on extraction. BibRef

Khoshelham, K.[Kourosh],
Role of Tie Points in Integrated Sensor Orientation for Photogrammetric Map Compilation,
PhEngRS(75), No. 3, March 2009, pp. 305-312.
WWW Link. 0903
An investigation of the influence of the number and distribution of tie points on the integrated orientation of aerial frame cameras for photogrammetric map compilation. BibRef

Fraser, C.S.[Clive S.], Cronk, S.[Simon],
A hybrid measurement approach for close-range photogrammetry,
PandRS(64), No. 3, May 2009, pp. 328-333.
Elsevier DOI 0905
BibRef
Earlier: A2, A1:
Hybrid Measurement Scenarios in Automated Close-Range Photogrammetry,
ISPRS08(B3b: 745 ff).
PDF File. 0807
Automation; Camera calibration; Colour image scanning; Point correspondence determination; Red retroreflective targeting BibRef

Li, R.X.[Rong-Xing], Niu, X.[Xutong], Liu, C.[Chun], Wu, B.[Bo], Deshpande, S.[Sagar],
Impact of Imaging Geometry on 3D Geopositioning Accuracy of Stereo Ikonos Imagery,
PhEngRS(75), No. 9, September 2009, pp. 1119-1126.
WWW Link. 0910
Evaluation, Ikonos. A special experiment and investigation proves the high impact of stereo imaging geometry on geopositioning accuracy. BibRef

Morgan, G.L.K., Liu, J.G., Yan, H.,
Precise Subpixel Disparity Measurement From Very Narrow Baseline Stereo,
GeoRS(48), No. 9, September 2010, pp. 3424-3433.
IEEE DOI 1008
BibRef

Belhaoua, A.[Abdelkrim], Kohler, S.[Sophie], Hirsch, E.[Ernest],
Error Evaluation in a Stereovision-Based 3D Reconstruction System,
JIVP(2010), No. 2010, pp. xx-yy.
DOI Link 1003
BibRef

MacKinnon, D.K.[David K.],
Assessing the performance of 3D-imaging systems,
SPIE(Newsroom), March 9, 2011
DOI Link 1103
Evaluation, 3-D Sensors. Statistically traceable testing procedures employ terminology selected to be familiar to those who regularly work with geometrical dimensioning and tolerancing. Not just stereo. BibRef

Zhu, K.[Ke], Neilson, D.[Daniel], d'Angelo, P.[Pablo],
Confidence-Based Surface Prior for Energy-Minimization Stereo Matching,
GCPR13(91-100).
Springer DOI 1311
BibRef

d'Angelo, P., Reinartz, P.,
Semiglobal Matching Results on the ISPRS Stereo Matching Benchmark,
HighRes11(xx-yy).
PDF File. 1106
BibRef

Ahmadabadian, A.H.[Ali Hosseininaveh], Robson, S.[Stuart], Boehm, J.[Jan], Shortis, M.[Mark], Wenzel, K.[Konrad], Fritsch, D.[Dieter],
A comparison of dense matching algorithms for scaled surface reconstruction using stereo camera rigs,
PandRS(78), No. 1, April 2013, pp. 157-167.
Elsevier DOI 1304
Close range photogrammetry; Structure from Motion; 3D reconstruction; Multi-View Stereo; Stereo camera BibRef

Lakemond, R.[Ruan], Fookes, C.[Clinton], Sridharan, S.[Sridha],
Evaluation of two-view geometry methods with automatic ground-truth generation,
IVC(31), No. 12, 2013, pp. 921-934.
Elsevier DOI 1312
Evaluation BibRef

Papachristou, C.[Christos], Delopoulos, A.N.[Anastasios N.],
A method for the evaluation of projective geometric consistency in weakly calibrated stereo with application to point matching,
CVIU(119), No. 1, 2014, pp. 81-101.
Elsevier DOI 1402
Correspondence problem BibRef

Skarlatos, D.[Dimitrios], Kiparissi, S.[Stavroula], Theodoridou, S.[Sofia], Pandermalis, D.[Dimitrios],
A Method of Evaluating the Internal Precision of Multi-View Stereo Dense Reconstruction, Applied on Parthenon Frieze,
PFG(2014), No. 3, 2014, pp. 189-195.
DOI Link 1407
BibRef

Raghavendra, U., Makkithaya, K.[Krishnamoorthi], Karunakar, A.K.,
Structural similarity-based ranking of stereo algorithms for dynamic adaptation in real-time robot navigation,
IJCVR(4), No. 4, 2014, pp. 281-293.
DOI Link 1411
BibRef

Shin, B.S.[Bok-Suk], Caudillo, D.[Diego], Klette, R.[Reinhard],
Evaluation of two stereo matchers on long real-world video sequences,
PR(48), No. 4, 2015, pp. 1113-1124.
Elsevier DOI 1502
Stereo vision BibRef

Brandao, M., Ferreira, R., Hashimoto, K., Takanishi, A., Santos-Victor, J.,
On Stereo Confidence Measures for Global Methods: Evaluation, New Model and Integration into Occupancy Grids,
PAMI(38), No. 1, January 2016, pp. 116-128.
IEEE DOI 1601
Benchmark testing BibRef

Mertens, B.[Benjamin], Delchambre, A.[Alain],
3D Reconstruction: Why should the accuracy always be presented in the pixel unit?,
IVC(48-49), No. 1, 2016, pp. 57-60.
Elsevier DOI 1604
Error BibRef

