Date | Method | Res. | Reference |
---|---|---|---|

02/05/19 | AMNet | Q | Anonymous. Foreground-background-aware atrous multiscale network for disparity estimation. CVPR 2019 submission 6368. |

01/17/19 | FASW | Q | Anonymous. Stereo matching with fusing adaptive support weights. Submitted to IEEE ACCESS, 2019. |

01/16/19 | 3DFMR | H | Anonymous. 3D fill with minimum spanning trees by rolling filter for stereo matching. ISMAR 2019 submission. |

01/12/19 | EHCI_net | H | Run Wang. An end to end network for stereo matching using exploiting hierarchical context information. Master's thesis, HUST, 2019. |

12/18/18 | MCV-MFC | H | Z. Liang, Y. Guo, Y. Feng, Y. Lei, Q. Wang, and X. Chen. Stereo matching using multi-level cost volume and multi-scale feature constancy. Submitted to PAMI 2019. |

11/28/18 | MSFNetA | H | K.-R. Kim, Y. J. Koh, and C.-S. Kim. Multiscale feature extractors for stereo matching cost computation. IEEE Access (Vol. 6), 2018. |

11/15/18 | VN | H | Anonymous. Collaborative disparity map denoising. CVPR 2019 submission 2247. |

11/13/18 | WDMC | H | Anonymous. Matching cost network without using stereo data. CVPR 2019 submission 5913. |

11/11/18 | MBM | H | Q Chang and T Maruyama. Real-time stereo vision system: a multi-block matching on GPU. IEEE Access, vol. 6, 2018. |

11/08/18 | HSM | F | Anonymous. Real-time on-demand deep stereo matching on high-resolution images. CVPR 2019 submission 2471. |

11/07/18 | IEBIMst | H | C. He, C. Zhang, Z. Chen, and S. Jiang. Minimum spanning tree based stereo matching using image edge and brightness information. CISP-BMEI 2017. |

10/29/18 | iResNet | H | Anonymous. Learning for disparity estimation through feature constancy. CVPR 2019 submission 2403. |

10/29/18 | Dense-CNN | H | Anonymous. Dense-CNN: Dense convolutional neural network for stereo matching with feature connected modules. Submitted to Neurocomputing 2018. |

10/10/18 | DISCO | H | Anonymous. DISCO: Depth inference from stereo using context. ICME 2019 submission 3. |

07/31/18 | MotionStereo | H | Anonymous. Depth from motion for smartphone AR. SIGGRAPH Asia 2018 submission 242. |

06/27/18 | DCNN | H | Anonymous. Semi-dense stereo matching using dual CNNs. WACV 2019 submission 385. |

06/14/18 | MSMD_ROB | Q | Anonymous. Cascaded multi-scale and multi-dimension convolutional neural network for stereo matching. PRICAI 2018 submission 41. |

06/05/18 | CBMBNet | H | C Wu. CBMB: A crop-based multi-branch network for matching cost computation. Bachelors thesis, Fuzhou University, 2018. |

06/01/18 | PDISCO_ROB | H | Anonymous. Depth inference from stereo image pair using stacked network based refinement. ROB 2018 submission. |

06/01/18 | NaN_ROB | H | Anonymous. A hybrid pipeline for robust stereo disparity computation. ROB 2018 submission. |

06/01/18 | DPSimNet_ROB | F | Anonymous. Robust stereo matching with dot product similarity. ROB 2018 submission. |

05/31/18 | CBMV_ROB | H | K. Batsos, C. Cai, and P. Mordohai. CBMV: A coalesced bidirectional matching volume for disparity estimation. CVPR 2018. ROB 2018 submission. |

05/31/18 | iResNet_ROB | H | Anonymous. Learning for disparity estimation through feature constancy. ROB 2018 submission. |

05/31/18 | FBW_ROB | H | B. Wiberg. Stereo matching with neural networks. Bachelors thesis, TU Munich 2018. ROB 2018 submission. |

