Reference list of currently published stereo methods

DateMethodRes.Reference
02/24/20SGBMPFYaoyu Hu, Weikun Zhen, and Sebastian Scherer. Deep-learning assisted high-resolution binocular stereo depth reconstruction. ICRA 2020.
02/20/20CRARHLinghua Zeng and Xinmei Tian. CRAR: Accelerating stereo matching with cascaded regression and adaptive refinement. Submitted to Pattern Recognition, 2020.
02/07/20CasStereoHAnonymous. Cascade cost volume for high-resolution multi-view stereo and stereo matching. CVPR 2020 submission 6312.
01/07/20ADSR_GIFQLingyin Kong, Jiangping Zhu, and Sancong Ying. Stereo matching based on guidance image and adaptive support region. Submitted to Acta Optica Sinica, 2020.
01/05/20MTSFRafael Brandt, Nicola Strisciuglio, and Nicolai Petkov. Efficient and accurate depth estimation with 1-D max-tree matching. ICRA 2020 submission.
01/02/20PPEP-GFQYuli Fu, Kaimin Lai, Weixiang Chen, and Youjun Xiang. A pixel pair based encoding pattern for stereo matching via an adaptively weighted cost. Submitted to IET Image Processing, 2020.
12/30/19F-GDGIFHWeimin Yuan. Efficient local stereo matching algorithm based on fast gradient domain guided image filtering. Submitted to Pattern Recognition Letters, 2019.
12/19/19CRLEHHuaiyuan Xu, Xiaodong Chen, Haitao Liang, Siyu Ren, and Haotian Li. Cross-based rolling label expansion for dense stereo matching. Submitted to IEEE Access, 2019.
11/15/19SPPSMNetFAnonymous. Superpixel segmentation with fully convolutional networks. CVPR 2020 submission 8460.
11/14/19HSM-Smooth-OccFAnonymous. Enhancing deep stereo networks with geometric priors. CVPR 2020 submission 387.
11/11/19SACA-NetQAnonymous. Scale-aware cost aggregation for stereo matching. CVPR 2020 submission 582.
11/07/19LBPSHAnonymous. Belief propagation reloaded: Learning BP layers for dense prediction tasks. CVPR 2020 submission 5455.
11/01/19NVstereo2DHAnonymous. Deep stereo matching over 100 FPS. CVPR 2020 submission 8537.
07/26/19EdgeStereoFXiao Song, Xu Zhao, Liangji Fang, and Hanwen Hu. Edgestereo: An effective multi-task learning network for stereo matching and edge detection. To appear in IJCV 2019.
06/26/19DeepPruner_ROBQShivam Duggal, Shenlong Wang, Wei-Chiu Ma, Rui Hu, and Raquel Urtasun. DeepPruner: Learning efficient stereo matching via differentiable PatchMatch. ICCV 2019. Code.
05/28/19PWCA_SGMQHao Li, Yanwei Sun, and Li Sun. Edge-preserved disparity estimation with piecewise cost aggregation. Submitted to the International Journal of Geo-Information, 2019.
05/15/19PSMNet_2000HWei Wang, Wei Bao, Yulan Guo, Siyu Hong, Zhengfa Liang, Xiaohu Zhang, and Yuhua Xu. An indoor real scene dataset to train convolution networks for stereo matching. Submitted to SCIENCE CHINA Information Sciences, 2019.
05/13/19VNHPatrick Knöbelreiter and Thomas Pock. Learned collaborative stereo refinement. GCPR 2019.
05/10/19tMGM-16FSonali Patil, Tanmay Prakash, Bharath Comandur, and Avinash Kak. A comparative evaluation of SGM variants for dense stereo matching. Submitted to PAMI, 2019.
04/12/19TCSCSMHChunbo Cheng, Hong Li, and Liming Zhang. A new stereo matching cost based on two-branch convolutional sparse coding and sparse representation. Submitted to IEEE TIP, 2019.
03/09/193DMST-CMHYuhao Xiao, Dingding Xu, Guijin Wang, Xiaowei Hu, Yongbing Zhang, Xiangyang Ji, and Li Zhang. Confidence map based 3D cost aggregation with multiple minimum spanning trees for stereo matching. ACPR 2019.
03/06/19SM-AWPQSiti Safwana Abd Razak, Mohd Azlishah Othman, and Ahmad Fauzan Kadmin. The effect of adaptive weighted bilateral filter on stereo matching algorithm. International Journal of Engineering and Advanced Technology(IJEAT) 8(3) 2019, C5839028319.
