10.6 Multiple Cameras or Views

Pix4D,
3D model of buildings, cities, from images.
WWW Link. Vendor, 3-D Construction. Aerial and terrestrial photogrammetry and mapping. EPFL Spinoff:
See also Ecole Polytechnique Fédérale de Lausanne.

Murray, D.W.,
Recovering Range Using Virtual Multicamera Stereo,
CVIU(61), No. 2, March 1995, pp. 285-291.
DOI Link
HTML Version. BibRef 9503

Murray, D.W., and Beardsley, P.A.,
Range recovery using virtual multi-camera stereo,
BMVC92(29-38).
PDF File. BibRef 9200

Beardsley, P.A., Brady, J.M., Murray, D.W.,
Prediction of stereo disparity using optical flow,
BMVC90(xx-yy).
PDF File. 9009
BibRef

Berthod, M., Shekarforoush, H., Werman, M., Zerubia, J.B.,
Reconstruction of High Resolution 3D Visual Information Using Sub-pixel Camera Displacements,
CVPR94(654-657).
IEEE DOI BibRef 9400

Baltsavias, E.P.,
Multiphoto Geometrically Constrained Matching,
Ph.D.Thesis (Zurich), December 1991. A Photogrammetry thesis on image matching exploring many of the problems in matching at the image level for stereo analysis. BibRef 9112

Bridwell, N.J.[Nelson J.], Huang, T.S.[Thomas S.],
A Discrete Spatial Representation for Lateral Motion Stereo,
CVGIP(21), No. 1, January 1983, pp. 33-57.
Elsevier DOI Laterally translating camera. BibRef 8301

Yoshida, K., and Hirose, S.,
Real-Time Stereo Vision with Multiple Arrayed Camera,
CRA92(1765-1770). BibRef 9200

Gruen, A., and Baltsavias, E.,
Geometrically Constrained Multiphoto Matching,
PhEngRS(54), No. 5, 1988, pp. 633-641. BibRef 8800

Zhang, L.[Li], Gruen, A.[Armin],
Multi-image matching for DSM generation from IKONOS imagery,
PandRS(60), No. 3, May 2006, pp. 195-211.
Elsevier DOI 0610
High-resolution satellite imagery; IKONOS; Image matching; DSM generation BibRef

Tateda, M., Naruse, H., Ide, A., Nobiki, A.,
Integration Of Overlapping Maps Made By Stereo Vision Using View Field Information,
SMC(24), No. 8, August 1994, pp. 1273-1279. BibRef 9408

Pollard, S.B.[Stephen B.], Porrill, J.[John], Mayhew, J.E.W.[John E.W.], Frisby, J.P.[John P.],
Matching Geometrical Descriptions in Three-Space,
IVC(5), No. 2, May 1987, pp. 73-78.
Elsevier DOI Combine, edge-based stereo. BibRef 8705

Thacker, N.A.[Neil A.], Mayhew, J.E.W.[John E.W.],
Optimal Combination of Stereo Camera Calibration from Arbitrary Stereo Images,
IVC(9), No. 1, February 1991, pp. 27-32.
Elsevier DOI BibRef 9102
Earlier: BMVC90(xx-yy).
PDF File. 9009
BibRef

Porrill, J.[John], Pollard, S.B.[Stephen B.], Mayhew, J.E.W.[John E.W.],
Optimal Combination of Multiple Sensors Including Stereo Vision,
IVC(5), No. 2, May 1987, pp. 174-180.
Elsevier DOI Multiple sources. BibRef 8705

Pridmore, A.P., Porrill, J.[John], Mayhew, J.E.W.[John E.W.],
Segmentation and Description of Binocularly Viewed Contours,
IVC(5), No. 2, May 1987, pp. 132-138.
Elsevier DOI BibRef 8705

Pollard, S.B., Pridmore, T.P., Porrill, J., Mayhew, J.E.W.[John E.W.], Frisby, J.P.[John P.],
Geometrical Modeling from Multiple Stereo Views,
IJRR(8), No. 4, 1989, pp. 3-32. BibRef 8900

Pollard, S.B.[Stephen B.], Porrill, J.[John], Mayhew, J.E.W.[John E.W.],
Recovering Partial 3D Wire Frame Descriptions from Stereo Data,
IVC(9), No. 1, February 1991, pp. 58-65.
Elsevier DOI BibRef 9102
Earlier: BMVC90(xx-yy).
PDF File. 9009
BibRef

Pollard, S.B., Mayhew, J.E.W.[John E.W.], Frisby, J.P.[John P.],
PMF: A Stereo Correspondence Algorithm Using a Disparity Gradient Limit,
Perception(14), 1985, pp. 449-470. Relaxation based matching scheme. BibRef 8500

Mohr, R.[Roger], Boufama, B.S.[Boubakeur S.], Brand, P.[Pascal],
Understanding Positioning from Multiple Images,
AI(78), No. 1-2, October 1995, pp. 213-238.
Elsevier DOI BibRef 9510

Florou, G., Mohr, R.,
What Accuracy for 3D Measurements with Cameras?,
ICPR96(I: 354-358).
IEEE DOI 9608
(GRAVIR, F) BibRef

Muller, R.,
Three-Dimensional (3-D) Image-Reconstruction from Series of Light-Microscopic Exposures,
Optik(104), No. 2, December 1996, pp. 55-66. 9701
BibRef

Basri, R.[Ronen], Grove, A.J.[Adam J.], Jacobs, D.W.[David W.],
Efficient determination of shape from multiple images containing partial information,
PR(31), No. 11, November 1998, pp. 1691-1703. BibRef 9811
Earlier:
Elsevier DOI ICPR96(I: 268-274).
IEEE DOI 9608
(The Weizmann Inst. of Science, IL) BibRef

Park, J.I.[Jong-Il], Inoue, S.[Seiki],
Acquisition of sharp depth map from multiple cameras,
SP:IC(14), No. 1-2, November 1998, pp. 7-19.
Elsevier DOI BibRef 9811
Earlier:
Hierarchical depth mapping from multiple cameras,
CIAP97(I: 685-692).
Springer DOI 9709
Overcome occlusions. BibRef

Tsai, C.J.[Chun-Jen], Katsaggelos, A.K.,
Sequential Construction of 3-D Based Scene Description,
CirSysVideo(10), No. 4, June 2000, pp. 576-584.
IEEE Top Reference. 0006
BibRef
Earlier: ICIP99(II:510-514).
IEEE DOI BibRef

Kahl, F.[Fredrik], Heyden, A.[Anders], Quan, L.[Long],
Minimal Projective Reconstruction Including Missing Data,
PAMI(23), No. 4, April 2001, pp. 418-424.
IEEE Abstract.
IEEE DOI 0001
BibRef
Earlier: A3, A2, A1:
Minimal Projective Reconstruction with Missing Data,
CVPR99(II: 210-216).
IEEE Abstract.
IEEE DOI How to analyze the conditions for reconstruction. BibRef

Cheng, Y.Q., Wang, X.G., Collins, R.T., Riseman, E.M., Hanson, A.R.,
Three-Dimensional Reconstruction of Points and Lines with Unknown Correspondence across Images,
IJCV(45), No. 2, November 2001, pp. 129-156.
DOI Link 0111
Line based multi-image stereo analysis. BibRef

Cheng, Y.Q.[Yong-Qing], Collins, R.T.[Robert T.], Wang, X.G.[Xiao-Guang], Riseman, E.M.[Edward M.], Hanson, A.R.[Allen R.],
Erratum: Three-Dimensional Reconstruction of Points and Lines with Unknown Correspondence,
IJCV(47), No. 1-3, April-June 2002, pp. 289-289.
DOI Link 0203
BibRef

Wang, X.G.[Xiao-Guang], Cheng, Y.Q.[Yong-Qing], Collins, R.T.[Robert T.], and Hanson, A.R.,
Determining Correspondences and Rigid Motion of 3D Point Sets with Missing Data,
CVPR96(252-257).
IEEE Abstract.
IEEE DOI BibRef 9600

Collins, R.T., Hanson, A.R., Riseman, E.M., and Cheng, Y.Q.,
Model Matching and Extension for Automated 3D Site Modeling,
DARPA93(197-203). Matching the model (wire-frame) to the extracted data and extending the existing model. BibRef 9300

Irani, M.[Michal], Anandan, P., Cohen, M.[Meir],
Direct Recovery of Planar-Parallax from Multiple Frames,
PAMI(24), No. 11, November 2002, pp. 1528-1534.
IEEE Abstract. 0211
Depth in terms of plan plus parallax. BibRef

Davison, A.J.[Allan Joseph], Haslam, J.[Jane], Lyons, A.R.[Alexander Ralph], Rowe, S.M.[Simon Michael], Taylor, R.I.[Richard Ian],
Image processing apparatus,
US_Patent6,668,082, Dec 23, 2003
WWW Link. multiple views for 3-D BibRef 0312

Kolmogorov, V.[Vladimir], Zabih, R.[Ramin],
What Energy Functions Can Be Minimized via Graph Cuts?,
PAMI(26), No. 2, February 2004, pp. 147-159.
IEEE DOI 0402
BibRef
Earlier: ECCV02(III: 65 ff.). Award, ECCV. Award, Koenderink Prize.
Springer DOI 0205
For Code:
See also Kolmogorov and Zabih's Graph Cuts Stereo Matching Algorithm.
See also Fast Approximate Energy Minimization via Graph Cuts.
See also Performance vs computational efficiency for optimizing single and dynamic MRFs: Setting the state of the art with primal-dual strategies. BibRef

Kolmogorov, V.[Vladimir], Zabih, R.[Ramin],
Multi-camera Scene Reconstruction via Graph Cuts,
ECCV02(III: 82 ff.). Award, ECCV.
Springer DOI 0205
BibRef
Earlier:
Computing Visual Correspondence with Occlusions via Graph Cuts,
ICCV01(II: 508-515).
IEEE DOI 0106
Handle occlusions in correspondence. code is available:
WWW Link. Code, Stereo. BibRef

Zabih, R.,
Solving Linear Inverse Systems with Graph Cuts,
BMVC08(xx-yy).
PDF File. 0809
BibRef

Kolmogorov, V.[Vladimir], Rother, C.[Carsten],
Comparison of Energy Minimization Algorithms for Highly Connected Graphs,
ECCV06(II: 1-15).
Springer DOI 0608
BibRef

Kim, J.[Junhwan], Kolmogorov, V., Zabih, R.,
Visual correspondence using energy minimization and mutual information,
ICCV03(1033-1040).
IEEE DOI 0311
BibRef

Kolmogorov, V.[Vladimir], Monasse, P.[Pascal], Tan, P.[Pauline],
Kolmogorov and Zabih's Graph Cuts Stereo Matching Algorithm,
IPOL(2014), No. 2014, pp. 220-251.
DOI Link 1411
Code, Stereo Matching.
See also What Energy Functions Can Be Minimized via Graph Cuts?.
See also Performance vs computational efficiency for optimizing single and dynamic MRFs: Setting the state of the art with primal-dual strategies. BibRef

Pal, C.J.[Christopher J.], Weinman, J.J.[Jerod J.], Tran, L.C.[Lam C.], Scharstein, D.[Daniel],
On Learning Conditional Random Fields for Stereo: Exploring Model Structures and Approximate Inference,
IJCV(99), No. 3, September 2012, pp. 319-337.
WWW Link. 1206
BibRef
Earlier: A2, A3, A1, Only:
Efficiently Learning Random Fields for Stereo Vision with Sparse Message Passing,
ECCV08(I: 617-630).
Springer DOI 0810
BibRef