Aanæs, H.[Henrik], Jensen, R.R.[Rasmus Ramsbøl], Vogiatzis, G.[George], Tola, E.[Engin], Dahl, A.B.[Anders Bjorholm],
Large-Scale Data for Multiple-View Stereopsis,
IJCV(120), No. 2, November 2016, pp. 153-168.
Springer DOI 1609
BibRef
Earlier: A2, A5, A3, A4, A1:
Large Scale Multi-view Stereopsis Evaluation,
CVPR14(406-413)
IEEE DOI 1409
Multi view stereopsis; structured light; surface reconstruction BibRef

Kim, S.[Sujung], Kim, S.D.[Seong Dae], Dahl, A.L.[Anders Lindbjerg], Conradsen, K.[Knut], Jensen, R.R.[Rasmus Ramsbol], Aanæs, H.[Henrik],
Multiple View Stereo by Reflectance Modeling,
3DIMPVT12(579-586).
IEEE DOI 1212
BibRef

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IEEE DOI 2009
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Unsupervised Adaptation for Deep Stereo,
ICCV17(1614-1622)
IEEE DOI 1802
Training, Reliability, Estimation, Loss measurement, Prediction algorithms, Deep learning, domain adaptation. learning (artificial intelligence), neural nets, stereo image processing, data corpus, deep learning stereo model, Reliability BibRef

Poggi, M.[Matteo], Tonioni, A.[Alessio], Tosi, F.[Fabio], Mattoccia, S.[Stefano], di Stefano, L.[Luigi],
Continual Adaptation for Deep Stereo,
PAMI(44), No. 9, September 2022, pp. 4713-4729.
IEEE DOI 2208
Training, Real-time systems, Estimation, Adaptation models, Proposals, Stereo matching, deep learning, continual learning BibRef

Cai, C., Poggi, M.[Matteo], Mattoccia, S.[Stefano], Mordohai, P.,
Matching-space Stereo Networks for Cross-domain Generalization,
3DV20(364-373)
IEEE DOI 2102
Feature extraction, Training, Estimation, Pipelines, Data models, Correlation BibRef

Tonioni, A.[Alessio], Rahnama, O.[Oscar], Joy, T.[Thomas], di Stefano, L.[Luigi], Ajanthan, T.[Thalaiyasingam], Torr, P.H.S.[Philip H.S.],
Learning to Adapt for Stereo,
CVPR19(9653-9662).
IEEE DOI 2002
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Poggi, M.[Matteo], Tosi, F.[Fabio], Mattoccia, S.[Stefano],
Quantitative Evaluation of Confidence Measures in a Machine Learning World,
ICCV17(5238-5247)
IEEE DOI 1802
learning (artificial intelligence), stereo image processing, deep learning, depth measurements, Reliability BibRef

Poggi, M.[Matteo], Tosi, F.[Fabio], Mattoccia, S.[Stefano],
Efficient Confidence Measures for Embedded Stereo,
CIAP17(I:483-494).
Springer DOI 1711
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Even More Confident Predictions with Deep Machine-Learning,
ECVW17(393-401)
IEEE DOI 1709
Stereo. Atmospheric measurements, Feature extraction, Particle measurements, Proposals, Reliability, BibRef

Fan, C.L.[Chun-Ling], Zhang, Y.[Yun], Hamzaoui, R.[Raouf], Ziou, D.[Djemel], Jiang, Q.S.[Qing-Shan],
Learning-Based Satisfied User Ratio Prediction for Symmetrically and Asymmetrically Compressed Stereoscopic Images,
MultMedMag(28), No. 3, July 2021, pp. 8-20.
IEEE DOI 2109
Image coding, Stereo image processing, Feature extraction, Distortion, Visualization, Image quality, asymmetric stereoscopic compression BibRef

Zizien, A.[Adam], Fliegel, K.[Karel],
Regarding the quality of disparity estimation from distorted light fields,
SP:IC(109), 2022, pp. 116867.
Elsevier DOI 2210
Light field, Image compression, Image quality evaluation, Subjective assessment, Objective assessment, Depth estimation BibRef

Marí, R.[Roger], Ehret, T.[Thibaud], Facciolo, G.[Gabriele],
Disparity Estimation Networks for Aerial and High-Resolution Satellite Images: A Review,
IPOL(12), 2022, pp. 501-526.
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Code, Stereo.
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Purinton, B.[Benjamin], Mueting, A.[Ariane], Bookhagen, B.[Bodo],
Image Texture as Quality Indicator for Optical DEM Generation: Geomorphic Applications in the Arid Central Andes,
RS(15), No. 1, 2023, pp. xx-yy.
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Tang, X.M.[Xin-Ming], Zhu, X.Y.[Xiao-Yong], Hu, W.[Wenmin], Ding, J.H.[Jian-Hang],
Geometric Accuracy Analysis of Regional Block Adjustment Using GF-7 Stereo Images without GCPs,
RS(15), No. 10, 2023, pp. xx-yy.
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Nasiri, S.M.[Seyed-Mahdi], Hosseini, R.[Reshad], Moradi, H.[Hadi],
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IET-IPR(17), No. 10, 2023, pp. 2855-2865.
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robot vision BibRef