02/15/19DAWA-FHJulia Navarro and Antoni Buades. Dense and robust image registration by shift adapted weighted aggregation and variational completion. Submitted to Image and Vision Computing, 2019.
02/05/19AMNetQXianzhi Du, Mostafa El-Khamy, and Jungwon Lee. AMNet: Deep atrous multiscale stereo disparity estimation networks. arXiv:1904.09099, 2019.
01/17/19FASWQWenhuan Wu, Hong Zhu, Shunyuan Yu, and Jing Shi. Stereo matching with fusing adaptive support weights. IEEE Access 7:61960-61974, 2019.
01/12/19EHCI_netHRun Wang. An end to end network for stereo matching using exploiting hierarchical context information. Master's thesis, HUST, 2019.
12/18/18MCV-MFCHZhengfa Liang, Yulan Guo, Yiliu Feng, Wei Chen, Linbo Qiao, Li Zhou, Jianfeng Zhang, and Hengzhu Liu. Stereo matching using multi-level cost volume and multi-scale feature constancy. PAMI 2019.
11/28/18MSFNetAHKyung-Rae Kim, Yeong Jun Koh, and Chang-Su Kim. Multiscale feature extractors for stereo matching cost computation. IEEE Access 6:27971-27983, 2018.
11/11/18MBMHQiong Chang and Tsutomu Maruyama. Real-time stereo vision system: a multi-block matching on GPU. IEEE Access 6:27971-27983, 2018.
11/08/18HSMFGengshan Yang, Joshua Manela, Michael Happold, and Deva Ramanan. Hierarchical deep stereo matching on high-resolution images. CVPR 2019. Code.
11/07/18IEBIMstHChao He, Congxuan Zhang, Zhen Chen, and Shaofeng Jiang. Minimum spanning tree based stereo matching using image edge and brightness information. CISP-BMEI 2017.
10/29/18iResNetHZhengfa Liang, Yiliu Feng, Yulan Guo, Hengzhu Liu, Wei Chen, Linbo Qiao, Li Zhou, and Jianfeng Zhang. Learning for disparity estimation through feature constancy. CVPR 2018.
10/29/18Dense-CNNHCongxuan Zhang, Junjie Wu, Zhen Chen, Wen Liu, Ming Li, and Shaofeng Jiang. Dense-CNN: Dense convolutional neural network for stereo matching using multi-scale feature connection. Submitted to Signal Processing and Image Communication, 2019.
10/10/18DISCOHKunal Swami, Kaushik Raghavan, Nikhilanj Pelluri, Rituparna Sarkar, and Pankaj Bajpai. DISCO: Depth inference from stereo using context. ICME 2019.
07/31/18MotionStereoHJulien Valentin, Adarsh Kowdle, Jonathan Barron, et al. Depth from motion for smartphone AR. ACM TOG 37(6):193 (Proc. of SIGGRAPH Asia), 2018.
06/27/18DCNNHWendong Mao, Mingjie Wang, Jun Zhou, and Minglun Gong. Semi-dense stereo matching using dual CNNs. WACV 2019.
06/14/18MSMD_ROBQHaihua Lu, Hai Xu, Li Zhang, Yanbo Ma, and Yong Zhao. Cascaded multi-scale and multi-dimension convolutional neural network for stereo matching. VCIP 2018.
06/05/18CBMBNetHYu Chen, Youshen Xia, and Chenwang Wu. A crop-based multi-branch network for matching cost computation. CISP-BMEI 2018.
05/31/18CBMV_ROBHKonstantinos Batsos, Changjiang Cai, and Philippos Mordohai. CBMV: A coalesced bidirectional matching volume for disparity estimation. ROB 2018 entry based on CVPR 2018 paper.
05/31/18iResNet_ROBHZhengfa Liang, Yiliu Feng, Yulan Guo, Hengzhu Liu, Wei Chen, Linbo Qiao, Li Zhou, and Jianfeng Zhang. Learning for disparity estimation through feature constancy. ROB 2018 entry based on CVPR 2018 paper.
05/31/18FBW_ROBHBenedikt Wiberg. Stereo matching with neural networks. Bachelors thesis, TU Munich 2018. ROB 2018 entry.
05/26/18NOSS_ROBHJie Li, Penglei Ji, and Xinguo Liu. Superpixel alpha-expansion and normal adjustment for stereo matching. Proceeding of CAD/Graphics 2019.