Scharstein, D.[Daniel], Pal, C.J.[Christopher J.],
Learning Conditional Random Fields for Stereo,
CVPR07(1-8).
IEEE DOI 0706
BibRef

Szeliski, R.S.[Richard S.], Zabih, R.[Ramin], Scharstein, D.[Daniel], Veksler, O.[Olga], Kolmogorov, V.[Vladimir], Agarwala, A.[Aseem], Tappen, M.[Marshall], Rother, C.[Carsten],
A Comparative Study of Energy Minimization Methods for Markov Random Fields with Smoothness-Based Priors,
PAMI(30), No. 6, June 2008, pp. 1068-1080.
IEEE DOI 0804
Survey, Energy Minimization. BibRef
Earlier:
A Comparative Study of Energy Minimization Methods for Markov Random Fields,
ECCV06(II: 16-29).
Springer DOI 0608
Iterated conditional Modes (ICM) (
See also On the Statistical Analysis of Dirty Pictures. ), Graph Cuts (
See also Fast Approximate Energy Minimization via Graph Cuts. ), Max-Product Loopy Belief Propogation LBP (
See also Efficient Belief Propagation for Early Vision. ) or (
See also Learning Low-Level Vision. ) Tree Reweighted Message Passing (TRW) (Similar to LBP, but different) (
See also MAP Estimation via Agreement on (Hyper)Trees: Message-Passing and Linear-Programming Approaches. ) BibRef

Ma, Y.[Yi],
A Differential Geometric Approach to Multiple View Geometry in Spaces of Constant Curvature,
IJCV(58), No. 1, June 2004, pp. 37-53.
DOI Link 0403
Based upon an axiomatic formulation of vision system in a general Riemannian manifold. BibRef

Huang, K., Fossum, R.M., Ma, Y.,
Generalized Rank Conditions in Multiple View Geometry with Applications to Dynamical Scenes,
ECCV02(II: 201 ff.).
Springer DOI 0205
Constraints from the problem. BibRef

Liu, B.[Bing], Yu, M.[Maoyuan], Maier, D.[Dennis], Männer, R.[Reinhard],
An Efficient and Accurate Method for 3D-Point Reconstruction from Multiple Views,
IJCV(65), No. 3, December 2005, pp. 175-188.
Springer DOI 0601
BibRef

Tsin, Y., Kang, S.B.[Sing Bing], Szeliski, R.S.,
Stereo Matching with Linear Superposition of Layers,
PAMI(28), No. 2, February 2006, pp. 290-301.
IEEE DOI 0601
BibRef
Earlier:
Stereo matching with reflections and translucency,
CVPR03(I: 702-709).
IEEE DOI 0307
BibRef

Kang, S.B.[Sing Bing], Szeliski, R.S., Chai, J.X.[Jin-Xiang],
Handling Occlusions in Dense Multi-view Stereo,
CVPR01(I:103-110).
IEEE DOI 0110
Use a combination of shiftable windows and a dynamically se-lected subset of the neighboring images to do the matches. BibRef

Joulin, A.[Armand], Kang, S.B.[Sing Bing],
Recovering Stereo Pairs from Anaglyphs,
CVPR13(289-296)
IEEE DOI 1309
3D; anaglyph; image reconstruction. Almost the inverse of the stereo problem, but the same matching issues. BibRef

Mordohai, P.[Philippos], Medioni, G.,
Stereo Using Monocular Cues within the Tensor Voting Framework,
PAMI(28), No. 6, June 2006, pp. 968-982.
IEEE DOI 0605
BibRef
Earlier:
Dense multiple view stereo with general camera placement using tensor voting,
3DPVT04(725-732).
IEEE DOI 0412
BibRef
Earlier:
Perceptual grouping for multiple view stereo using tensor voting,
ICPR02(III: 639-644).
IEEE DOI 0211
Matching with occlusion and lack of texture. Multiple matching candidates embedded in disparity space, grouping applied in 3-D neighborhoods. Correct matches produce good surfaces. BibRef

Hu, X.Y.[Xiao-Yan], Mordohai, P.[Philippos],
A Quantitative Evaluation of Confidence Measures for Stereo Vision,
PAMI(34), No. 11, November 2012, pp. 2121-2133.
IEEE DOI 1209
BibRef
Earlier:
Evaluation of stereo confidence indoors and outdoors,
CVPR10(1466-1473).
IEEE DOI 1006
Compare 17 confidence measures. Binocular and multibaseline data with groundtruth. Rank on capability to rank depth estimates, likelihood of correctness, and occlusion detection. BibRef

Hu, X.Y.[Xiao-Yan], Mordohai, P.[Philippos],
Robust Probabilistic Occupancy Grid Estimation from Positive and Negative Distance Fields,
3DIMPVT12(539-546).
IEEE DOI 1212
BibRef

Mordohai, P.[Philippos],
The Self-Aware Matching Measure for stereo,
ICCV09(1841-1848).
IEEE DOI 0909
BibRef

Schrameck, M.[Michel], Voyles, R.[Richard], Myers, T.[Tom], Bodor, R.[Robert], Masoud, O.[Osama],
Automatic Euclidean reconstruction for turn-table sequences by indirect epipolar search between pairs of views,
IVC(24), No. 7, July 2006, pp. 693-708.
Elsevier DOI 0608
Structure from motion -- small rotation. BibRef

Wang, J.H.[Jia-Hui], Saito, H.[Hideo], Kimura, M.[Makoto], Mochimaru, M.[Masaaki], Kanade, T.[Takeo],
Human Foot Reconstruction from Multiple Camera Images with Foot Shape Database,
IEICE(E89-D), No. 5, May 2006, pp. 1732-1742.
DOI Link 0605
BibRef
Earlier:
Shape reconstruction of human foot from multi-camera images based on PCA of human shape database,
3DIM05(424-431).
IEEE DOI 0508

See also Face Shape Reconstruction from Image Sequence Taken with Monocular Camera using Shape Database. BibRef

Kimura, M.[Makoto], Mochimaru, M.[Masaaki], Kanade, T.[Takeo],
3D measurement of feature cross-sections of foot while walking,
MVA(22), No. 2, March 2011, pp. 377-388.
WWW Link. 1103
BibRef

Ebrahimnezhad, H.[Hossein], Ghassemian, H.[Hassan],
Robust motion from space curves and 3d reconstruction from multiviews using perpendicular double stereo rigs,
IVC(26), No. 10, 1 October 2008, pp. 1397-1420.
Elsevier DOI 0804
3D reconstruction; Perpendicular double stereo; Silhouette; Structure from motion; Space curves; Unique points; Visual hull BibRef

Kolev, K.[Kalin], Klodt, M.[Maria], Brox, T.[Thomas], Cremers, D.[Daniel],
Continuous Global Optimization in Multiview 3D Reconstruction,
IJCV(84), No. 1, August 2009, pp. xx-yy.
Springer DOI 0905
BibRef
Earlier: EMMCVPR07(441-452).
Springer DOI 0708
BibRef

Kahl, F.[Fredrik], Agarwal, S.[Sameer], Chandraker, M.K.[Manmohan Krishna], Kriegman, D.J.[David J.], Belongie, S.J.[Serge J.],
Practical Global Optimization for Multiview Geometry,
IJCV(79), No. 3, September 2008, pp. xx-yy.
Springer DOI 0806
BibRef
Earlier: A2, A3, A1, A4, A5: ECCV06(I: 592-605).
Springer DOI 0608
A practical method for finding the provably globally optimal solution to numerous problems in projective geometry including multiview triangulation, camera resectioning and homography estimation. BibRef

Hartley, R.I.[Richard I.], Kahl, F.[Fredrik],
Optimal Algorithms in Multiview Geometry,
ACCV07(I: 13-34).
Springer DOI 0711
BibRef

Kahl, F., August, J.,
Multiview reconstruction of space curves,
ICCV03(1017-1024).
IEEE DOI 0311
BibRef

Enqvist, O.[Olof], Ask, E.[Erik], Kahl, F.[Fredrik], Åström, K.[Kalle],
Tractable Algorithms for Robust Model Estimation,
IJCV(112), No. 1, March 2015, pp. 115-129.
Springer DOI 1503
BibRef
Earlier:
Robust Fitting for Multiple View Geometry,
ECCV12(I: 738-751).
Springer DOI 1210
BibRef

Zhou, J., Wan, D., Wu, Y.,
The Chameleon-Like Vision System,
SPMag(27), No. 5, 2010, pp. 91-101.
IEEE DOI 1003
2 PTZ cameras. Control not conventional binocular. BibRef

Xi, Y.J.[Yong-Jian], Duan, Y.[Ye],
An Iterative Surface Evolution Algorithm for Multiview Stereo,
JIVP(2010), No. 2010, pp. xx-yy.
DOI Link 1003
BibRef

Montijano, E.[Eduardo], Sagues, C.[Carlos],
Distributed multi-camera visual mapping using topological maps of planar regions,
PR(44), No. 7, July 2011, pp. 1528-1539.
Elsevier DOI 1103
Distributed systems; Mapping BibRef

Zhang, L., Hu, Y., Zhang, J., Li, Y.,
Multiview geometry in traditional vision and omnidirectional vision under the L_inf-norm,
IET-CV(6), No. 1, 2012, pp. 13-20.
DOI Link 1201
BibRef

Furukawa, Y.[Yasutaka], Hernández, C.[Carlos],
Multi-View Stereo: A Tutorial,
FTCGV(9), No. 1-2, 2015, pp. 1-148.
DOI Link Survey, Stereo, Multi-View. 1507
BibRef

Miyan, S.B.[Saba Batool], Sato, J.[Jun],
Reconstructing Sequential Patterns without Knowing Image Correspondences,
IEICE(E98-D), No. 7, July 2015, pp. 1343-1352.
WWW Link. 1508
BibRef
Earlier: ACCV12(IV:484-496).
Springer DOI 1304
series of colored 3D points. BibRef

Ylimaki, M.[Markus], Kannala, J.H.[Ju-Ho], Holappa, J.[Jukka], Brandt, S.S.[Sami S.], Heikkila, J.[Janne],
Fast and accurate multi-view reconstruction by multi-stage prioritised matching,
IET-CV(9), No. 4, 2015, pp. 576-587.
DOI Link 1509
BibRef
Earlier: A1, A2, A3, A5, A4:
Robust and accurate multi-view reconstruction by prioritized matching,
ICPR12(2673-2676).
WWW Link. 1302
image matching BibRef

Hou, Y.X.[Yu-Xin], Solin, A.[Arno], Kannala, J.H.[Ju-Ho],
Unstructured Multi-view Depth Estimation Using Mask-Based Multiplane Representation,
SCIA19(54-66).
Springer DOI 1906
BibRef

Ylimäki, M.[Markus], Kannala, J.H.[Ju-Ho], Heikkilä, J.[Janne],
Optimizing the Accuracy and Compactness of Multi-view Reconstructions,
CAIP15(II:171-183).
Springer DOI 1511
BibRef