Wan, W.F.[Wen-Fei], Huang, D.J.[Deng-Jia], Shang, B.[Bin], Wei, S.Y.[Sheng-Yu], Wu, H.R.[Hong Ren], Wu, J.J.[Jin-Jian], Shi, G.M.[Guang-Ming],
Depth Perception Assessment of 3D Videos Based on Stereoscopic and Spatial Orientation Structural Features,
CirSysVideo(33), No. 9, September 2023, pp. 4588-4602.
IEEE DOI 2310
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Shao, S.W.[Shu-Wei], Li, R.[Ran], Pei, Z.C.[Zhong-Cai], Liu, Z.[Zhong], Chen, W.H.[Wei-Hai], Zhu, W.T.[Wen-Tao], Wu, X.M.[Xing-Ming], Zhang, B.C.[Bao-Chang],
Towards Comprehensive Monocular Depth Estimation: Multiple Heads are Better Than One,
MultMed(25), 2023, pp. 7660-7671.
IEEE DOI 2312
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Wu, R.C.[Rong-Cheng], Wang, M.Z.[Ming-Zhe], Li, Z.D.[Zhi-Dong], Zhou, J.L.[Jian-Long], Chen, F.[Fang], Wang, X.[Xuan], Sun, C.M.[Chang-Ming],
Few-Shot Stereo Matching with High Domain Adaptability Based on Adaptive Recursive Network,
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Springer DOI 2405
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Wu, R.C.[Rong-Cheng], Sun, C.M.[Chang-Ming], Liu, Z.Y.[Zhao-Ying], Sowmya, A.[Arcot],
Deep learning based stereo cost aggregation on a small dataset,
DICTA21(01-08)
IEEE DOI 2201
Training, Deep learning, Costs, Digital images, Pipelines, Training data BibRef

Voynov, O.[Oleg], Bobrovskikh, G.[Gleb], Karpyshev, P.[Pavel], Galochkin, S.[Saveliy], Ardelean, A.T.[Andrei-Timotei], Bozhcnko, A.[Arseniy], Karmanova, E.[Ekaterina], Kopanev, P.[Pavel], Labutin-Rymsho, Y.[Yaroslav], Rakhimov, R.[Ruslan], Safin, A.[Aleksandr], Serpiva, V.[Valerii], Artemov, A.[Alexey], Burnaev, E.[Evgeny], Tsetserukou, D.[Dzmitry], Zorin, D.[Denis],
Multi-Sensor Large-Scale Dataset for Multi-View 3D Reconstruction,
CVPR23(21392-21403)
IEEE DOI 2309
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Hu, Y.[Yue], Lu, Y.F.[Yi-Fan], Xu, R.S.[Run-Sheng], Xie, W.[Weidi], Chen, S.[Siheng], Wang, Y.F.[Yan-Feng],
Collaboration Helps Camera Overtake LiDAR in 3D Detection,
CVPR23(9243-9252)
IEEE DOI 2309
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Ramirez, P.Z.[Pierluigi Zama], Tosi, F.[Fabio], Poggi, M.[Matteo], Salti, S.[Samuele], Mattoccia, S.[Stefano], di Stefano, L.[Luigi],
Open Challenges in Deep Stereo: the Booster Dataset,
CVPR22(21136-21146)
IEEE DOI 2210
Pipelines, Sensors, Labeling, Datasets and evaluation, 3D from multi-view and sensors BibRef

Li, Z.S.[Zhao-Shuo], Liu, X.T.[Xing-Tong], Drenkow, N.[Nathan], Ding, A.[Andy], Creighton, F.X.[Francis X.], Taylor, R.H.[Russell H.], Unberath, M.[Mathias],
Revisiting Stereo Depth Estimation From a Sequence-to-Sequence Perspective with Transformers,
ICCV21(6177-6186)
IEEE DOI 2203
Costs, Estimation, Network architecture, Benchmark testing, Transformers, Stereo, Vision for robotics and autonomous vehicles BibRef

Wu, T.[Teng], Vallet, B.[Bruno], Pierrot-Deseilligny, M.[Marc],
PSMNet-FusionX3: LiDAR-Guided Deep Learning Stereo Dense Matching On Aerial Images,
PCV23(6527-6536)
IEEE DOI 2309
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Wu, T.[Teng], Vallet, B.[Bruno], Pierrot-Deseilligny, M.[Marc], Rupnik, E.,
A New Stereo Dense Matching Benchmark Dataset for Deep Learning,
ISPRS21(B2-2021: 405-412).
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Zhao, Y.H.[Yun-Han], Kong, S.[Shu], Fowlkes, C.C.[Charless C.],
Camera Pose Matters: Improving Depth Prediction by Mitigating Pose Distribution Bias,
CVPR21(15754-15763)
IEEE DOI 2111
Training, Image coding, Handheld computers, Perturbation methods, Cameras, Encoding BibRef

He, J.[Ju], Zhou, E.[Enyu], Sun, L.S.[Liu-Sheng], Lei, F.[Fei], Liu, C.Y.[Chen-Yang], Sun, W.X.[Wen-Xiu],
Semi-synthesis: A fast way to produce effective datasets for stereo matching,
WAD21(2878-2887)
IEEE DOI 2109
Training, Training data, Benchmark testing, Rendering (computer graphics), Environmental factors BibRef

Cai, C., Mordohai, P.,
Do End-to-end Stereo Algorithms Under-utilize Information?,
3DV20(374-383)
IEEE DOI 2102
Picture archiving and communication systems, Motion segmentation BibRef