05/30/18 | DLCB_ROB | H | Anonymous. Deep learning combination on disparity maps. ROB 2018 submission. |

05/28/18 | PWCDC_ROB | F | Anonymous. PWC-Net for stereo matching. ROB 2018 submission. |

05/26/18 | NOSS_ROB | H | Anonymous. Superpixel alpha-expansion and normal adjustment for stereo matching. ECCV 2018 submission 500. |

05/25/18 | XPNet_ROB | H | Anonymous. Edge enhanced end to end neural network for disparity estimation. ROB 2018 submission. |

05/25/18 | ETED_ROB | H | Anonymous. End to end refined estimation for depth. Submitted to ROB 2018. |

05/24/18 | LALA_ROB | H | Anonymous. Robust depth estimation. ROB 2018 submission. |

05/22/18 | DN-CSS_ROB | H | Anonymous. DN-CSS_ROB. ROB 2018 submission. |

05/18/18 | PDS | H | Anonymous. Practical deep stereo (PDS): Toward applications-friendly deep stereo matching. NIPS 2018 submission 2833. |

05/05/18 | WCMA_ROB | Q | Anonymous. Multi-scale random walk for stereo matching with weighted census transform. To be submitted to 3DV 2018. |

05/01/18 | PSMNet_ROB | Q | Anonymous. Pyramid stereo matching network. To appear at CVPR 2018. |

04/17/18 | ISM | Q | R. Hamzah, A. Kadmin, M. Hamid, S. Fakhar, A. Ghani, and H. Ibrahim. Improvement of stereo matching algorithm for 3D surface reconstruction. Signal Processing: Image Communication 65:165-172, 2018. |

03/26/18 | ELAS_ROB | H | A. Geiger, M. Roser, and R. Urtasun. Efficient large-scale stereo matching. ACCV 2010. |

03/23/18 | AVERAGE_ROB | H | Average disparity over all training images of the ROB 2018 stereo challenge. |

03/23/18 | MEDIAN_ROB | H | Median disparity over all training images of the ROB 2018 stereo challenge. |

03/14/18 | DTS | H | Anonymous. The domain transform solver. ECCV 2018 submission 1189. |

03/11/18 | SGM-Forest | H | Anonymous. Learning to aggregate costs from multiple scanline optimizations in semi-global matching. ECCV 2018 submission 1093. |

03/09/18 | SGM_ROB | H | H. Hirschmueller. Stereo processing by semi-global matching and mutual information. PAMI 30(2):328-341, 2008. ROB 2018 submission. |

03/06/18 | NOSS | H | Anonymous. Superpixel stereo matching based on normal optimization. ECCV 2018 submission 500. |

02/28/18 | FDR | H | T. Yan and Q. Zhao. Fast disparity refinement with occlusion handling for stereo matching. To appear in IEEE TIP 2018. |

02/07/18 | DF | Q | Anonymous. Disparity filtering with 3D convolutional neural networks. CRV 2018 submission 52. |

01/24/18 | SMSSR | Q | H. Li and C. Cheng. Adaptive weighted matching cost based on sparse representation. Submitted to IEEE TIP, 2018. |

12/11/17 | OVOD | H | M. Mozeorov and J. van de Weijer. One-view-occlusion detection for stereo matching with a fully connected CRF model. IEEE TIP 25 (2019). |

11/17/17 | CGPT | Q | J. Li, W. Shi, P. Gong, and G. Wang. Dense stereo matching method based on propagated filter. Submitted to IET Image Processing 2017. |

11/13/17 | CBMV | H | K. Batsos, C. Cai, and P. Mordohai. CBMV: A Coalesced bidirectional matching volume for disparity estimation. CVPR 2018. Code available. |

10/12/17 | FEN-D2DRR | H | X. Ye, J. Li, H. Wang, H. Huang, and X. Zhang. Efficient stereo matching leveraging deep local and context information. IEEE Access vol. 5, 2017. |