Sakai, S.[Shuji], Ito, K.[Koichi], Aoki, T.[Takafumi], Watanabe, T.[Takafumi], Unten, H.[Hiroki],
Phase-Based Window Matching with Geometric Correction for Multi-View Stereo,
IEICE(E98-D), No. 10, October 2015, pp. 1818-1828.
WWW Link. 1511
BibRef

Sakai, S.[Shuji], Ito, K.[Koichi], Aoki, T.[Takafumi], Masuda, T.[Tomohito], Unten, H.[Hiroki],
An Efficient Image Matching Method for Multi-View Stereo,
ACCV12(IV:283-296).
Springer DOI 1304
BibRef

Harwin, S.[Steve], Lucieer, A.[Arko], Osborn, J.[Jon],
The Impact of the Calibration Method on the Accuracy of Point Clouds Derived Using Unmanned Aerial Vehicle Multi-View Stereopsis,
RS(7), No. 9, 2015, pp. 11933.
DOI Link 1511
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Zhu, Z.K.[Zhao-Kun], Stamatopoulos, C.[Christos], Fraser, C.S.[Clive S.],
Accurate and occlusion-robust multi-view stereo,
PandRS(109), No. 1, 2015, pp. 47-61.
Elsevier DOI 1512
Multi-view stereo BibRef

Li, Z., Wang, K., Zuo, W., Meng, D., Zhang, L.,
Detail-Preserving and Content-Aware Variational Multi-View Stereo Reconstruction,
IP(25), No. 2, February 2016, pp. 864-877.
IEEE DOI 1601
Adaptation models BibRef

Nguyen, H.M.[Hoang Minh], Wünsche, B.C.[Burkhard C.], Delmas, P.[Patrice], Lutteroth, C.[Christof], Zhang, E.[Eugene],
A robust hybrid image-based modeling system,
VC(32), No. 5, May 2016, pp. 625-640.
Springer DOI 1605
3D models from sequence of unconstrained photos. BibRef

Nie, Y.M.[Yong-Ming], Yue, T.[Tao], Zhu, H.[Hao], Du, S.D.[Si-Dan], Cao, X.[Xun],
Robust multi-view stereo synthesized by various parameters model,
JVCIR(42), No. 1, 2017, pp. 183-191.
Elsevier DOI 1701
Multi-view stereo BibRef

Li, M.[Ming], Shi, L.[Li], Chen, X.D.[Xu-Dong], Du, S.D.[Si-Dan], Li, Y.[Yang],
Using Temporal Correlation to Optimize Stereo Matching in Video Sequences,
IEICE(E102-D), No. 6, June 2019, pp. 1183-1196.
WWW Link. 1906
BibRef

Robertini, N.[Nadia], Casas, D.[Dan], de Aguiar, E.[Edilson], Theobalt, C.[Christian],
Multi-view Performance Capture of Surface Details,
IJCV(124), No. 1, August 2016, pp. 96-113.
Springer DOI 1707
BibRef

Robertini, N.[Nadia], de Aguiar, E.[Edilson], Helten, T.[Thomas], Theobalt, C.[Christian],
Efficient Multi-view Performance Capture of Fine-Scale Surface Detail,
3DV14(5-12)
IEEE DOI 1503
Animation BibRef

Shibata, K.[Kazuki], Panahpour Teherani, M.[Mehrdad], Takahashi, K.[Keita], Fujii, T.[Toshiaki],
Estimation of Dense Displacement by Scale Invariant Polynomial Expansion of Heterogeneous Multi-View Images,
IEICE(E100-D), No. 9, September 2017, pp. 2048-2051.
WWW Link. 1709
BibRef

Zhao, H., Liu, H., Ding, Z., Fu, Y.,
Consensus Regularized Multi-View Outlier Detection,
IP(27), No. 1, January 2018, pp. 236-248.
IEEE DOI 1712
image reconstruction, image representation, learning (artificial intelligence), optimisation, outlier detection BibRef

Palma, G.[Gianpaolo], Boubekeur, T.[Tamy], Ganovelli, F.[Fabio], Cignoni, P.[Paolo],
Scalable non-rigid registration for multi-view stereo data,
PandRS(142), 2018, pp. 328-341.
Elsevier DOI 1807
Non-rigid registration, Multi-view-stereo, Low-frequency deformation, Scalable implementation BibRef

Hu, J.W.[Jing-Wen], Xia, G.S.[Gui-Song], Sun, H.[Hong],
An SRTM-Aided Epipolar Resampling Method for Multi-Source High-Resolution Satellite Stereo Observation,
RS(11), No. 6, 2019, pp. xx-yy.
DOI Link 1903
Binocular stereo. BibRef

Wei, M.Q.A.[Meng-Qi-Ang], Yan, Q.[Qingan], Luo, F.[Fei], Song, C.F.[Cheng-Fang], Xiao, C.X.[Chun-Xia],
Joint bilateral propagation upsampling for unstructured multi-view stereo,
VC(35), No. 6-8, June 2018, pp. 797-809.
WWW Link. 1906
BibRef

Xue, T.F.[Tian-Fan], Owens, A.[Andrew], Scharstein, D.[Daniel], Goesele, M.[Michael], Szeliski, R.S.[Richard S.],
Multi-frame stereo matching with edges, planes, and superpixels,
IVC(91), 2019, pp. 103771.
Elsevier DOI 1912
Stereo, Edge matching, Superpixels, Plane fitting BibRef

Yang, H., Chen, P., Chen, K., Lee, C., Chen, Y.,
FADE: Feature Aggregation for Depth Estimation With Multi-View Stereo,
IP(29), 2020, pp. 6590-6600.
IEEE DOI 2007
Feature extraction, Estimation, Image reconstruction, Cameras, Visualization, plane sweep algorithm BibRef

Chen, P., Yang, H., Chen, K., Chen, Y.,
MVSNet++: Learning Depth-Based Attention Pyramid Features for Multi-View Stereo,
IP(29), 2020, pp. 7261-7273.
IEEE DOI 2007
Feature extraction, Solid modeling, Computational modeling, Image reconstruction, Estimation, Training, plane sweep algorithm BibRef

Scholefield, A.[Adam], Ghasemi, A.[Alireza], Vetterli, M.[Martin],
Bound and Conquer: Improving Triangulation by Enforcing Consistency,
PAMI(42), No. 9, September 2020, pp. 2321-2326.
IEEE DOI 2008
BibRef
And: A2, A1, A3:
On the accuracy of point localisation in a circular camera-array,
ICIP15(981-985)
IEEE DOI 1512
Cameras, Uncertainty, Biological system modeling, Matrix decomposition, Geometry, Computational modeling, triangulation. Image sampling BibRef

Xiang, X.[Xiang], Wang, Z.Y.[Zhi-Yuan], Lao, S.S.[Shan-Shan], Zhang, B.C.[Bao-Chang],
Pruning multi-view stereo net for efficient 3D reconstruction,
PandRS(168), 2020, pp. 17-27.
Elsevier DOI 2009
Multi-view stereo, 3D reconstruction, Deep learning, Network pruning, Efficiency BibRef

Agarwal, S.[Sameer], Pryhuber, A.[Andrew], Thomas, R.R.[Rekha R.],
Ideals of the Multiview Variety,
PAMI(43), No. 4, April 2021, pp. 1279-1292.
IEEE DOI 2103
Cameras, Algebra, Optimization, Kernel, Terminology, Google, Projective geometry, structure-from-motion, nonlinear algebra BibRef

Stathopoulou, E.K.[Elisavet Konstantina], Battisti, R.[Roberto], Cernea, D.[Dan], Remondino, F.[Fabio], Georgopoulos, A.[Andreas],
Semantically Derived Geometric Constraints for MVS Reconstruction of Textureless Areas,
RS(13), No. 6, 2021, pp. xx-yy.
DOI Link 2104
BibRef

Chen, R.[Rui], Han, S.F.[Song-Fang], Xu, J.[Jing], Su, H.[Hao],
Visibility-Aware Point-Based Multi-View Stereo Network,
PAMI(43), No. 10, October 2021, pp. 3695-3708.
IEEE DOI 2109
BibRef
Earlier:
Point-Based Multi-View Stereo Network,
ICCV19(1538-1547)
IEEE DOI 2004
Code, Stereo.
WWW Link. Image reconstruction, Geometry, Task analysis, Aggregates, 3D deep learning. computational geometry, image motion analysis, image texture, iterative methods, Feature extraction BibRef

Ji, M.Q.[Meng-Qi], Zhang, J.Z.[Jin-Zhi], Dai, Q.H.[Qiong-Hai], Fang, L.[Lu],
SurfaceNet+: An End-to-end 3D Neural Network for Very Sparse Multi-View Stereopsis,
PAMI(43), No. 11, November 2021, pp. 4078-4093.
IEEE DOI 2110
Cameras, Surface reconstruction, Solid modeling, Geometry, view selection BibRef

Yang, Y.[Yang], Han, J.W.[Jun-Wei], Zhang, D.W.[Ding-Wen], Tian, Q.[Qi],
Exploring rich intermediate representations for reconstructing 3D shapes from 2D images,
PR(122), 2022, pp. 108295.
Elsevier DOI 2112
3D Reconstruction, Shape transformation, Intermediate representations BibRef

Masson, J.E.N.[Juliano Emir Nunes], Petry, M.R.[Marcelo Roberto], Coutinho, D.F.[Daniel Ferreira], de Mello Honório, L.[Leonardo],
Deformable convolutions in multi-view stereo,
IVC(118), 2022, pp. 104369.
Elsevier DOI 2202
Multi-view stereo, Depth map, Deep learning BibRef

Tian, Q.[Qing], Ma, C.[Chuang], Cao, M.[Meng], Chen, S.C.[Song-Can], Yin, H.J.[Hu-Jun],
A Convex Discriminant Semantic Correlation Analysis for Cross-View Recognition,
Cyber(52), No. 2, February 2022, pp. 849-861.
IEEE DOI 2202
Correlation, Semantics, Linear programming, Measurement, Analytical models, Face, Information science, discriminant semantic correlation analysis (DSCA) BibRef

Gao, Z.[Zan], Zhang, Y.[Yan], Zhang, H.[Hua], Guan, W.L.[Wei-Li], Feng, D.[Dong], Chen, S.Y.[Sheng-Yong],
Multi-Level View Associative Convolution Network for View-Based 3D Model Retrieval,
CirSysVideo(32), No. 4, April 2022, pp. 2264-2278.
IEEE DOI 2204
Solid modeling, Adaptation models, Computational modeling, Feature extraction, Data models, Integrated circuit modeling, multi-level BibRef

Xu, L.Y.[Luo-Yuan], Luo, Y.[Yawei], Luo, K.[Keyang], Wang, Y.[Yuesong], Guan, T.[Tao], Chen, Z.[Zhuo], Liu, W.K.[Wen-Kai],
Exploiting the Structure Information of Suppositional Mesh for Unsupervised Multiview Stereo,
MultMedMag(29), No. 1, January 2022, pp. 94-103.
IEEE DOI 2205
Faces, Costs, Cognition, Integrated circuits, Memory management, Feature extraction BibRef

Qi, S.[Shuai], Sang, X.Z.[Xin-Zhu], Yan, B.B.[Bin-Bin], Wang, P.[Peng], Chen, D.[Duo], Wang, H.C.[Hua-Chun], Ye, X.Q.[Xiao-Qian],
Unsupervised multi-view stereo network based on multi-stage depth estimation,
IVC(122), 2022, pp. 104449.
Elsevier DOI 2205
3D reconstruction, Depth estimation, Multi-view stereo BibRef