Zhou, T., Hasheminasab, S.M., Lin, Y.C., Habib, A.,
Comparative Evaluation of Derived Image and LIDAR Point Clouds from UAV-based Mobile Mapping Systems,
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DOI Link 2012
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Cournet, M., Sarrazin, E., Dumas, L., Michel, J., Guinet, J., Youssefi, D., Defonte, V., Fardet, Q.,
Ground Truth Generation and Disparity Estimation for Optical Satellite Imagery,
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Saponaro, M., Capolupo, A., Caporusso, G., Mondino, E.B.[E. Borgogno], Tarantino, E.,
Predicting the Accuracy of Photogrammetric 3d Reconstruction From Camera Calibration Parameters Through A Multivariate Statistical Approach,
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Wan, W., Liu, Z., Wang, Y., Peng, M., Di, K., Liu, C., Li, L., Wang, J., Yu, T., Wang, R., Bo, Z.,
Topographic Mapping with Manipulator Arm Camera In Lunar Sample Return Mission,
ISPRS20(B3:1159-1163).
DOI Link 2012
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Wang, R., Di, K., Wan, W., Liu, Z., Wang, Y., Liang, W., Wang, Y., Chen, X., Zhi, S.,
Topographic Mapping and Analysis Based on 3d Reconstruction Model Of Simulated Asteroid,
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Re, C., Borin, N., Simioni, E., Lazzarotto, F., Zusi, M., Palumbo, P., Debei, S., Cremonese, G.,
Validation of the Stereo Observation Strategy of Simbio-sys Using A Virtual Simulator,
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Frommholz, D.,
A Synthetic 3d Scene for The Validation of Photogrammetric Algorithms,
ISSDQ19(1221-1228).
DOI Link 1912
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Berveglieri, A., Tommaselli, A.M.G., Santos, G., Santos, L.D., Honkavaara, E.,
Performance Evaluation of Sequential Band Orientation By Polynomial Models in Hyperspectral Cubes Collected With Uav,
EuroCOW-M3DMaN19(1625-1629).
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Berveglieri, A., Tommaselli, A.M.G., Santos, L.D.,
Tie Point Generation in Hyperspectral Cubes for Orientation With Polynomial Models,
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Re, C., Tulyakov, S., Simioni, E., Mudric, T., Cremonese, G., Thomas, N.,
Performance Evaluation of 3DPD, The Photogrammetric Pipeline for The Cassis Stereo Images,
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Mizginov, V.A., Kniaz, V.V.,
Evaluating The Accuracy of 3d Object Reconstruction From Thermal Images,
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Mattheuwsen, L., Bassier, M., Vergauwen, M.,
Theoretical Accuracy Prediction And Validation of Low-end And High-end Mobile Mapping System in Urban, Residential And Rural Areas,
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Smolyanskiy, N., Kamenev, A., Birchfield, S.T.,
On the Importance of Stereo for Accurate Depth Estimation: An Efficient Semi-Supervised Deep Neural Network Approach,
AutoDrive18(1120-11208)
IEEE DOI 1812
Cameras, Estimation, Laser radar, Buildings, Automobiles, Roads, Graphics processing units BibRef

Sharma, A.[Aashish], Cheong, L.F.[Loong-Fah], Heng, L., Tan, R.T.,
Nighttime Stereo Depth Estimation using Joint Translation-Stereo Learning: Light Effects and Uninformative Regions,
3DV20(23-31)
IEEE DOI 2102
Training, Estimation, Transforms, Testing, Stereo vision, Optimization BibRef

Sharma, A.[Aashish], Cheong, L.F.[Loong-Fah],
Into the Twilight Zone: Depth Estimation Using Joint Structure-Stereo Optimization,
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di Rita, M., Nascetti, A., Crespi, M.,
FOSS4G Date Assessment On the Isprs Optical Stereo Satellite Data: A Benchmark for DSM Generation,
Hannover17(635-638).
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Dataset, Stereo. benchmark dataset with several stereo data sets from space borne stereo sensors BibRef

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Modeling depth uncertainty of desynchronized multi-camera systems,
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cameras, image reconstruction, rendering (computer graphics), synchronisation, virtual reality, Synchronization error BibRef

Hafeez, J., Hamacher, A., Kwon, S., Lee, S.,
Performance evaluation of patterns for image-based 3D model reconstruction of textureless objects,
IC3D17(1-5)
IEEE DOI 1804
feature extraction, image reconstruction, image texture, photogrammetry, solid modelling, surface comparison BibRef

Bouchard, J.[Jonathan], Clark, J.J.[James J.],
Half-occluded regions: The key to detecting a diverse array of defects in S3D imagery,
IC3D17(1-8)
IEEE DOI 1804
Stereoscopic 3D. Visible in one view, not the other. image matching, solid modelling, stereo image processing, visual perception, S3D imagery, window violation BibRef

Kim, S., Min, D.B.[Dong-Bo], Ham, B.[Bumsub], Kim, S., Sohn, K.,
Deep stereo confidence prediction for depth estimation,
ICIP17(992-996)
IEEE DOI 1803
Convolutional neural networks, Estimation, Feature extraction, Impedance matching, Network architecture, Reliability, Training, stereo matching BibRef

Poggi, M.[Matteo], Mattoccia, S.[Stefano],
Learning to Predict Stereo Reliability Enforcing Local Consistency of Confidence Maps,
CVPR17(4541-4550)
IEEE DOI 1711
Estimation, Feature extraction, Neurons, Pattern matching, Reliability, Training BibRef

Ley, A.[Andreas], Hänsch, R.[Ronny], Hellwich, O.[Olaf],
SyB3R: A Realistic Synthetic Benchmark for 3D Reconstruction from Images,
ECCV16(VII: 236-251).
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Hanel, A., Hoegner, L., Stilla, U.,
Towards The Influence Of A Car Windshield On Depth Calculation With A Stereo Camera System,
ISPRS16(B5: 461-468).
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Mukherjee, S., Cheng, I., Guddeti, R.M.R., Basu, A.,
Entropy-difference based stereo error detection,
IVMSP16(1-5)
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Cost function BibRef