05/22/18DN-CSS_ROBHTonmoy Saikia, Eddy Ilg, and Thomas Brox. DispNet-CSS: Robust Vision submission. ROB 2018.
05/18/18PDSHStepan Tulyakov, Anton Ivanov, and Francois Fleuret. Practical deep stereo (PDS): Toward applications-friendly deep stereo matching. NeurIPS 2018.
05/01/18PSMNet_ROBQJia-Ren Chang and Yong-Sheng Chen. Pyramid stereo matching network. CVPR 2018. Code. ROB 2018 entry by Hisao Chien Yang.
04/17/18ISMQRostam Affendi Hamzah, Fauzan Kadmin, Saad Hamid, Fakhar Ghani, and Haidi Ibrahim. Improvement of stereo matching algorithm for 3D surface reconstruction. Signal Processing: Image Communication 65:165-172, 2018.
03/26/18ELAS_ROBHAndreas Geiger, Martin Roser, and Raquel Urtasun. Efficient large-scale stereo matching. ACCV 2010. Code. ROB 2018 baseline.
03/23/18AVERAGE_ROBHAverage disparity over all training images of the ROB 2018 stereo challenge.
03/23/18MEDIAN_ROBHMedian disparity over all training images of the ROB 2018 stereo challenge.
03/14/18DTSHAkash Bapat and Jan-Michael Frahm. The domain transform solver. CVPR 2019.
03/11/18SGM-ForestHJohannes Schönberger, Sudipta Sinha, and Marc Pollefeys. Learning to fuse proposals from multiple scanline optimizations in semi-global matching. ECCV 2018.
03/09/18SGM_ROBHHeiko Hirschmüller. Stereo processing by semi-global matching and mutual information. CVPR 2006; PAMI 30(2):328-341, 2008. ROB 2018 baseline.
02/28/18SDRHTingman Yan, Yangzhou Gan, Zeyang Xia, and Qunfei Zhao. Segment-based disparity refinement with occlusion handling for stereo matching. IEEE TIP 2019. Code.
02/07/18DFQWendong Mao and Minglun Gong. Disparity filtering with 3D convolutional neural networks. CRV 2018.
01/24/18SMSSRQHong Li and Chunbo Cheng. Adaptive weighted matching cost based on sparse representation. Submitted to IEEE TIP, 2018.
12/11/17OVODHMikhail Mozerov and Joost van de Weijer. One-view-occlusion detection for stereo matching with a fully connected CRF model. IEEE TIP 28(6):2936-2947, 2019. Code.
11/13/17CBMVHKonstantinos Batsos, Changjiang Cai, and Philippos Mordohai. CBMV: A Coalesced bidirectional matching volume for disparity estimation. CVPR 2018. Code.
10/12/17FEN-D2DRRHXiaoqing Ye, Jiamao Li, Han Wang, Hexiao Huang, and Xiaolin Zhang. Efficient stereo matching leveraging deep local and context information. IEEE Access 5:18745-18755, 2017.
06/22/17LocalExpHTatsunori Taniai, Yasuyuki Matsushita, Yoichi Sato, and Takeshi Naemura. Continuous 3D label stereo matching using local expansion moves. PAMI 40(11):2725-2739, 2018. Code.
06/14/17DoGGuidedQMasamichi Kitagawa, Ikuko Shimizu, and Radim Sara. High accuracy local stereo matching using DoG scale map. IAPR MVA 2017.
05/23/17r200highHLeonid Keselman, John Woodfill, Anders Grunnet-Jepsen, and Achintya Bhowmik. Intel RealSense stereoscopic depth cameras. CVPR workshop CCD 2017.
04/04/17DDLQJihao Yin, Hongmei Zhu, Ding Yuan, and Tianfan Xue. Sparse representation over discriminative dictionary for stereo matching. Pattern Recognition 71:278-289, 2017. Code.
03/23/17DSGCAQWilliem and In Kyu Park. Deep self-guided cost aggregation for stereo matching. Pattern Recognition Letters 112:168-175, 2018.
03/22/17JMRHPatrick Knöbelreiter, Christian Reinbacher, Alexander Shekhovtsov, and Thomas Pock. End-to-end training of hybrid CNN-CRF models for stereo. CVPR 2017. Code.
03/10/17MC-CNN+TDSRFSebastien Drouyer, Serge Beucher, Michel Bilodeau, Maxime Moreaud, and Loic Sorbier. Sparse stereo disparity map densification using hierarchical image segmentation. 13th International Symposium on Mathematical Morphology, 2017.