Liu, Z.[Zehua], Zhang, Y.[Yuhe], Gao, J.[Jian], Wang, S.R.[Shu-Rui],
VFMVAC: View-filtering-based multi-view aggregating convolution for 3D shape recognition and retrieval,
PR(129), 2022, pp. 108774.
Elsevier DOI 2206
Multi-view, Channel shuffle, Convolution, Recognition, Retrieval BibRef

Xu, Q.S.[Qing-Shan], Su, W.J.[Wan-Juan], Qi, Y.H.[Yu-Hang], Tao, W.B.[Wen-Bing], Pollefeys, M.[Marc],
Learning Inverse Depth Regression for Pixelwise Visibility-Aware Multi-View Stereo Networks,
IJCV(130), No. 8, August 2022, pp. 2040-2059.
Springer DOI 2207
BibRef

Su, W.J.[Wan-Juan], Xu, Q.S.[Qing-Shan], Tao, W.B.[Wen-Bing],
Uncertainty Guided Multi-View Stereo Network for Depth Estimation,
CirSysVideo(32), No. 11, November 2022, pp. 7796-7808.
IEEE DOI 2211
Uncertainty, Estimation, Costs, Reliability, Training, Task analysis, Memory management, Multi-view stereo, uncertainty estimation, deep learning BibRef

Schönberger, J.L.[Johannes L.], Zheng, E.[Enliang], Frahm, J.M.[Jan-Michael], Pollefeys, M.[Marc],
Pixelwise View Selection for Unstructured Multi-View Stereo,
ECCV16(III: 501-518).
Springer DOI 1611
BibRef

Song, B.[Boyang], Hu, X.G.[Xiao-Guang], Xiao, J.[Jin], Zhang, G.F.[Guo-Feng], Chen, T.[Tianyou],
Implicit neural refinement based multi-view stereo network with adaptive correlation,
IVC(124), 2022, pp. 104511.
Elsevier DOI 2208
Multi-view stereo, Implicit neural representation, Adaptive aggregation, Feature pyramid, Coarse-to-fine BibRef

Nair, N.S.[Nirmal S.], Nair, M.S.[Madhu S.],
On evolutionary computation techniques for multi-view triangulation,
MVA(31), No. 4, April 2020, pp. Article29.
Springer DOI 2005
BibRef

Nair, N.S.[Nirmal S.], Nair, M.S.[Madhu S.],
Multi-View Stereo Using Graph Cuts-Based Depth Refinement,
SPLetters(29), 2022, pp. 1903-1907.
IEEE DOI 2209
Optimization, Cameras, Estimation, Image reconstruction, Solid modeling, Costs, 3D reconstruction, multi-view stereo, graph cuts BibRef

Han, J.W.[Jia-Wei], Chen, X.M.[Xiao-Mei], Zhang, Y.T.[Yong-Tian], Hou, W.M.[Wei-Min], Hu, Z.[Zibo],
DEMVSNet: Denoising and depth inference for unstructured multi-view stereo on noised images,
IET-CV(16), No. 7, 2022, pp. 570-580.
DOI Link 2210
computer vision, neural net architecture, random noise BibRef

Zhou, H.Z.[Hui-Zhou], Zhao, H.L.[Hao-Liang], Wang, Q.[Qi], Lei, L.[Liang], Hao, G.[Gefei], Xu, Y.S.[Yu-Sheng], Ye, Z.[Zhen],
EMO-MVS: Error-Aware Multi-Scale Iterative Variable Optimizer for Efficient Multi-View Stereo,
RS(14), No. 23, 2022, pp. xx-yy.
DOI Link 2212
BibRef

Tong, W.[Wei], Guan, X.R.[Xiao-Rong], Kang, J.[Jian], Sun, P.Z.H.[Poly Z. H.], Law, R.[Rob], Ghamisi, P.[Pedram], Wu, E.Q.[Edmond Q.],
Normal Assisted Pixel-Visibility Learning With Cost Aggregation for Multiview Stereo,
ITS(23), No. 12, December 2022, pp. 24686-24697.
IEEE DOI 2212
Costs, Feature extraction, Estimation, Navigation, Image reconstruction, Geometry, Cost aggregation, depth estimation, surface normal BibRef

Zhang, J.Y.[Jing-Yang], Li, S.W.[Shi-Wei], Luo, Z.X.[Zi-Xin], Fang, T.[Tian], Yao, Y.[Yao],
Vis-MVSNet: Visibility-Aware Multi-view Stereo Network,
IJCV(131), No. 1, January 2023, pp. 199-214.
Springer DOI 2301
BibRef

Song, S.[Soohwan], Truong, K.G.[Khang Giang], Kim, D.[Daekyum], Jo, S.[Sungho],
Prior depth-based multi-view stereo network for online 3D model reconstruction,
PR(136), 2023, pp. 109198.
Elsevier DOI 2301
Multi-view stereo, Deep learning, Online 3D reconstruction BibRef

Gao, J.[Jian], Liu, J.[Jin], Ji, S.P.[Shun-Ping],
A general deep learning based framework for 3D reconstruction from multi-view stereo satellite images,
PandRS(195), 2023, pp. 446-461.
Elsevier DOI 2301
Multi-view stereo, Optical satellite images, Deep learning, Dense matching, 3D reconstruction BibRef

Wang, Z.Y.[Zi-Yun], Chaney, K.[Kenneth], Daniilidis, K.[Kostas],
EvAC3D: From Event-Based Apparent Contours to 3D Models via Continuous Visual Hulls,
ECCV22(VII:284-299).
Springer DOI 2211

WWW Link. 3D reconstruction from Event Cameras. BibRef

Ma, Z.[Zeyu], Teed, Z.[Zachary], Deng, J.[Jia],
Multiview Stereo with Cascaded Epipolar RAFT,
ECCV22(XXXI:734-750).
Springer DOI 2211
BibRef

Li, Z.Q.[Zhi-Qi], Wang, W.H.[Wen-Hai], Li, H.Y.[Hong-Yang], Xie, E.[Enze], Sima, C.H.[Chong-Hao], Lu, T.[Tong], Qiao, Y.[Yu], Dai, J.F.[Ji-Feng],
BEVFormer: Learning Bird's-Eye-View Representation from Multi-camera Images via Spatiotemporal Transformers,
ECCV22(IX:1-18).
Springer DOI 2211
BibRef

Guizilini, V.[Vitor], Vasiljevic, I.[Igor], Fang, J.[Jiading], Ambru, R.[Rare], Shakhnarovich, G.[Greg], Walter, M.R.[Matthew R.], Gaidon, A.[Adrien],
Depth Field Networks For Generalizable Multi-View Scene Representation,
ECCV22(XXXII:245-262).
Springer DOI 2211
BibRef

Wang, X.F.[Xiao-Feng], Zhu, Z.[Zheng], Huang, G.[Guan], Qin, F.[Fangbo], Ye, Y.[Yun], He, Y.[Yijia], Chi, X.[Xu], Wang, X.G.[Xin-Gang],
MVSTER: Epipolar Transformer for Efficient Multi-view Stereo,
ECCV22(XXXI:573-591).
Springer DOI 2211
BibRef

Ding, Y.K.[Yi-Kang], Zhu, Q.T.[Qing-Tian], Liu, X.Y.[Xiang-Yue], Yuan, W.T.[Wen-Tao], Zhang, H.[Haotian], Zhang, C.[Chi],
KD-MVS: Knowledge Distillation Based Self-supervised Learning for Multi-view Stereo,
ECCV22(XXXI:630-646).
Springer DOI 2211
BibRef

Chang, D.[Di], Božic, A.[Aljaž], Zhang, T.[Tong], Yan, Q.S.[Qing-Song], Chen, Y.[Yingcong], Süsstrunk, S.[Sabine], Nießner, M.[Matthias],
RC-MVSNet: Unsupervised Multi-View Stereo with Neural Rendering,
ECCV22(XXXI:665-680).
Springer DOI 2211
BibRef

Li, M.[Ming], Jin, X.Q.[Xue-Qian], Hu, X.[Xuejiao], Dai, J.Z.[Jing-Zhao], Du, S.[Sidan], Li, Y.[Yang],
MODE: Multi-view Omnidirectional Depth Estimation with 360° Cameras,
ECCV22(XXXIII:197-213).
Springer DOI 2211
BibRef

Ding, Y.K.[Yi-Kang], Li, Z.Y.[Zhen-Yang], Huang, D.[Dihe], Li, Z.H.[Zhi-Heng], Zhang, K.[Kai],
Enhancing Multi-View Stereo with Contrastive Matching and Weighted Focal Loss,
ICIP22(821-825)
IEEE DOI 2211
Transforms, Feature extraction, Robustness, Image reconstruction, Multi-view stereo, deep learning, 3D reconstruction BibRef

Mi, Z.X.[Zhen-Xing], Di, C.[Chang], Xu, D.[Dan],
Generalized Binary Search Network for Highly-Efficient Multi-View Stereo,
CVPR22(12981-12990)
IEEE DOI 2210
Training, Learning systems, Costs, Codes, Memory management, 3D from multi-view and sensors BibRef

Liu, I.[Isabella], Yang, E.[Edward], Tao, J.Y.[Jian-Yu], Chen, R.[Rui], Zhang, X.S.[Xiao-Shuai], Ran, Q.[Qing], Liu, Z.[Zhu], Su, H.[Hao],
ActiveZero: Mixed Domain Learning for Active Stereovision with Zero Annotation,
CVPR22(13023-13032)
IEEE DOI 2210
Training, Codes, Annotations, Shape, Robustness, Data models, 3D from multi-view and sensors, RGBD sensors and analytics BibRef

Guo, H.Y.[Hao-Yu], Peng, S.[Sida], Lin, H.T.[Hao-Tong], Wang, Q.Q.[Qian-Qian], Zhang, G.F.[Guo-Feng], Bao, H.J.[Hu-Jun], Zhou, X.W.[Xiao-Wei],
Neural 3D Scene Reconstruction with the Manhattan-world Assumption,
CVPR22(5501-5510)
IEEE DOI 2210
Geometry, Semantics, Estimation, Reconstruction algorithms, Robustness, Pattern recognition, 3D from multi-view and sensors BibRef

Usman, B.[Ben], Tagliasacchi, A.[Andrea], Saenko, K.[Kate], Sud, A.[Avneesh],
MetaPose: Fast 3D Pose from Multiple Views without 3D Supervision,
CVPR22(6749-6760)
IEEE DOI 2210
Training, Deep learning, Solid modeling, Uncertainty, Pose estimation, Cameras, Pose estimation and tracking, Deep learning architectures and techniques BibRef

Liu, J.C.[Jia-Chen], Ji, P.[Pan], Bansal, N.[Nitin], Cai, C.J.[Chang-Jiang], Yan, Q.[Qingan], Huang, X.L.[Xiao-Lei], Xu, Y.[Yi],
PlaneMVS: 3D Plane Reconstruction from Multi-View Stereo,
CVPR22(8655-8665)
IEEE DOI 2210
Geometry, Measurement, Semantics, Pipelines, Reconstruction algorithms, 3D from multi-view and sensors, Scene analysis and understanding BibRef