08/25/17 | SSR | Q | H. Li, C. Cheng, and L. Zhang. Stereo matching cost based on sparse representation. Submitted to IEEE Transactions on Circuits and Systems for Video Technology, 2017. |

06/22/17 | LocalExp | H | T. Taniai, Y. Matsushita, Y. Sato, and T. Naemura. Continuous 3D label stereo matching using local expansion moves. To appear in TPAMI 2018, DOI 10.1109/TPAMI.2017.2766072. |

06/14/17 | DoGGuided | Q | M. Kitagawa, I. Shimizu, and R. Sara. High accuracy local stereo matching using DoG scale map. IAPR MVA 2017. |

05/23/17 | r200high | H | L. Keselman, J. Woodfill, A. Grunnet-Jepsen, and A. Bhowmik. Intel RealSense stereoscopic depth cameras. CCD 2017. |

04/04/17 | DDL | Q | J. Yin, H. Zhu, D. Yuan, and T. Xue. Sparse representation over discriminative dictionary for stereo matching. Submitted to Pattern Recognition 2017. |

03/23/17 | DSGCA | Q | Anonymous. Deep self-guided cost aggregation for stereo matching. ICCV 2017 submission 1999. |

03/22/17 | JMR | H | P. Knöbelreiter, C. Reinbacher, A. Shekhovtsov and T. Pock. End-to-end training of hybrid CNN-CRF models for stereo. CVPR 2017. |

03/10/17 | MC-CNN+TDSR | F | S. Drouyer, S. Beucher, M. Bilodeau, M. Moreaud, and L. Sorbier. Sparse stereo disparity map densification using hierarchical image segmentation. 13th International Symposium on Mathematical Morphology. |

03/09/17 | SGMEPi | F | D. Scharstein, T. Taniai, and S. Sinha. Semi-global stereo matching with surface orientation priors. 3DV 2017. |

01/24/17 | 3DMST | H | L. Li, X. Yu, S. Zhang, X. Zhao, and L. Zhang. 3D cost aggregation with multiple minimum spanning trees for stereo matching. Applied Optics 56(12):3411-3420, 2017. |

01/15/17 | IGF | Q | R. Hamzah, H. Ibrahim, and A. Hassan. Stereo matching algorithm based on per pixel difference adjustment, iterative guided filter and graph segmentation. Journal of Visual Communication and Image Representation, 42:145-160, 2017. |

01/03/17 | SLAC | H | Anonymous. Sparse locally adaptive cost aggregation for stereo matching. CVPR 2017 submission 3288. |

11/24/16 | ADSM | Q | N. Ma, Y. Men, C. Men, and X. Li. Accurate dense stereo matching based on image segmentation using an adaptive multi-cost approach. Submitted to Symmetry, 2016. |

11/16/16 | MCSC | F | Anonymous. Simultaneous learning matching cost and smoothness constraint for stereo matching. CVPR 2017 submission 1368. |

11/15/16 | UCNN | H | Anonymous. Learning to compute the stereo matching cost without supervision. CVPR 2017 submission 2151. |

11/15/16 | MC-CNN-WS | H | S. Tulyakov, A. Ivanov, and F. Fleuret. Weakly supervised learning of deep metrics for stereo reconstruction. ICCV 2017. |

11/06/16 | SPS | F | C. Legendre, K. Batsos, and P. Mordohai. High-resolution stereo matching based on sampled photoconsistency computation. BMVC 2017. |

10/23/16 | SIGMRF | Q | M. Joshi. A learned IGMRF sparseness and IGMRF based regularization framework for dense disparity estimation. Submitted to IPSJ CVA 2016. |

10/19/16 | LW-CNN | H | H. Park and K. Lee. Look wider to match image patches with convolutional neural network. Submitted to IEEE Signal Processing Letters, 2016. |

09/20/16 | LFSIR | Q | Anonymous. A learned sparseness and IGMRF based regularization framework for dense disparity estimation using unsupervised feature learning. Submitted to IPSJ Transactions on Computer Vision and Applications, 2016. |