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Calibration of Shared Flat Refractive Stereo Systems,
ICIAR16(433-442).
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Aguiar, J.[João], Pinto, A.M.[Andry Maykol], Cruz, N.A.[Nuno A.], Matos, A.C.[Anibal C.],
The Impact of Convergence Cameras in a Stereoscopic System for AUVs,
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Kerner, S., Kaufman, I., Raizman, Y.,
Role of Tie-Points Distribution in Aerial Photography,
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Honauer, K., Maier-Hein, L., Kondermann, D.,
The HCI Stereo Metrics: Geometry-Aware Performance Analysis of Stereo Algorithms,
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Algorithm design and analysis BibRef

Vargas, C.[Camilo], Cabezas, I.[Ivan], Branch, J.W.[John W.],
Stereo Correspondence Evaluation Methods: A Systematic Review,
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Kim, J.H.[Jung-Hwan], Lee, S.H.[Sang-Hoon],
Deep blind image quality assessment by employing FR-IQA,
ICIP17(3180-3184)
IEEE DOI 1803
Correlation, Databases, Distortion, Image quality, Machine learning, Measurement, Training, Convolutional neural network, deep learning, no-reference image quality assessment BibRef

Kim, H.[Haksub], Kim, J.H.[Jung-Hwan], Lee, S.H.[Sang-Hoon],
3D perception based quality pooling on stereoscopic image,
ICIP15(3382-3386)
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3D Perception BibRef

Guney, F.[Fatma], Geiger, A.[Andreas],
Displets: Resolving stereo ambiguities using object knowledge,
CVPR15(4165-4175)
IEEE DOI 1510
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Anai, T., Kochi, N., Yamada, M., Sasaki, T., Otani, H., Sasaki, D., Nishimura, S., Kimoto, K., Yasui, N.,
Examination About Influence for Precision of 3D Image Measurement from the Ground Control Point Measurement and Surface Matching,
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Kondermann, D.[Daniel], Nair, R.[Rahul], Honauer, K.[Katrin], Krispin, K., Andrulis, J., Brock, A., Güssefeld, B.[Burkhard], Rahimimoghaddam, M., Hofmann, S.[Sabine], Brenner, C.[Claus], Jähne, B.[Bernd],
The HCI Benchmark Suite: Stereo and Flow Ground Truth with Uncertainties for Urban Autonomous Driving,
CVVT16(19-28)
IEEE DOI 1612
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Kondermann, D.[Daniel], Nair, R.[Rahul], Meister, S.[Stephan], Mischler, W.[Wolfgang], Güssefeld, B.[Burkhard], Honauer, K.[Katrin], Hofmann, S.[Sabine], Brenner, C.[Claus], Jähne, B.[Bernd],
Stereo Ground Truth with Error Bars,
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Varekamp, C., Hinnen, K., Simons, W.,
Detection and correction of disparity estimation errors via supervised learning,
IC3D13(1-7)
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error correction BibRef

Thoeni, K., Giacomini, A., Murtagh, R., Kniest, E.,
A comparison of multi-view 3D reconstruction of a rock wall using several cameras and a laser scanner,
CloseRange14(573-580).
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Dall'Asta, E., Roncella, R.,
A comparison of semiglobal and local dense matching algorithms for surface reconstruction,
CloseRange14(187-194).
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GCPR14(31-42).
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Dataset, Stereo. Award, GCPR. BibRef

Cavegn, S., Haala, N., Nebiker, S., Rothermel, M., Tutzauer, P.,
Benchmarking High Density Image Matching for Oblique Airborne Imagery,
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Pinggera, P.[Peter], Pfeiffer, D.[David], Franke, U.[Uwe], Mester, R.[Rudolf],
Know Your Limits: Accuracy of Long Range Stereoscopic Object Measurements in Practice,
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Griessbach, D.G., Baumbach, D.B., Boerner, A.B., Zuev, S.Z.,
Accuracy Evaluation of Stereo Vision Aided Inertial Navigation for Indoor Environments,
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Hamilton, O.K.[Oliver K.], Breckon, T.P.[Toby P.], Bai, X.J.[Xue-Jiao], Kamata, S.I.[Sei-Ichiro],
A foreground object based quantitative assessment of dense stereo approaches for use in automotive environments,
ICIP13(418-422)
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Accuracy BibRef

Zhao, Y.[Yin], Zhang, Y.C.[Yi-Chen], Yu, L.[Lu],
Subjective study of binocular rivalry in stereoscopic images with transmission and compression artifacts,
ICIP13(132-135)
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Databases BibRef

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Field Accuracy Test of RPAS Photogrammetry,
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Generating Dense Point Correspondence Ground-Truth across Multiple Views,
3DIMPVT12(214-221).
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Real-World Stereo-Analysis Evaluation,
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Quality Assessment of Non-dense Image Correspondences,
UnOptFlow12(II: 114-123).
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A Method for Reducing the Cardinality of the Pareto Front,
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González-Aguilera, D., Fernández-Hernández, J., Mancera-Taboada, J., Rodríguez-Gonzálvez, P., Hernández-López, D., Felipe-García, B., Gozalo-Sanz, I., Arias-Perez, B.,
3d Modelling and Accuracy Assessment of Granite Quarry Using Unmmanned Aerial Vehicle,
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Choi, S.Y., Kang, J.M., Shin, D.S.,
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Smith, M.J., Kokkas, N.,
Assessing the Photogrammetric Potential of Cameras in Portable Devices,
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Burkhard, J., Cavegn, S., Barmettler, A., Nebiker, S.,
Stereovision Mobile Mapping: System Design and Performance Evaluation,
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Yanagi, H., Chikatsu, H.,
Construction of a System for Defining Areas Which Are Not Obtained Data from Stereo Images,
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Toutin, T., Schmitt, C.V., Wang, H., Reinartz, P.,
3D Photogrammetric Processing of Worldview-2 Data Without GCP,
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Dataset, Stereo. Color and depth pairs. BibRef