Peng, R.[Rui], Wang, R.J.[Rong-Jie], Wang, Z.Y.[Zhen-Yu], Lai, Y.[Yawen], Wang, R.G.[Rong-Gang],
Rethinking Depth Estimation for Multi-View Stereo: A Unified Representation,
CVPR22(8635-8644)
IEEE DOI 2210
Costs, Image analysis, Estimation, Benchmark testing, Sensors, 3D from multi-view and sensors, Scene analysis and understanding BibRef

Goel, S.[Shubham], Gkioxari, G.[Georgia], Malik, J.[Jitendra],
Differentiable Stereopsis: Meshes from multiple views using differentiable rendering,
CVPR22(8625-8634)
IEEE DOI 2210
Solid modeling, Shape, Statistical analysis, Predictive models, Rendering (computer graphics), Cameras, 3D from multi-view and sensors BibRef

Wang, Y.F.[Yi-Fan], Doersch, C.[Carl], Arandjelovic, R.[Relja], Carreira, J.[João], Zisserman, A.[Andrew],
Input-level Inductive Biases for 3D Reconstruction,
CVPR22(6166-6176)
IEEE DOI 2210
Geometry, Solid modeling, Computational modeling, Estimation, 3D from multi-view and sensors, Deep learning architectures and techniques BibRef

Yang, Z.P.[Zhen-Pei], Ren, Z.[Zhile], Shan, Q.[Qi], Huang, Q.X.[Qi-Xing],
MVS2D: Efficient Multiview Stereo via Attention-Driven 2D Convolutions,
CVPR22(8564-8574)
IEEE DOI 2210
Deep learning, Costs, Codes, Convolution, Estimation, 3D from multi-view and sensors, 3D from single images, Vision applications and systems BibRef

Ding, Y.K.[Yi-Kang], Yuan, W.T.[Wen-Tao], Zhu, Q.T.[Qing-Tian], Zhang, H.[Haotian], Liu, X.Y.[Xiang-Yue], Wang, Y.[Yuanjiang], Liu, X.[Xiao],
TransMVSNet: Global Context-aware Multi-view Stereo Network with Transformers,
CVPR22(8575-8584)
IEEE DOI 2210
Correlation, Aggregates, Benchmark testing, Transformers, Loss measurement, 3D from multi-view and sensors BibRef

Xi, J.[Junhua], Shi, Y.F.[Yi-Fei], Wang, Y.J.[Yi-Jie], Guo, Y.L.[Yu-Lan], Xu, K.[Kai],
RayMVSNet: Learning Ray-based 1D Implicit Fields for Accurate Multi-View Stereo,
CVPR22(8585-8595)
IEEE DOI 2210
Point cloud compression, Costs, Convolution, Optimized production technology, Cameras, Transformers, 3D from multi-view and sensors BibRef

Wang, F.J.[Fang-Jinhua], Galliani, S.[Silvano], Vogel, C.[Christoph], Pollefeys, M.[Marc],
IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo,
CVPR22(8596-8605)
IEEE DOI 2210
Learning systems, Memory management, Estimation, Probability distribution, Sensors, Photogrammetry and remote sensing BibRef

Guizilini, V.[Vitor], Ambrus, R.[Rares], Chen, D.[Dian], Zakharov, S.[Sergey], Gaidon, A.[Adrien],
Multi-Frame Self-Supervised Depth with Transformers,
CVPR22(160-170)
IEEE DOI 2210

WWW Link. Measurement, Training, Costs, Pipelines, Estimation, Transformers, Machine learning, 3D from single images BibRef

Lomurno, E.[Eugenio], Romanoni, A.[Andrea], Matteucci, M.[Matteo],
Improving Multi-View Stereo via Super-Resolution,
CIAP22(II:102-113).
Springer DOI 2205
BibRef

Xu, H.B.[Hong-Bin], Zhou, Z.P.[Zhi-Peng], Wang, Y.[Yali], Kang, W.X.[Wen-Xiong], Sun, B.[Baigui], Li, H.[Hao], Qiao, Y.[Yu],
Digging into Uncertainty in Self-supervised Multi-view Stereo,
ICCV21(6058-6067)
IEEE DOI 2203
Optical losses, Optical filters, Uncertainty, Monte Carlo methods, Benchmark testing, Stereo, 3D from multiview and other sensors, BibRef

Gao, J.[Jian], Liu, J.[Jin], Ji, S.P.[Shun-Ping],
Rational Polynomial Camera Model Warping for Deep Learning Based Satellite Multi-View Stereo Matching,
ICCV21(6128-6137)
IEEE DOI 2203
Earth, Deep learning, Surface reconstruction, Satellites, Tensors, Cameras, Data models, Stereo, 3D from multiview and other sensors, Datasets and evaluation BibRef

Zhang, J.Y.[Jing-Yang], Yao, Y.[Yao], Quan, L.[Long],
Learning Signed Distance Field for Multi-view Surface Reconstruction,
ICCV21(6505-6514)
IEEE DOI 2203
Geometry, Surface cleaning, Surface reconstruction, Network topology, Estimation, Rendering (computer graphics), BibRef

Kang, K.Z.[Kai-Zhang], Xie, C.[Cihui], Zhu, R.S.[Rui-Sheng], Ma, X.H.[Xiao-He], Tan, P.[Ping], Wu, H.Z.[Hong-Zhi], Zhou, K.[Kun],
Learning Efficient Photometric Feature Transform for Multi-view Stereo,
ICCV21(5936-5945)
IEEE DOI 2203
Reflectivity, Multiplexing, Pipelines, Lighting, Scattering, Stereo, 3D from multiview and other sensors, Low-level and physics-based vision BibRef

Kim, T.[Taekyung], Choi, J.[Jaehoon], Choi, S.[Seokeon], Jung, D.[Dongki], Kim, C.[Changick],
Just a Few Points are All You Need for Multi-view Stereo: A Novel Semi-supervised Learning Method for Multi-view Stereo,
ICCV21(6158-6166)
IEEE DOI 2203
Computer network reliability, Semisupervised learning, Propagation losses, Reliability, Stereo, BibRef

Wang, D.[Dan], Cui, X.R.[Xin-Rui], Chen, X.[Xun], Zou, Z.X.[Zheng-Xia], Shi, T.Y.[Tian-Yang], Salcudean, S.[Septimiu], Wang, Z.J.[Z. Jane], Ward, R.[Rabab],
Multi-view 3D Reconstruction with Transformers,
ICCV21(5702-5711)
IEEE DOI 2203
Solid modeling, Codes, Transformer cores, Predictive models, Benchmark testing, Stereo, 3D from multiview and other sensors, Representation learning BibRef

Ma, X.J.[Xin-Jun], Gong, Y.[Yue], Wang, Q.[Qirui], Huang, J.W.[Jing-Wei], Chen, L.[Lei], Yu, F.[Fan],
EPP-MVSNet: Epipolar-assembling based Depth Prediction for Multi-view Stereo,
ICCV21(5712-5720)
IEEE DOI 2203
Learning systems, Costs, Image resolution, Refining, Redundancy, Memory management, Stereo, 3D from multiview and other sensors, Efficient training and inference methods BibRef

Wei, Y.[Yi], Liu, S.H.[Shao-Hui], Rao, Y.M.[Yong-Ming], Zhao, W.[Wang], Lu, J.W.[Ji-Wen], Zhou, J.[Jie],
NerfingMVS: Guided Optimization of Neural Radiance Fields for Indoor Multi-view Stereo,
ICCV21(5590-5599)
IEEE DOI 2203
Codes, Neural networks, Optimization methods, Estimation, Rendering (computer graphics), Indoor environment, Stereo, Image and video synthesis BibRef

Chen, A.[Anpei], Xu, Z.X.[Ze-Xiang], Zhao, F.Q.[Fu-Qiang], Zhang, X.S.[Xiao-Shuai], Xiang, F.[Fanbo], Yu, J.Y.[Jing-Yi], Su, H.[Hao],
MVSNeRF: Fast Generalizable Radiance Field Reconstruction from Multi-View Stereo,
ICCV21(14104-14113)
IEEE DOI 2203
Training, Deep learning, Costs, Neural networks, Rendering (computer graphics), Cognition, 3D from multiview and other sensors BibRef

Lee, J.Y.[Jae Yong], DeGol, J.[Joseph], Zou, C.[Chuhang], Hoiem, D.[Derek],
PatchMatch-RL: Deep MVS with Pixelwise Depth, Normal, and Visibility,
ICCV21(6138-6147)
IEEE DOI 2203
Training, Costs, Estimation, Reinforcement learning, Benchmark testing, Cameras, Stereo, Optimization and learning methods BibRef

Hamdi, A.[Abdullah], Giancola, S.[Silvio], Ghanem, B.[Bernard],
MVTN: Multi-View Transformation Network for 3D Shape Recognition,
ICCV21(1-11)
IEEE DOI 2203
Training, Point cloud compression, Shape, Pipelines, Performance gain, Recognition and classification, 3D from multiview and other sensors BibRef

Darmon, F.[François], Bascle, B.[Bénédicte], Devaux, J.C.[Jean-Clément], Monasse, P.[Pascal], Aubry, M.[Mathieu],
Deep Multi-View Stereo Gone Wild,
3DV21(484-493)
IEEE DOI 2201
Deep learning, Systematics, Art, Training data, Network architecture, Internet, Multi View Stereo, MVS, Deep Learning, Dataset BibRef

Rich, A.[Alexander], Stier, N.[Noah], Sen, P.[Pradeep], Höllerer, T.[Tobias],
3DVNet: Multi-View Depth Prediction and Volumetric Refinement,
3DV21(700-709)
IEEE DOI 2201
Measurement, Point cloud compression, Geometry, Solid modeling, Image segmentation, Estimation, multi view stereo, volumetric reconstruction BibRef

Shrestha, R.[Rakesh], Fan, Z.W.[Zhi-Wen], Su, Q.K.[Qing-Kun], Dai, Z.Z.[Zuo-Zhuo], Zhu, S.[Siyu], Tan, P.[Ping],
MeshMVS: Multi-View Stereo Guided Mesh Reconstruction,
3DV21(1290-1300)
IEEE DOI 2201
Visualization, Solid modeling, Shape, Semantics, Color, Feature extraction, multi-view stereo, 3d reconstruction BibRef

Dabhi, M.[Mosam], Wang, C.Y.[Chao-Yang], Saluja, K.[Kunal], Jeni, L.A.[László A.], Fasel, I.[Ian], Lucey, S.[Simon],
High Fidelity 3D Reconstructions with Limited Physical Views,
3DV21(1301-1311)
IEEE DOI 2201
Shape, Cameras, Calibration, Multi-view geometry, 3D reconstruction, Deep learning, Optimization, 3D lifting, Neural Prior BibRef

Stathopoulou, E.K., Rigon, S., Battisti, R., Remondino, F.,
Enhancing Geometric Edge Details in MVS Reconstruction,
ISPRS21(B2-2021: 391-398).
DOI Link 2201
BibRef

Huang, B.C.[Bai-Chuan], Yi, H.W.[Hong-Wei], Huang, C.[Can], He, Y.J.[Yi-Jia], Liu, J.B.[Jing-Bin], Liu, X.[Xiao],
M3VSNET: Unsupervised Multi-Metric Multi-View Stereo Network,
ICIP21(3163-3167)
IEEE DOI 2201
Training, Benchmark testing, Robustness, Image reconstruction, Multi-view stereo, unsupervised, multi-metric, depth map BibRef