09/13/16 | SNP-RSM | H | S. Zhang, W. Xie, G. Zhang, H. Bao, and M. Kaess. Robust stereo matching with surface normal prediction. ICRA 2017. |

08/31/16 | SED | F | Anonymous. Disparity estimation by simultaneous edge drawing. ACCV 2016 Workshop 1 - 3D modelling and applications - Submission id 18. |

07/03/16 | LPU | H | Anonymous. 3D labeling stereo matching with content aware adaptive windows. 3DV 2016 submission 25. |

05/28/16 | APAP-Stereo | H | M.-G. Park and K.-J. Yoon. As-planar-as-possible depth map estimation. Submitted to IEEE TPAMI 2016. |

05/12/16 | PMSC | H | L. Li, S. Zhang, X. Yu, and L. Zhang. PMSC: PatchMatch-based superpixel cut for accurate stereo matching. Submitted to IEEE Transactions on Circuits and Systems for Video Technology, 2016. |

04/27/16 | JEM | Q | Anonymous. Stereo matching by joint energy minimization. ECCV 2016 submission 41. |

04/24/16 | HLSC_cor | H | S. Hadfield, K. Lebeda, and R. Bowden. Stereo reconstruction using top-down cues. Submitted to CVIU 2016. |

04/13/16 | Glstereo | H | Z. Ge. A global stereo matching algorithm with iterative optimization. China CAD & CG 2016 submission 595. |

04/12/16 | MeshStereoExt | H | C. Zhang, Z. Li, Y. Cheng, R. Cai, H. Chao, and Y. Rui. MeshStereo: a global stereo model with mesh alignment regularization for view interpolation. Submitted to IJCV 2016. |

04/03/16 | ICSG | F | Shahbazi et al. Revisiting intrinsic curves for efficient dense stereo matching. ISPRS Congress 2016 submission 913. |

03/15/16 | MPSV | Q | Anonymous. Morphological processing of stereoscopic image superimpositions for disparity map estimation. ECCV 2016 submission 1308. |

02/18/16 | LS-ELAS | F | R. Ait-Jellal, M. Lange, B. Wassermann, A. Schilling, and A. Zell. LS-ELAS: line segment based efficient large scale stereo matching. ICRA 2017. |

01/26/16 | MC-CNN-fst | H | J. Žbontar and Y. LeCun. Stereo matching by training a convolutional neural network to compare image patches. Submitted to JMLR 2015. Code available. |

01/21/16 | MCCNN_Layout | H | Anonymous. Stereo depth map refinement with scene layout estimation. CVPR 2016 submission 617. |

01/19/16 | NTDE | H | K.-R. Kim and C.-S. Kim. Adaptive smoothness constraints for efficient stereo matching using texture and edge information. ICIP 2016. |

12/18/15 | INTS | H | Anonymous. Image-guided non-local dense matching with three-steps optimization. ISPRS Congress 2016 submission 231. |

11/06/15 | SOU4P-net | H | Anonymous. Look wider and deeper to match. CVPR 2016 submission 975. |

11/05/15 | GCSVR | H | Anonymous. High accuracy stereo matching with spatially varying regularization. CVPR 2016 submission 863. |

11/03/15 | MC-CNN+RBS | H | J. Barron and B. Poole. The fast bilateral solver. ECCV 2016. |

10/13/15 | MDP | H | A. Li, D. Chen, Y. Liu, and Z. Yuan. Coordinating multiple disparity proposals for stereo computation. CVPR 2016. |

09/28/15 | R-NCC | F | S. Fang and Y. Li. Removed based multi-view stereo using window-based matching method. Submitted to MV&A, 2015. |

09/14/15 | ELAS | F | A. Geiger, M. Roser, and R. Urtasun. Efficient large-scale stereo matching. ACCV 2010. |

08/28/15 | MC-CNN-acrt | H | J. Žbontar and Y. LeCun. Stereo matching by training a convolutional neural network to compare image patches. Submitted to JMLR 2015. A previous version appeared in CVPR 2015. Code available. |