03/09/17SGMEPiFDaniel Scharstein, Tatsunori Taniai, and Sudipta Sinha. Semi-global stereo matching with surface orientation priors. 3DV 2017.
01/24/173DMSTHLincheng Li, Xin Yu, Shunli Zhang, Xiaolin Zhao, and Li Zhang. 3D cost aggregation with multiple minimum spanning trees for stereo matching. Applied Optics 56(12):3411-3420, 2017.
01/15/17IGFQRostam Affendi Hamzah, Haidi Ibrahim, and A. H. Abu 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.
11/24/16ADSMQNing Ma, Yobo Men, Chaoguang Men, and Xiang Li. Accurate dense stereo matching based on image segmentation using an adaptive multi-cost approach. Symmetry 8(12):159, 2016.
11/16/16MCSCFMenglong Yang and Xuebin Lv. Learning both matching cost and smoothness constraint for stereo matching. Neurocomputing 314:234-241, 2018.
11/15/16MC-CNN-WSHStepan Tulyakov, Anton Ivanov, and Francois Fleuret. Weakly supervised learning of deep metrics for stereo reconstruction. ICCV 2017.
11/06/16SPSFChloe LeGendre, Konstantinos Batsos, and Philippos Mordohai. High-resolution stereo matching based on sampled photoconsistency computation. BMVC 2017.
10/23/16SIGMRFQSonam Nahar and Manjunath Joshi. A learned sparseness and IGMRF-based regularization framework for dense disparity estimation using unsupervised feature learning. IPSJ CVA 9:2, 2017.
10/19/16LW-CNNHHaesol Park and Kyoung Mu Lee. Look wider to match image patches with convolutional neural network. IEEE Signal Processing Letters 24(12):1788-1792, 2017.
09/13/16SNP-RSMHShuangli Zhang, Weijian Xie, Guofeng Zhang, Hujun Bao, and Michael Kaess. Robust stereo matching with surface normal prediction. ICRA 2017.
08/31/16SEDFDexmont Pena and Alistair Sutherland. Disparity estimation by simultaneous edge drawing. ACCV 2016 Workshop on 3D modelling and applications.
07/03/16LPUHLuis Horna and Robert Fisher. 3D plane labeling stereo matching with content aware adaptive windows. VISAPP 2017.
05/28/16APAP-StereoHMin-Gyu Park and Kuk-Jin Yoon. As-planar-as-possible depth map estimation. CVIU 181:50-59, 2019.
05/12/16PMSCHLincheng Li, Shunli Zhang, Xin Yu, and Li Zhang. PMSC: PatchMatch-based superpixel cut for accurate stereo matching. IEEE TCSVT 28(3):679-692, 2016.
04/27/16JEMQHongyang Xue and Deng Cai. Stereo matching by joint energy minimization. arXiv:1601.03890, 2016.
04/24/16HLSC_corHSimon Hadfield, Karel Lebeda, and Richard Bowden. Stereo reconstruction using top-down cues. CVIU 157:206-222, 2017.
04/03/16ICSGFMozhdeh Shahbazi, Gunho Sohn, Jerome Theau, and Patrick Menard. Revisiting intrinsic curves for efficient dense stereo matching. ISPRS Congress 2016.
03/15/16MPSVQJean-Charles Bricola, Michel Bilodeau, and Serge Beucher. Morphological processing of stereoscopic image superimpositions for disparity map estimation. HAL archives, hal-01330139f, 2016.
02/18/16LS-ELASFRadouane Ait-Jellal, Manuel Lange, Benjamin Wassermann, Andreas Schilling, and Andreas Zell. LS-ELAS: line segment based efficient large scale stereo matching. ICRA 2017.
01/26/16MC-CNN-fstHJure Zbontar and Yann LeCun. Stereo matching by training a convolutional neural network to compare image patches (fast architecture). JMLR 17:1-32, 2016. Code.
01/19/16NTDEHKyung-Rae Kim and Chang-Su Kim. Adaptive smoothness constraints for efficient stereo matching using texture and edge information. ICIP 2016.
12/18/15INTSHXu Huang, Yongjun Zhang, and Zhaoxi Yue. Image-guided non-local dense matching with three-steps optimization. ISPRS Congress 2016.
11/03/15MC-CNN+RBSHJonathan Barron and Ben Poole. The fast bilateral solver. ECCV 2016. Code.