Gray, J.L.[James L.], Naman, A.T.[Aous T.], Taubman, D.S.[David S.],
Welsch Based Multiview Disparity Estimation,
ICIP21(3223-3227)
IEEE DOI 2201
Degradation, Image processing, Estimation, Light fields, High frequency, Disparity Estimation, Multiview, Welsch loss BibRef

Acharya, P.[Pranav], Lohn, D.[Daniel], Ross, V.[Vivian], Ha, M.[Maya], Rich, A.[Alexander], Sayyad, E.[Ehsan], Höllerer, T.[Tobias],
Using Synthetic Data Generation to Probe Multi-View Stereo Networks,
ILDAV21(1583-1591)
IEEE DOI 2112
Training, Solid modeling, Neural networks, Tools, Cameras, Image sequences BibRef

Düzçeker, A.[Arda], Galliani, S.[Silvano], Vogel, C.[Christoph], Speciale, P.[Pablo], Dusmanu, M.[Mihai], Pollefeys, M.[Marc],
DeepVideoMVS: Multi-View Stereo on Video with Recurrent Spatio-Temporal Fusion,
CVPR21(15319-15328)
IEEE DOI 2111
Measurement, Geometry, Image coding, Costs, Memory management, Transforms, Streaming media BibRef

Wang, F.J.[Fang-Jinhua], Galliani, S.[Silvano], Vogel, C.[Christoph], Speciale, P.[Pablo], Pollefeys, M.[Marc],
PatchmatchNet: Learned Multi-View Patchmatch Stereo,
CVPR21(14189-14198)
IEEE DOI 2111
Performance evaluation, Learning systems, Costs, Image resolution, Head, Memory management BibRef

Cheng, Z.[Ziang], Li, H.D.[Hong-Dong], Asano, Y.[Yuta], Zheng, Y.Q.[Yin-Qiang], Sato, I.[Imari],
Multi-view 3D Reconstruction of a Texture-less Smooth Surface of Unknown Generic Reflectance,
CVPR21(16221-16230)
IEEE DOI 2111
Reflectivity, Geometry, Surface reconstruction, Shape, Minimization, Surface texture BibRef

Khan, N.[Numair], Kim, M.H.[Min H.], Tompkin, J.[James],
Differentiable Diffusion for Dense Depth Estimation from Multi-view Images,
CVPR21(8908-8917)
IEEE DOI 2111
Deep learning, Analytical models, Estimation, Rendering (computer graphics), Propagation losses, Light fields BibRef

Oestreich, C.[Charles], Espinoza, A.T.[Antonio Terán], Todd, J.[Jessica], Albee, K.[Keenan], Linares, R.[Richard],
On-Orbit Inspection of an Unknown, Tumbling Target using NASA's Astrobee Robotic Free-Flyers,
AI4Space21(2039-2047)
IEEE DOI 2109
Space vehicles, Heuristic algorithms, Robot vision systems, Inspection, Extraterrestrial measurements, International Space Station BibRef

Fujitomi, T.[Taku], Ito, S.[Seiya], Kaneko, N.[Naoshi], Sumi, K.[Kazuhiko],
Bi-directional Recurrent MVSNet for High-resolution Multi-view Stereo,
MVA21(1-5)
DOI Link 2109
Aggregates, Memory management, Bidirectional control, Benchmark testing, Network architecture BibRef

Zhang, X.D.[Xu-Dong], Hu, Y.[Yutao], Wang, H.[Haochen], Cao, X.B.[Xian-Bin], Zhang, B.C.[Bao-Chang],
Long-range Attention Network for Multi-View Stereo,
WACV21(3781-3790)
IEEE DOI 2106
Geometry, Aggregates, Benchmark testing, Robustness, Image reconstruction BibRef

Doi, T.[Takuma], Okura, F.[Fumio], Nagahara, T.[Toshiki], Matsushita, Y.[Yasuyuki], Yagi, Y.S.[Yasu-Shi],
Descriptor-free Multi-view Region Matching for Instance-wise 3d Reconstruction,
ACCV20(V:581-599).
Springer DOI 2103
BibRef

Weilharter, R.[Rafael], Fraundorfer, F.[Friedrich],
ATLAS-MVSNet: Attention Layers for Feature Extraction and Cost Volume Regularization in Multi-View Stereo,
ICPR22(3557-3563)
IEEE DOI 2212
Training, Costs, Memory management, Neural networks, Graphics processing units, Deep architecture BibRef

Sormann, C., Knöbelreiter, P., Kuhn, A., Rossi, M., Pock, T., Fraundorfer, F.,
BP-MVSNet: Belief-Propagation-Layers for Multi-View-Stereo,
3DV20(394-403)
IEEE DOI 2102
Feature extraction, Memory management, Mathematical model, Belief propagation, Tensors, depth estimation BibRef

Cassidy, M., Mélou, J., Quéau, Y., Lauze, F., Durou, J.D.,
Refractive Multi-view Stereo,
3DV20(384-393)
IEEE DOI 2102
Cameras, Shape, Optical refraction, Optical imaging, Geometry, Refractive index, Image reconstruction, 3Dreconstruction, Refraction BibRef

Nietiedt, S., Kalinowski, P., Hastedt, H., Luhmann, T.,
Accuracy Investigations of Image Matching Techniques By Means of A Textured Dumbbell Artefact,
ISPRS20(B2:791-796).
DOI Link 2012
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Mat Amin, M.A., Abdullah, S., Abdul fMukti, S.N., Mohd Zaidi, M.H.A., Tahar, K.N.,
Reconstruction of 3D Accident Scene From Multirotor UAV Platform,
ISPRS20(B2:451-458).
DOI Link 2012
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Lanz, O., Sottsas, F., Conni, M., Boschetti, M., Nocerino, E., Menna, F., Remondino, F.,
A Versatile Multi-camera System for 3d Acquisition and Modeling,
ISPRS20(B2:785-790).
DOI Link 2012
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Zhu, X., Pang, G., Chen, C.,
Research and Application of Historical 3D Modeling Based on Archived Non-oblique Aerial Photos,
ISPRS20(B2:555-560).
DOI Link 2012
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Chen, J.X.[Jia-Xin], Qin, J.[Jie], Shen, Y.M.[Yu-Ming], Liu, L.[Li], Zhu, F.[Fan], Shao, L.[Ling],
Learning Attentive and Hierarchical Representations for 3D Shape Recognition,
ECCV20(XV:105-122).
Springer DOI 2011
Hyperbolic Embedded Attentive Representation (HEAR). BibRef

Wei, Z.Z.[Zi-Zhuang], Zhu, Q.T.[Qing-Tian], Min, C.[Chen], Chen, Y.S.[Yi-Song], Wang, G.P.[Guo-Ping],
AA-RMVSNet: Adaptive Aggregation Recurrent Multi-view Stereo Network,
ICCV21(6167-6176)
IEEE DOI 2203
Costs, Adaptive systems, Convolution, Scalability, Benchmark testing, Feature extraction, Stereo, 3D from multiview and other sensors, Vision for robotics and autonomous vehicles BibRef

Yi, H.W.[Hong-Wei], Wei, Z.Z.[Zi-Zhuang], Ding, M.Y.[Ming-Yu], Zhang, R.Z.[Run-Ze], Chen, Y.S.[Yi-Song], Wang, G.P.[Guo-Ping], Tai, Y.W.[Yu-Wing],
Pyramid Multi-view Stereo Net with Self-adaptive View Aggregation,
ECCV20(IX:766-782).
Springer DOI 2011
BibRef

Yan, J.F.[Jian-Feng], Wei, Z.Z.[Zi-Zhuang], Yi, H.W.[Hong-Wei], Ding, M.Y.[Ming-Yu], Zhang, R.Z.[Run-Ze], Chen, Y.S.[Yi-Song], Wang, G.P.[Guo-Ping], Tai, Y.W.[Yu-Wing],
Dense Hybrid Recurrent Multi-view Stereo Net with Dynamic Consistency Checking,
ECCV20(IV:674-689).
Springer DOI 2011
BibRef

Chen, D.[Di], Wu, Y.[Yi],
Exploring the Properties of Points Generation Network,
ICIVC20(272-277)
IEEE DOI 2009
Image reconstruction, Shape, Training, Machine learning, Market research, Telephone sets, point cloud generation BibRef

Luo, K., Guan, T., Ju, L., Wang, Y., Chen, Z., Luo, Y.,
Attention-Aware Multi-View Stereo,
CVPR20(1587-1596)
IEEE DOI 2008
Feature extraction, Robustness, Measurement, Surface reconstruction, Task analysis, Estimation BibRef

Mustikovela, S.K.[Siva Karthik], Jampani, V.[Varun], de Mello, S.[Shalini], Liu, S.F.[Si-Fei], Iqbal, U.[Umar], Rother, C.[Carsten], Kautz, J.[Jan],
Self-Supervised Viewpoint Learning From Image Collections,
CVPR20(3970-3980)
IEEE DOI 2008
Estimation, Automobiles, Head, Training, Image reconstruction BibRef

Navaneet, K.L., Mathew, A.[Ansu], Kashyap, S.[Shashank], Hung, W.C.[Wei-Chih], Jampani, V.[Varun], Babu, R.V.[R. Venkatesh],
From Image Collections to Point Clouds With Self-Supervised Shape and Pose Networks,
CVPR20(1129-1137)
IEEE DOI 2008
Image reconstruction, Solid modeling, Training, Cameras BibRef

Innmann, M., Kim, K., Gu, J., Nießner, M., Loop, C., Stamminger, M., Kautz, J.,
NRMVS: Non-Rigid Multi-View Stereo,
WACV20(2743-2752)
IEEE DOI 2006
Strain, Image reconstruction, Cameras, Surface reconstruction, Optimization, Dynamics BibRef

Xu, X.Y.[Xiang-Yu], Dunn, E.[Enrique],
Discrete Laplace Operator Estimation for Dynamic 3D Reconstruction,
ICCV19(1548-1557)
IEEE DOI 2004
From multiple, diverse imagery. convex programming, geometry, graph theory, image motion analysis, image reconstruction, image segmentation, Optimization BibRef

Esteves, C., Xu, Y., Allec-Blanchette, C., Daniilidis, K.,
Equivariant Multi-View Networks,
ICCV19(1568-1577)
IEEE DOI 2004
convolutional neural nets, group theory, image classification, image representation, image retrieval, Solid modeling BibRef

Hou, Y., Kannala, J., Solin, A.,
Multi-View Stereo by Temporal Nonparametric Fusion,
ICCV19(2651-2660)
IEEE DOI 2004
Gaussian processes, image coding, image fusion, pose estimation, stereo image processing, scaling Gaussian process inference, Encoding BibRef

Xie, H., Yao, H., Sun, X., Zhou, S., Zhang, S.,
Pix2Vox: Context-Aware 3D Reconstruction From Single and Multi-View Images,
ICCV19(2690-2698)
IEEE DOI 2004
feature extraction, image coding, image colour analysis, image fusion, image reconstruction, image representation, Kernel BibRef

Gupta, K., Jabbireddy, S., Shah, K., Shrivastava, A., Zwicker, M.,
Improved Modeling of 3D Shapes with Multi-view Depth Maps,
3DV20(71-80)
IEEE DOI 2102
Shape, Solid modeling, Convolution, Generators, Image reconstruction, reconstruction BibRef