04/19/15 | MeshStereo | H | C. Zhang, Z. Li, Y. Cheng, R. Cai, H. Chao, and Y. Rui. MeshStereo: A global stereo model with mesh alignment regularization for view interpolation. ICCV 2015. |

04/17/15 | TMAP | H | E. Psota, J. Kowalczuk, M. Mittek, and L. Perez. MAP disparity estimation using hidden Markov trees. ICCV 2015. |

04/09/15 | PFS | F | C. Cigla and A. Alatan. Information permeability for stereo matching. Signal Processing: Image Communication 28(9), 2013. |

04/08/15 | REAF | H | C. Cigla. Recursive edge-aware filters for stereo matching. CVPR Embedded Vision Workshop 2015. |

01/21/15 | TSGO | F | M. Mozerov and J. Van de Weijer. Accurate stereo matching by two-step energy minimization. IEEE TIP 24(3):1153-1163, 2015. |

11/12/14 | LCU | Q | Anonymous. Using local cues to improve dense stereo matching. CVPR 2015 submission 973. |

10/07/14 | IDR | H | J. Kowalczuk, E. Psota, and L. Perez. Real-time stereo Matching on CUDA using an iterative refinement method for adaptive support-weight correspondences. IEEE TCSVT 23(1):94-104, 2013. |

09/18/14 | SNCC | H | N. Einecke and J. Eggert. A two-stage correlation method for stereoscopic depth estimation. DICTA 2010. |

09/10/14 | LAMC_DSM | H | C. Stentoumis, L. Grammatikopoulos, I. Kalisperakis, G. Karras On accurate dense stereo-matching using a local adaptive multi-cost approach. ISPRS Journal of Photogrammetry and Remote Sensing 91:29-49, 2014. |

08/31/14 | BSM | Q | K. Zhang, J. Li, Y. Li, W. Hu, L. Sun, and S. Yang. Binary stereo matching. ICPR 2012. |

08/27/14 | LPS | F | S. Sinha, D. Scharstein, and R. Szeliski. Efficient high-resolution stereo matching using local plane sweeps. CVPR 2014. |

08/25/14 | LPS | H | S. Sinha, D. Scharstein, and R. Szeliski. Efficient high-resolution stereo matching using local plane sweeps. CVPR 2014. |

07/28/14 | SGM | F | H. Hirschmüller. Stereo processing by semi-global matching and mutual information. CVPR 2006; PAMI 30(2):328-341, 2008. |

07/28/14 | SGM | H | H. Hirschmüller. Stereo processing by semi-global matching and mutual information. CVPR 2006; PAMI 30(2):328-341, 2008. |

07/28/14 | SGBM1 | H | OpenCV 2.4.8 StereoSGBM method, single-pass variant. Reimplementation and modification of H. Hirschmüller's SGM method (CVPR 2006; PAMI 2008). |

07/28/14 | Cens5 | H | H. Hirschmüller, P. Innocent, and J. Garibaldi. Real-time correlation-based stereo vision with reduced border errors. IJCV 47(1-3):229-246, 2002. |

07/25/14 | SGBM1 | F | OpenCV 2.4.8 StereoSGBM method, single-pass variant. Reimplementation and modification of H. Hirschmüller's SGM method (CVPR 2006; PAMI 2008). |

07/25/14 | SGM | Q | H. Hirschmüller. Stereo processing by semi-global matching and mutual information. CVPR 2006; PAMI 30(2):328-341, 2008. |

07/25/14 | SGBM1 | Q | OpenCV 2.4.8 StereoSGBM method, single-pass variant. Reimplementation and modification of H. Hirschmüller's SGM method (CVPR 2006; PAMI 2008). |

07/25/14 | SGBM2 | Q | OpenCV 2.4.8 StereoSGBM method, full variant (2 passes). Reimplementation of H. Hirschmüller's SGM method (CVPR 2006; PAMI 2008). |