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IEEE DOI 1111
BibRef

Wang, W.[Wei], Zhao, L.P.[Li-Ping],
Geolocation Accuracy Evaluation of GeoEye-1 Stereo Image Pair,
ISIDF11(1-4).
IEEE DOI 1111
BibRef

Zhu, K.[Ke], d'Angelo, P.[Pablo], Butenuth, M.[Matthias],
A Performance Study on Different Stereo Matching Costs Using Airborne Image Sequences and Satellite Images,
PIA11(159-170).
Springer DOI 1110
BibRef

Khoshelham, K.[Kourosh],
Accuracy Analysis Of Kinect Depth Data,
Laser11(xx-yy).
DOI Link 1109
BibRef

Mannan, F.[Fahim], Langer, M.S.[Michael S.],
Performance of Stereo Methods in Cluttered Scenes,
CRV11(94-101).
IEEE DOI 1105
BibRef

Natalia, K.,
Photo-based 3D scanning vs. laser scanning: Competitive data acquisition methods for digital terrain modelling of steep mountain slopes,
HighRes11(xx-yy).
PDF File. 1106
BibRef

Bianco, G., Gallo, A., Bruno, F., Muzzupappa, M.,
A Comparison Between Active and Passive Techniques for Underwater 3D Applications,
3DARCH11(xx-yy).
PDF File. 1103
Stereo, structured light. BibRef

Zhang, T.[Tao], Boult, T.E.[Terry E.],
Realistic stereo error models and finite optimal stereo baselines,
WACV11(426-433).
IEEE DOI 1101
BibRef

Haeusler, R.[Ralf], Klette, R.[Reinhard],
Analysis of KITTI Data for Stereo Analysis with Stereo Confidence Measures,
UnOptFlow12(II: 158-167).
Springer DOI 1210
BibRef
And:
Disparity Confidence Measures on Engineered and Outdoor Data,
CIARP12(624-631).
Springer DOI 1209
BibRef
Earlier:
Benchmarking Stereo Data (Not the Matching Algorithms),
DAGM10(383-392).
Springer DOI 0109
Dataset, Stereo. BibRef

Barazzetti, L.[Luigi], Remondino, F.[Fabio], Scaioni, M.[Marco],
Extraction of accurate tie points for automated pose estimation of close-range blocks,
PCVIA10(A:151).
PDF File. 1009
BibRef
Earlier: A1, A3, A2:
Automation in 3-D Reconstruction: Results on Different Kinds of Close-Range Blocks,
CloseRange10(xx-yy).
PDF File. 1006
BibRef

Theiss, H.,
Error Propagation for Close-Range Single Image Relative Measurements,
CloseRange10(xx-yy).
PDF File. 1006
BibRef

Takahashi, G., Matsouka, R.,
Accuracy Of Measurement Using A Pair Of Stereo Images Acquired By Finepix Real 3d W1 Without Controls,
CloseRange10(xx-yy).
PDF File. 1006
BibRef

Kliparchuk, K.[Karl], Collins, D.[Denis],
Evaluation of stereoscopic Geoeye-1 satellite imagery to assess landscape and stand level characteristics,
CGC10(161).
PDF File. 1006
BibRef

Mukherjee, D.[Dibyendu], Bhatnagar, G.[Gaurav], Wu, Q.M.J.[Q.M. Jonathan],
Performance Evaluation of Multiresolution Methods in Disparity Estimation,
ICISP10(496-504).
Springer DOI 1006
BibRef

Gehrig, S.K.[Stefan K.], Schneider, N.[Nicolai], Franke, U.[Uwe],
Exploiting Traffic Scene Disparity Statistics for Stereo Vision,
ECVW14(688-695)
IEEE DOI 1409
FPGA; embedded vision; scene prior; semi-global matching; stereo vision
See also Improving Stereo Sub-Pixel Accuracy for Long Range Stereo. BibRef

Pfeiffer, D.[David], Gehrig, S.K.[Stefan K.], Schneider, N.[Nicolai],
Exploiting the Power of Stereo Confidences,
CVPR13(297-304)
IEEE DOI 1309
Evaluation BibRef

Schneider, N.[Nicolai], Gehrig, S.K.[Stefan K.], Pfeiffer, D.[David], Banitsas, K.[Konstantinos],
An Evaluation Framework for Stereo-Based Driver Assistance,
WTFCV11(27-51).
Springer DOI 1210
BibRef

Steingrube, P.[Pascal], Gehrig, S.K.[Stefan K.], Franke, U.[Uwe],
Performance Evaluation of Stereo Algorithms for Automotive Applications,
CVS09(285-294).
Springer DOI 0910
BibRef

Poli, D., Wolff, K., Gruen, A.,
Evaluation of WORLDVIEW-1 Stereo Scenes,
HighRes09(xx-yy).
PDF File. 0906
BibRef

Radhadevi, P.V., Nagasubramanian, V., Archana, M., Krishna, S., Solanki, S S., Geeta, V.,
Potential of High-resolution Indian Remote Sensing Satellite Imagery for Large Scale Mapping,
HighRes09(xx-yy).
PDF File. 0906
BibRef

Gonçalves, J.A.,
An Empirical Model for Orientation of ALOS-PRISM Images of Level 1-B2,
HighRes09(xx-yy).
PDF File. 0906
Japanese satellite ALOS with PRISM sensor for high resolution stereo imaging. BibRef