10/13/15MDPHAng Li, Dapeng Chen, Yuanliu Liu, and Zejian Yuan. Coordinating multiple disparity proposals for stereo computation. CVPR 2016.
09/28/15R-NCCFYichao Li and Suping Fang. Removal-based multi-view stereo using a window-based matching method. Optik 178:1318-1336, 2019.
09/14/15ELASFAndreas Geiger, Martin Roser, and Raquel Urtasun. Efficient large-scale stereo matching. ACCV 2010. Code.
08/28/15MC-CNN-acrtHJure Zbontar and Yann LeCun. Stereo matching by training a convolutional neural network to compare image patches (accurate architecture). JMLR 17:1-32, 2016. Code.
04/19/15MeshStereoHChi Zhang, Zhiwei Li, Yanhua Cheng, Rui Cai, Hongyang Chao, and Yong Rui. MeshStereo: A global stereo model with mesh alignment regularization for view interpolation. ICCV 2015.
04/17/15TMAPHEric Psota, Jedrzej Kowalczuk, Mateusz Mittek, and Lance Perez. MAP disparity estimation using hidden Markov trees. ICCV 2015.
04/09/15PFSFCevahir Cigla and Aydin Alatan. Information permeability for stereo matching. Signal Processing: Image Communication 28(9), 2013.
04/08/15REAFHCevahir Cigla. Recursive edge-aware filters for stereo matching. CVPR Embedded Vision Workshop 2015.
01/21/15TSGOFMikhail Mozerov and Joost van de Weijer. Accurate stereo matching by two-step energy minimization. IEEE TIP 24(3):1153-1163, 2015.
10/07/14IDRHJedrzej Kowalczuk, Eric Psota, and Lance Perez. Real-time stereo Matching on CUDA using an iterative refinement method for adaptive support-weight correspondences. IEEE TCSVT 23(1):94-104, 2013.
09/18/14SNCCHNils Einecke and Julian Eggert. A two-stage correlation method for stereoscopic depth estimation. DICTA 2010.
09/10/14LAMC_DSMHChristos Stentoumis, Lazaros Grammatikopoulos, Ilias Kalisperakis, and George Karras. On accurate dense stereo-matching using a local adaptive multi-cost approach. ISPRS Journal of Photogrammetry and Remote Sensing 91:29-49, 2014.
08/31/14BSMQKang Zhang, Jiyang Li, Yijing Li, Weidong Hu, Lifeng Sun, and Shiqiang Yang. Binary stereo matching. ICPR 2012.
08/27/14LPSFSudipta Sinha, Daniel Scharstein, and Richard Szeliski. Efficient high-resolution stereo matching using local plane sweeps. CVPR 2014.
08/25/14LPSHSudipta Sinha, Daniel Scharstein, and Richard Szeliski. Efficient high-resolution stereo matching using local plane sweeps. CVPR 2014.
07/28/14SGMFHeiko Hirschmüller. Stereo processing by semi-global matching and mutual information. CVPR 2006; PAMI 30(2):328-341, 2008.
07/28/14SGMHHeiko Hirschmüller. Stereo processing by semi-global matching and mutual information. CVPR 2006; PAMI 30(2):328-341, 2008.
07/28/14SGBM1HOpenCV 2.4.8 StereoSGBM method, single-pass variant. Reimplementation and modification of H. Hirschmüller's SGM method (CVPR 2006; PAMI 2008).
07/28/14Cens5HHeiko Hirschmüller, Peter Innocent, and Jon Garibaldi. Real-time correlation-based stereo vision with reduced border errors. IJCV 47(1-3):229-246, 2002.
07/25/14SGBM1FOpenCV 2.4.8 StereoSGBM method, single-pass variant. Reimplementation and modification of H. Hirschmüller's SGM method (CVPR 2006; PAMI 2008).
07/25/14SGMQHeiko Hirschmüller. Stereo processing by semi-global matching and mutual information. CVPR 2006; PAMI 30(2):328-341, 2008.
07/25/14SGBM1QOpenCV 2.4.8 StereoSGBM method, single-pass variant. Reimplementation and modification of H. Hirschmüller's SGM method (CVPR 2006; PAMI 2008).
07/25/14SGBM2QOpenCV 2.4.8 StereoSGBM method, full variant (2 passes). Reimplementation of H. Hirschmüller's SGM method (CVPR 2006; PAMI 2008).