Hu, T.[Tao], Lin, G.[Geng], Han, Z.Z.[Zhi-Zhong], Zwicker, M.[Matthias],
Learning to Generate Dense Point Clouds with Textures on Multiple Categories,
WACV21(2169-2178)
IEEE DOI 2106
Geometry, Shape, Pipelines, Reconstruction algorithms, Topology BibRef

Hu, T.[Tao], Han, Z.Z.[Zhi-Zhong], Shrivastava, A., Zwicker, M.[Matthias],
Render4Completion: Synthesizing Multi-View Depth Maps for 3D Shape Completion,
GMDL19(4114-4122)
IEEE DOI 2004
image representation, rendering (computer graphics), render4completion, volumetric representations, point clouds, 3D vision BibRef

Jaritz, M., Gu, J., Su, H.,
Multi-View PointNet for 3D Scene Understanding,
GMDL19(3995-4003)
IEEE DOI 2004
feature extraction, image fusion, image reconstruction, image representation, image segmentation, solid modelling, Point Cloud BibRef

Lee, M., Um, G., Yun, J., Cheong, W., Park, S.,
Disparity Refinement with Guided Filtering of Soft 3D Cost Function in Multi-view Stereo System,
IVCNZ19(1-5)
IEEE DOI 2004
edge detection, Gaussian processes, image colour analysis, image filtering, image matching, image reconstruction, probability, Guide filter BibRef

Xue, Y., Chen, J., Wan, W., Huang, Y., Yu, C., Li, T., Bao, J.,
MVSCRF: Learning Multi-View Stereo With Conditional Random Fields,
ICCV19(4311-4320)
IEEE DOI 2004
learning (artificial intelligence), random processes, recurrent neural nets, stereo image processing, MVSCRF, Semantics BibRef

Romanoni, A., Matteucci, M.,
TAPA-MVS: Textureless-Aware PAtchMatch Multi-View Stereo,
ICCV19(10412-10421)
IEEE DOI 2004
image filtering, image fusion, image matching, image reconstruction, image resolution, image texture, Image edge detection BibRef

Luo, K., Guan, T., Ju, L., Huang, H., Luo, Y.,
P-MVSNet: Learning Patch-Wise Matching Confidence Aggregation for Multi-View Stereo,
ICCV19(10451-10460)
IEEE DOI 2004
convolutional neural nets, feature extraction, image matching, image reconstruction, learning (artificial intelligence), Surface texture BibRef

Xu, Q.S.[Qing-Shan], Tao, W.B.[Wen-Bing],
Multi-Scale Geometric Consistency Guided Multi-View Stereo,
CVPR19(5478-5487).
IEEE DOI 2002
BibRef

Fuse, T., Ikezawa, H.,
Development of Multi-view Stereo Considering Accuracy of Exterior Orientation Elements,
Optical3D19(47-52).
DOI Link 1912
BibRef

Tatar, N., Arefi, H.,
Evaluation of Selected Cost Aggregation Methods On High Resolution Satellite Stereo Images,
SMPR19(1045-1050).
DOI Link 1912
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Mahphood, A., Arefi, H., Hosseininaveh, A., Naeini, A.A.,
Dense Multi-view Image Matching for DSM Generation From Satellite Images,
SMPR19(709-715).
DOI Link 1912
BibRef

Stathopoulou, E.K., Remondino, F.,
Multi-view Stereo With Semantic Priors,
CIPA19(1135-1140).
DOI Link 1912
BibRef

Wang, S.Q.[Shao-Qian], Li, B.[Bo], Dai, Y.C.[Yu-Chao],
Efficient Multi-view Stereo by Iterative Dynamic Cost Volume,
CVPR22(8645-8654)
IEEE DOI 2210
Costs, Image resolution, Convolution, Feature extraction, Sensors, 3D from multi-view and sensors BibRef

Dai, Y.C.[Yu-Chao], Zhu, Z.D.[Zhi-Dong], Rao, Z.B.[Zhi-Bo], Li, B.[Bo],
MVS2: Deep Unsupervised Multi-View Stereo with Multi-View Symmetry,
3DV19(1-8)
IEEE DOI 1911
Geometry, Measurement, Image reconstruction, Feature extraction, Training, Estimation, unsupervised learning BibRef

Armando, M., Franco, J., Boyer, E.,
Adaptive Mesh Texture for Multi-View Appearance Modeling,
3DV19(700-708)
IEEE DOI 1911
Solid modeling, Shape, Image coding, Image color analysis, Multi view stereo BibRef

Kuhn, A.[Andreas], Lin, S.[Shan], Erdler, O.[Oliver],
Plane Completion and Filtering for Multi-View Stereo Reconstruction,
GCPR19(18-32).
Springer DOI 1911
BibRef

Mao, W.D.[Wen-Dong], Gong, M.L.[Ming-Lun], Huang, X.[Xin], Cai, H.[Hao], Yi, Z.L.[Zi-Li],
A Global-Matching Framework for Multi-View Stereopsis,
CAIP19(I:635-647).
Springer DOI 1909
BibRef

Truong, T.P.[Trong Phuc], Nozick, V.[Vincent], Saito, H.[Hideo],
Semi-independent Stereo Visual Odometry for Different Field of View Cameras,
3D-Wild18(I:430-442).
Springer DOI 1905
BibRef

Paschalidou, D.[Despoina], Ulusoy, A.O.[Ali Osman], Schmitt, C.[Carolin], van Gool, L.J.[Luc J.], Geiger, A.[Andreas],
RayNet: Learning Volumetric 3D Reconstruction with Ray Potentials,
CVPR18(3897-3906)
IEEE DOI 1812
Image reconstruction, Cameras, Solid modeling, Feature extraction, Geometry BibRef

Choi, S., Kim, S., park, K., Sohn, K.,
Learning Descriptor, Confidence, and Depth Estimation in Multi-view Stereo,
DeepSLAM18(389-3896)
IEEE DOI 1812
Estimation, Feature extraction, Training, Task analysis, Cameras, Reliability BibRef

Poms, A., Wu, C., Yu, S., Sheikh, Y.,
Learning Patch Reconstructability for Accelerating Multi-view Stereo,
CVPR18(3041-3050)
IEEE DOI 1812
Image reconstruction, Surface reconstruction, Acceleration, Pipelines, Neural networks BibRef

Huang, P., Matzen, K., Kopf, J., Ahuja, N., Huang, J.,
DeepMVS: Learning Multi-view Stereopsis,
CVPR18(2821-2830)
IEEE DOI 1812
Image reconstruction, Feature extraction, Decoding, Cameras, Training, Calibration, Semantics BibRef

Wang, K., Shen, S.,
MVDepthNet: Real-Time Multiview Depth Estimation Neural Network,
3DV18(248-257)
IEEE DOI 1812
image motion analysis, neural nets, stereo image processing, MVDepthNet, Structure from motion and SLAM BibRef

Yao, Y.[Yao], Luo, Z.[Zixin], Li, S.[Shiwei], Shen, T.W.[Tian-Wei], Fang, T.[Tian], Quan, L.[Long],
Recurrent MVSNet for High-Resolution Multi-View Stereo Depth Inference,
CVPR19(5520-5529).
IEEE DOI 2002
BibRef
Earlier: A1, A2, A3, A5, A6, Only:
MVSNet: Depth Inference for Unstructured Multi-view Stereo,
ECCV18(VIII: 785-801).
Springer DOI 1810
BibRef

Yodokawa, K., Ito, K., Aoki, T., Sakai, S., Watanabe, T., Masuda, T.,
Outlier and Artifact Removal Filters for Multi-View Stereo,
ICIP18(3638-3642)
IEEE DOI 1809
Image reconstruction, Cats, Dogs, Image color analysis, Shape, Cameras, 3D reconstruction, depth map, multi-view stereo BibRef

Engler, T., Wuensche, H.J.,
Recursive 3D scene estimation with multiple camera pairs,
IPTA17(1-6)
IEEE DOI 1804
cameras, recursive estimation, stereo image processing, camera positions, cameras disparities, Stereo Vision BibRef

Kaczmarek, A.L.,
Stereo camera upgraded to equal baseline multiple camera set (EBMCS),
3DTV-CON17(1-4)
IEEE DOI 1804
cameras, image sensors, optical radar, stereo image processing, 3D images, EBMCS, LIDAR, camera array, camera matrix, depth maps, stereo matching algorithm BibRef

Tian, H., Li, F.,
Accurate multi-view stereo by selective expansion,
3DTV-CON17(1-4)
IEEE DOI 1804
image reconstruction, stereo image processing, dense modeling, depth refinement process, multiview stereo method, selective expansion BibRef

Ebner, T., Schreer, O., Feldmann, I.,
Fully automated highly accurate 3D reconstruction from multiple views,
ICIP17(2528-2532)
IEEE DOI 1803
Cameras, Estimation, Image reconstruction, Pipelines, Solid modeling, Surface reconstruction, 3D modelling, surface reconstruction BibRef

Ji, M., Gall, J., Zheng, H., Liu, Y., Fang, L.,
SurfaceNet: An End-to-End 3D Neural Network for Multiview Stereopsis,
ICCV17(2326-2334)
IEEE DOI 1802
cameras, image representation, learning (artificial intelligence), neural nets, Training BibRef

Hartmann, W.[Wilfried], Galliani, S.[Silvano], Havlena, M.[Michal], Van Gool, L.J.[Luc J.], Schindler, K.[Konrad],
Learned Multi-patch Similarity,
ICCV17(1595-1603)
IEEE DOI 1802
Depth map from multiple views. convolution, image matching, image reconstruction, learning (artificial intelligence), neural nets, solid modelling, BibRef

Ji, P., Li, H., Dai, Y., Reid, I.D.[Ian D.],
Maximizing Rigidity Revisited: A Convex Programming Approach for Generic 3D Shape Reconstruction from Multiple Perspective Views,
ICCV17(929-937)
IEEE DOI 1802
convex programming, image motion analysis, image reconstruction, pose estimation, 3D reconstruction problems, 3D scene structure, BibRef

Schöps, T.[Thomas], Schönberger, J.L.[Johannes L.], Galliani, S.[Silvano], Sattler, T.[Torsten], Schindler, K.[Konrad], Pollefeys, M.[Marc], Geiger, A.[Andreas],
A Multi-view Stereo Benchmark with High-Resolution Images and Multi-camera Videos,
CVPR17(2538-2547)
IEEE DOI 1711
Benchmark testing, Cameras, Image resolution, Lasers, Robot vision systems, Videos BibRef

Bourki, A.[Amine], de La Gorce, M.[Martin], Marlet, R.[Renaud], Komodakis, N.[Nikos],
Patchwork Stereo: Scalable, Structure-Aware 3D Reconstruction in Man-Made Environments,
WACV17(292-301)
IEEE DOI 1609
Calibration, Feature extraction, Image reconstruction, Image segmentation, Robustness, Two, dimensional, displays BibRef

Loop, C.[Charles], Cai, Q.[Qin], Orts-Escolano, S.[Sergio], Chou, P.A.[Philip A.],
A Closed-Form Bayesian Fusion Equation Using Occupancy Probabilities,
3DV16(380-388)
IEEE DOI 1701
Bayes methods BibRef