Bier, A.[Agnieszka], Luchowski, L.[Leszek],
Error Analysis of Stereo Calibration and Reconstruction,
MIRAGE09(230-241).
Springer DOI 0905
BibRef

Morales, S.[Sandino], Klette, R.[Reinhard],
Ground Truth Evaluation of Stereo Algorithms for Real World Applications,
CVVT10(152-162).
Springer DOI 1109
BibRef

Liu, Z.F.[Zhi-Feng], Klette, R.[Reinhard],
Approximated Ground Truth for Stereo and Motion Analysis on Real-World Sequences,
PSIVT09(874-885).
Springer DOI 0901
Stereo model analysis. BibRef

Wang, W.X.[Wei-Xi], Zhu, Q.[Qing], Liu, H.[Hao],
The Precision Analysis of 3D Reconstruction Based on Generalized Stereopair,
ISPRS08(B3b: 91 ff).
PDF File. 0807
BibRef

Xing, S.[Shuai], Xu, Q.[Qing], Zhang, Y.[Yan], He, Y.[Yu], Jin, G.W.[Guo-Wang],
Optical/SAR Sensors Stereo Positioning,
ISPRS08(B1: 993 ff).
PDF File. 0807
BibRef

Huhle, B.[Benjamin], Pirinen, O.[Ossi], Fleck, S.[Sven], Gotchev, A.[Atanas], Strasser, W.[Wolfgang],
Why HDR is Important for 3DTV Model Acquisition,
3DTV08(45-48).
IEEE DOI 0805
BibRef

Mierle, K.[Keir], MacLean, W.J.[W. James],
Evaluating Multiview Reconstruction,
CVS08(xx-yy).
Springer DOI 0805
BibRef

Herath, D.C., Kodagoda, K.R.S., Dissanayake, G.,
Modeling Errors in Small Baseline Stereo for SLAM,
ICARCV06(1-6).
IEEE DOI 0612
BibRef

Hao, X., Mayer, H.[Helmut],
Orientation and Auto-Calibration of Image Triplets and Sequences,
PIA05(xx-yy).
PDF File. 0509
BibRef

Mayer, H.[Helmut],
Analysis of Means to Improve Cooperative Disparity Estimation,
PIA05(xx-yy).
PDF File. 0509
BibRef
And:
Robust Least-Squares Adjustment Based Orientation and Auto-Calibration of Wide-Baseline Image Sequences,
BenCOS05(xx-yy).
PDF File. 0510
BibRef
Earlier:
Robust Orientation, Calibration, and Disparity Estimation of Image Triplets,
DAGM03(281-288).
Springer DOI 0310
BibRef

Nielsen, M., Andersen, H.J., Granum, E.,
Comparative Study of Disparity Estimations with Multi-Camera Configurations in Relation to Descriptive Parameters of Complex Biological Objects,
BenCOS05(xx-yy).
PDF File. 0510
BibRef

Henze, F., Siedler, G., Vetter, S.,
Integration of digital image analysis for automated measurements into a photogrammetric stereo evaluation system,
IEVM06(xx-yy).
PDF File. 0609
BibRef

Janowski, A., Sawicki, P., Szulwic, J.,
Internet database for photogrammetric close range applications,
IEVM06(xx-yy).
PDF File. 0609
Dataset, Photogrammetry. BibRef

Robertson, G.[Gary],
Precise dynamic measurement of structures automatically utilizing adaptive targeting,
IEVM06(xx-yy).
PDF File. 0609
Precise measurements of building walls. BibRef

Parian, J.A., Grün, A., Cozzani, A.,
High accuracy space structures monitoring by a close-range photogrammetric network,
IEVM06(xx-yy).
PDF File. 0609
BibRef

Tian, B.Z.[Bao-Zhong], Barron, J.L.[John L.],
A Quantitative Comparison of 4 Algorithms for Recovering Dense Accurate Depth,
CRV05(498-505).
IEEE DOI 0505
BibRef

Leclercq, P., Liu, J., Woodward, A., Delmas, P.,
Which Stereo Matching Algorithm for Accurate 3D Face Creation?,
IWCIA04(690-704).
Springer DOI 0505
Conclusion is that there is little difference for this task. BibRef

Huang, Y.[Yuman], Klette, R.[Reinhard],
A Comparison of Property Estimators in Stereology and Digital Geometry,
IWCIA04(421-431).
Springer DOI 0505
BibRef

Tappen, M.F., Freeman, W.T.,
Comparison of Graph Cuts with Belief Propagation for Stereo, Using Identical MRF Parameters,
ICCV03(900-907).
IEEE DOI 0311
Comparison of Graph Cuts and Belief Propogation for the same stereo algorithm. BibRef

Cyganek, B.[Boguslaw],
Adaptive Window Growing Technique for Efficient Image Matching,
IbPRIA05(I:308).
Springer DOI 0509
BibRef
Earlier:
Comparison of Nonparametric Transformations and Bit Vector Matching for Stereo Correlation,
IWCIA04(534-547).
Springer DOI 0505
To deal with featureless areas. BibRef

Cyganek, B.[Boguslaw],
Computational Framework for Family of Order Statistic Filters for Tensor Valued Data,
ICIAR06(I: 156-162).
Springer DOI 0610
BibRef
Earlier:
Matching of the Multi-channel Images with Improved Nonparametric Transformations and Weighted Binary Distance Measures,
IWCIA06(74-88).
Springer DOI 0606
BibRef
Earlier:
Object Detection in Multi-channel and Multi-scale Images Based on the Structural Tensor,
CAIP05(570).
Springer DOI 0509

See also Object Recognition with the HOSVD of the Multi-model Space-Variant Pattern Tensors. BibRef

Cyganek, B.[Boguslaw], Borgosz, J.[Jan],
An Improved Variogram Analysis of the Maximum Expected Disparity in Stereo Images,
SCIA03(640-645).
Springer DOI 0310
BibRef
Earlier:
A Comparative Study of Performance and Implementation of Some Area-Based Stereo Algorithms,
CAIP01(709 ff.).
Springer DOI 0210
BibRef

Leclercq, P., Morris, J.,
Robustness to noise of stereo matching,
CIAP03(606-611).
IEEE DOI 0310
BibRef

Elaksher, A.F.[Ahmed F.], Elghazali, M.[Mohammed], Sayed, A.[Ashraf], Elmanadilli, Y.[Yasser],
Assessment of Two Cheap Close-Range Feature Extraction Systems,
PCV02(A: 97). 0305
BibRef

Soukup, L.[Lubomir],
Rigorous Quality Assessment of 3D Object Reconstruction for an Arbitrary Configuration of Control Points,
PCV02(B: 263). 0305
BibRef

Baker, S.[Simon], Sim, T.[Terence], Kanade, T.[Takeo],
A Characterization of Inherent Stereo Ambiguities,
ICCV01(I: 428-435).
IEEE DOI 0106
BibRef

Schreer, O., Brandenburg, N., Kauff, P.,
A Comparative Study on Disparity Analysis Based on Convergent and Rectified Views,
BMVC00(xx-yy).
PDF File. 0009
BibRef

Wang, J.Z.[James Ze], Fischler, M.A.[Martin A.],
Visual Similarity, Judgemental Certainty and Stereo Correspondence,
DARPA98(1237-1248). BibRef 9800

Albrecht, P.[Peter], Michaelis, B.[Bernd],
Stereo Photogrammetry with Improved Spatial Resolution,
ICPR98(Vol I: 845-849).
IEEE DOI 9808
BibRef

Georgis, N.[Nikos],
Three Dimensional Reconstruction and Lay Planning for Industrial Automation,
Ph.D.Thesis, Univ. of Surrey, December 1994. BibRef 9412

Mandelbaum, R., Kamberova, G., and Mintz, M.,
Stereo Depth Estimation: A Confidence Interval Approach,
ICCV98(503-509).
IEEE DOI BibRef 9800

Kamberova, G.[Gerda], and Bajcsy, R.[Ruzena],
Sensor Errors and the Uncertainties in Stereo Reconstruction,
EEMCV98(xx). BibRef 9800
And: EEMTV98(xx) BibRef

Roy, S.[Sébastien], Meunier, J., Cox, I.J.[Ingemar J.],
Cylindrical Rectification to Minimize Epipolar Distortion,
CVPR97(393-399).
IEEE Abstract.
IEEE DOI 9704
Remap images to a cylinder rather than a plane. BibRef

Roy, S.[Sébastien], Cox, I.J.[Ingemar J.],
Direct estimation of rotation from two frames via epipolar search,
CAIP95(880-887).
Springer DOI 9509
BibRef

Xiong, Y., Matthies, L.H.,
Error Analysis of a Real Time Stereo System,
CVPR97(1087-1093).
IEEE Abstract.
IEEE DOI 9704
Errors foreshortening, misalignment, systematic 0.3 pixel errors lead to significant range errors. Correct these and improve precision. BibRef

Rodin, V., Ayache, A.,
Axial Stereovision: Modelization and Comparison Between two Calibration Methods,
ICIP94(II: 725-729).
IEEE DOI 9411
BibRef

Maimone, M.W., Shafer, S.A.,
A Taxonomy for Stereo Computer Vision Experiments,
PERF96(XX-YY).
HTML Version. BibRef 9600

Nagao, K.,
Direct Methods for Evaluating the Planarity and Rigidity of a Surface Using Only 2D Views,
ICPR96(I: 417-422).
IEEE DOI 9608
(Matsushita Electric Industrial Res. Lab., J) BibRef

Baratoff, G.,
On the Sensitivity of Estimating Ordinal and Metric Structure of Smooth Surfaces from Parallax,
ICPR96(I: 275-279).
IEEE DOI 9608
(Univ. of Maryland, USA) BibRef

Ohta, Y., Satoh, K., Kitahara, I., Matsuura, T.,
Displaying Motion Parallax by Occlusion Detectable Stereo,
CVVRHC98(Sensing and Rendering Real Scenes). BibRef 9800

Satoh, K., Ohta, Y.,
Occlusion Detectable Stereo: Systematic Comparison of Detection Algorithms,
ICPR96(I: 280-286).
IEEE DOI 9608
(Univ. of Tsukuba, J) BibRef

Bajcsy, R.K., Krotkov, E.P., and Mintz, M.,
Models of Errors and Mistakes in Machine Perception, Part 1. First Results for Computer Vision Range Measurements,
DARPA87(194-204). Stereo, Evaluation. Error analysis for depth from focus, depth from point stereo, and depth from line based stereo. BibRef 8700

Nguyen, T.C., Huang, T.S.,
Quantization Errors in Axial Motion Stereo on Rectangular-Tessellated Image Sensors,
ICPR92(I:13-16).
IEEE DOI BibRef 9200

Chan, K.L., Forrest, A.K.,
An empirical study on the effects of spatial discretization error in a stereo vision system,
BMVC90(xx-yy).
PDF File. 9009
BibRef

Chapter on Stereo: Three Dimensional Descriptions from Two or More Views, Binocular, Trinocular continues in
Stereo: Real Time Systems, Hardware Implementations .