Locher, A.[Alex], Havlena, M.[Michal], Van Gool, L.J.[Luc J.],
Progressive 3D Modeling All the Way,
3DV16(11-18)
IEEE DOI 1701
image motion analysis BibRef

Savinov, N.[Nikolay], Häne, C.[Christian], Ladický, L.[L'ubor], Pollefeys, M.[Marc],
Semantic 3D Reconstruction with Continuous Regularization and Ray Potentials Using a Visibility Consistency Constraint,
CVPR16(5460-5469)
IEEE DOI 1612
BibRef

Ulusoy, A.O.[Ali Osman], Black, M.J.[Michael J.], Geiger, A.[Andreas],
Patches, Planes and Probabilities: A Non-Local Prior for Volumetric 3D Reconstruction,
CVPR16(3280-3289)
IEEE DOI 1612
BibRef

Langguth, F.[Fabian], Sunkavalli, K.[Kalyan], Hadap, S.I.[Sun-Il], Goesele, M.[Michael],
Shading-Aware Multi-view Stereo,
ECCV16(III: 469-485).
Springer DOI 1611
BibRef

Bignoli, A., Romanoni, A., Matteucci, M., di Milano, P.,
Multi-view Stereo 3D Edge Reconstruction,
WACV18(867-875)
IEEE DOI 1806
graph theory, image reconstruction, image sequences, stereo image processing, EdgeGraph3D, Video sequences BibRef

Romanoni, A., Matteucci, M.,
A Data-Driven Prior on Facet Orientation for Semantic Mesh Labeling,
3DV18(662-671)
IEEE DOI 1812
image classification, image segmentation, Markov processes, mesh generation, Markov random field, orientation prior BibRef

Romanoni, A.[Andrea], Matteucci, M.[Matteo],
Facetwise Mesh Refinement for Multi- View Stereo,
ICPR21(6794-6801)
IEEE DOI 2105
Manifolds, Geometry, Pipelines, Maintenance engineering, Cameras, Cognition BibRef

Romanoni, A., Ciccone, M., Visin, F., Matteucci, M.,
Multi-view Stereo with Single-View Semantic Mesh Refinement,
3DSemantics17(706-715)
IEEE DOI 1802
Image reconstruction, Image segmentation, Labeling, Semantics, Solid modeling, BibRef

Romanoni, A., Delaunoy, A., Pollefeys, M., Matteucci, M.,
Automatic 3D reconstruction of manifold meshes via delaunay triangulation and mesh sweeping,
WACV16(1-8)
IEEE DOI 1606
Cameras BibRef

Fujiwara, K.[Kent], Mori, M.[Minoru], Kashino, K.[Kunio],
Unsupervised categorical shape reconstruction through manifolds,
WACV16(1-8)
IEEE DOI 1606
Cameras. Without manual point correspondence. BibRef

Tron, R., Zhou, X.W.[Xiao-Wei], Esteves, C., Daniilidis, K.[Kostas],
Fast Multi-image Matching via Density-Based Clustering,
ICCV17(4077-4086)
IEEE DOI 1802
convex programming, feature extraction, image matching, iterative methods, pattern clustering, QuickMatch, Graph theory BibRef

Zhou, X.W.[Xiao-Wei], Zhu, M.L.[Meng-Long], Daniilidis, K.[Kostas],
Multi-image Matching via Fast Alternating Minimization,
ICCV15(4032-4040)
IEEE DOI 1602
Computational modeling BibRef

Zivanov, J.[Jasenko], Vetter, T.[Thomas],
Multiview Reconstruction of Complex Organic Shapes,
BMVC15(xx-yy).
DOI Link 1601
narrow baseline multiview stereo BibRef

Suominen, O.[Olli], Gotchev, A.[Atanas],
Efficient cost volume sampling for plane sweeping based multiview depth estimation,
ICIP15(1075-1079)
IEEE DOI 1512
Depth estimation; cost volume; multi-view; plane sweeping BibRef

Schöning, J.[Julius], Heidemann, G.[Gunther],
Evaluation of Multi-view 3D Reconstruction Software,
CAIP15(II:450-461).
Springer DOI 1511
Evaluation, Multiview. BibRef

Pistellato, M.[Mara], Bergamasco, F.[Filippo], Albarelli, A.[Andrea], Torsello, A.[Andrea],
Dynamic Optimal Path Selection for 3D Triangulation with Multiple Cameras,
CIAP15(I:468-479).
Springer DOI 1511
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Vlasov, V.V.[Vitaly V.], Konovalov, A.B.[Alexander B.], Uglov, A.S.[Alexander S.],
Few-Views Image Reconstruction with SMART and an Allowance for Contrast Structure Shadows,
CAIP15(I:667-677).
Springer DOI 1511
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Chhatkuli, S., Satoh, T., Tachibana, K.,
Multi Sensor Data Integration for an Accurate 3D Model Generation,
Seamless15(103-106).
DOI Link 1508
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Xiong, J., Zhong, S., Zheng, L.,
An automatic 3D Reconstruction Method Based on Multi-View Stereo Vision for the Mogao Grottoes,
Seamless15(171-176).
DOI Link 1508
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Oki, Y.[Yoshinori], Visentini-Scarzanella, M.[Marco], Wada, T.[Tomohito], Furukawa, R.[Ryo], Sagawa, R.[Ryusuke], Kawasaki, H.[Hiroshi],
Entire shape scan system with multiple pro-cams using texture information and accurate silhouette creating technique,
MVA15(18-21)
IEEE DOI 1507
Accuracy BibRef

Hafner, D.[David], Schroers, C.[Christopher], Weickert, J.[Joachim],
Introducing Maximal Anisotropy into Second Order Coupling Models,
GCPR15(79-90).
Springer DOI 1511
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Earlier: A2, A1, A3:
Multiview Depth Parameterisation with Second Order Regularisation,
SSVM15(551-562).
Springer DOI 1506
BibRef

Cui, Z., Gu, J., Shi, B., Tan, P., Kautz, J.,
Polarimetric Multi-view Stereo,
CVPR17(369-378)
IEEE DOI 1711
Azimuth, Cameras, Estimation, Lighting, Shape, Surface reconstruction BibRef

Doron, Y.[Yotam], Campbell, N.D.F.[Neill D. F.], Starck, J.[Jonathan], Kautz, J.[Jan],
User Directed Multi-view-stereo,
UCCV14(299-313).
Springer DOI 1504
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Galindo, P.[Patricio], Zayer, R.[Rhaleb],
Distortion Driven Variational Multi-view Reconstruction,
3DV14(649-656)
IEEE DOI 1503
Area measurement BibRef

Uh, Y.J.[Young-Jung], Matsushita, Y.[Yasuyuki], Byun, H.R.[Hye-Ran],
Efficient Multiview Stereo by Random-Search and Propagation,
3DV14(393-400)
IEEE DOI 1503
Accuracy BibRef

Chen, W.F.[Wei-Feng], Zhang, G.F.[Guo-Feng], Xiang, X.J.[Xiao-Jun], Jia, J.Y.[Jia-Ya], Bao, H.J.[Hu-Jun],
High-Quality Depth Recovery via Interactive Multi-view Stereo,
3DV14(329-336)
IEEE DOI 1503
Cameras BibRef

Raj, R.G.[Raghu G.], Bovik, A.C.[Alan C.],
A hierarchical Bayesian-map approach to computational imaging,
ICIP14(1337-1341)
IEEE DOI 1502
Bayes methods BibRef

Ismael, M.[Muhannad], Prevost, S.[Stephanie], Loscos, C.[Celine], Remion, Y.[Yannick],
Materiality maps: A novel scene-based framework for direct multi-view stereovision reconstruction,
ICIP14(5467-5471)
IEEE DOI 1502
Cameras BibRef

Max, N.[Nelson], Kim, H.[Hyojin],
Optimization of Plane Fits to Image Segments in Multi-view Stereo,
WACV15(1130-1136)
IEEE DOI 1503
Cost function BibRef

Kim, H.[Hyojin], Xiao, H.[Hong], Max, N.[Nelson],
Piecewise Planar Scene Reconstruction and Optimization for Multi-view Stereo,
ACCV12(IV:191-204).
Springer DOI 1304
BibRef

Yamao, S.[Sosuke], Miura, M.[Mamoru], Sakai, S.[Shuji], Ito, K.[Koichi], Aoki, T.[Takafumi],
A Sequential Online 3D Reconstruction System Using Dense Stereo Matching,
WACV15(341-348)
IEEE DOI 1503
Accuracy BibRef

Svarm, L.[Linus], Simayijiang, Z.[Zhayida], Enqvist, O.[Olof], Olsson, C.[Carl],
Point track creation in unordered image collections using Gomory-Hu trees,
ICPR12(2116-2119).
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multi-view stereo reconstructions BibRef

Fukushi, M.[Masaru], Sekiguchi, N.[Naoki], Abe, T.[Toru],
Parallelization of voxel based multiview stereo for arbitrarily configured viewpoints,
ICIP12(1701-1704).
IEEE DOI 1302
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Xu, M.[Ming], Ren, J.[Jie], Chen, D.Y.[Dong-Yong], Smith, J.S.[Jeremy S.], Wang, G.F.[Gui-Fen],
Real-time detection via homography mapping of foreground polygons from multiple cameras,
ICIP11(3593-3596).
IEEE DOI 1201
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Ren, J.[Jie], Xu, M.[Ming], Smith, J.S.[Jeremy S.],
Pruning phantom detections from multiview foreground intersection,
ICIP12(1025-1028).
IEEE DOI 1302
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Teney, D.[Damien], Piater, J.[Justus],
Sampling-Based Multiview Reconstruction without Correspondences for 3D Edges,
3DIMPVT12(160-167).
IEEE DOI 1212
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Hu, X.Y.[Xiao-Yan], Mordohai, P.[Philippos],
Least Commitment, Viewpoint-Based, Multi-view Stereo,
3DIMPVT12(531-538).
IEEE DOI 1212
BibRef

Dai, Z.J.[Zhi-Jun], Wu, Y.H.[Yi-Hong], Zhang, F.J.[Feng-Jun], Wang, H.A.[Hong-An],
A Novel Fast Method for L-inf Problems in Multiview Geometry,
ECCV12(V: 116-129).
Springer DOI 1210
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Asymmetrical Occlusion Handling Using Graph Cut for Multi-View Stereo,
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IEEE DOI 0408
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Weighted Minimal Hypersurfaces and Their Applications in Computer Vision,
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Joint 3D-reconstruction and background separation in multiple views using graph cuts,
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Shu, C.[Chang], Roth, G.,
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An Automatic Multi-view Approach to Planar Chain Matching,
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Wong, K.Y.K.[Kwan-Yee K.], Mendonça, P.R.S.[Paulo R.S.], Cipolla, R.[Roberto],
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Agrawal, M., Davis, L.S.,
Window-based, discontinuity preserving stereo,
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Using the expectation-maximization algorithm for depth estimation and segmentation of multi-view images,
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Dyer, C.R.[Charles R.],
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Irani, M., Anandan, P., Weinshall, D.,
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Peleg, S.[Shmuel], Shashua, A.[Amnon], Weinshall, D.[Daphna], Werman, M.[Michael], Irani, M.[Michal],
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Maruya, M., Nemoto, K., Takashima, Y.,
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Chapter on Stereo: Three Dimensional Descriptions from Two or More Views, Binocular, Trinocular continues in
Multi-View Object Detection, Object Extraction .