We will focus on how to do depth estimation using deep learning and traditional stereo matching methods.

1.FlowNet：Learning Optical Flow with Convolutional Networks(ICCV2015)
2.Computing the Stereo Matching Cost with a Convolutional Neural Network(cvpr2015)

1.DispNet:A Large Dataset to Train Convolutional Networks for Disparity, Optical Flow, and Scene Flow Estimatimation(cvpr2016)
2.Deep stereo fusion： combining multiple disparity hypotheses with deep-learning(3DV2016)
3.Efficient Deep Learning for Stereo Matching(cvpr2016)

1.GCNet:End-to-end learning of geometry and context for deep stereo regression(iccv2017)
2.Self-Supervised Learning for Stereo Matching with Self-Improving Ability(arxiv2017)
3.Unsupervised Learning of Stereo Matching(ICCV2017)
4.End-to-End Training of Hybrid CNN-CRF Models for Stereo(CVPR2017)
5.FlowNet 2.0： Evolution of Optical Flow Estimation with Deep Networks(CVPR2017)
6.Monodepth：Unsupervised Monocular Depth Estimation with Left-Right Consistency(cvpr2017)

1.Deep Material-aware Cross-spectral Stereo Matching(cvpr2018)
2.Deep Stereo Matching with Explicit Cost Aggregation Sub-Architecture(AAAI2018)
3.Deep Virtual Stereo Odometry:Leveraging Deep Depth Prediction for Monocular Direct Sparse Odometry(ECCV2018)
4.DenseMepNet：Fast Disparity Estimation using Dense Networks(ICRA2018)
5.Deep Ordinal Regression Network for Monocular Depth Estimation(cvpr2018)
6.Learning for Disparity Estimation through Feature Constancy(cvpr2018)
7.Left-Right Comparative Recurrent Model for Stereo Matching(CVPR2018)
8.MSFNet:End-to-End Learning of Multi-scale Convolutional Neural Network for Stereo Matching(ACML2018)
9.MVSNet:Depth Inference for Unstructured Multi-view Stereo(Eccv2018)
10.Practical Deep Stereo (PDS):Toward applications-friendly deep stereo matching(2018)
11.T2Net:Synthetic-to-Realistic Translation for Solving Single-Image Depth Estimation Tasks(ECCV2018)
12.Multi-scale CNN stereo and pattern removal technique for underwater active stereo system(3DV2018)
13.ASN-ActiveStereoNet End-to-End Self-Supervised Learning for Active Stereo Systems(ECCV2018)
14.Sparse_Cost_Volume_for_Efficient_Stereo_Matching(2018)
15.StereoNet:Guided Hierarchical Refinement for Real-Time Edge-Aware Depth Prediction(ECCV2018)
16.Pyramid Stereo Matching Network(cvpr2018)
17.Zoom and Learn： Generalizing Deep Stereo Matching to Novel Domains(cvpr2018)

1.360SD-Net：360° Stereo Depth Estimation with Learnable Cost Volume(iccvw2019)
2.AnyNet：Anytime Stereo Image Depth Estimation on Mobile Devices(ICRA2019)
3.CNN-based Cost Volume Analysis as Confidence Measure for Dense Matching(ICCVW2019)
4.CSPN：Learning Depth with Convolutional Spatial Propagation Network(2019)
5.DeepPruner： Learning Efficient Stereo Matching via Differentiable PatchMatch(ICCV2019)
6.DSMNet：Domain-invariant Stereo Matching Networks(2019)
7.FD-Fusion：Fast Stereo Disparity Maps Refinement By Fusion of Data-Based And Model-Based Estimations(3DV2019)
8.GA-Net：Guided Aggregation Net for End-to-end Stereo Matching(CVPR2019)
9.GSM：Guided Stereo Matching(cvpr2019)
10.GwcNet：Group-wise Correlation Stereo Network(cvpr2019)
11.HD3Stereo:Hierarchical Discrete Distribution Decomposition for Match Density Estimation(cvpr2019)
12.Shift Convolution Network for Stereo Matching(arxiv2019)
13.HSM：Hierarchical Deep Stereo Matching on High-resolution Images(cvpr2019)
14.ISGMR：Fast and Differentiable Message Passing for Stereo Vision(2019)
15.Learn Stereo, Infer Mono：Siamese Networks for Self-Supervised, Monocular, Depth Estimation(cvprw2019)
16.MADNet：Real-Time Self-Adaptive Deep Stereo(cvpr2019oral)
17.Monodepth2：Digging Into Self-Supervised Monocular Depth Estimation(iccv2019)
18.Multi-scale Cross-form Pyramid Network for Stereo Matching(ICIEA2019)
19.MVS：Recurrent MVSNet for High-resolution Multi-view Stereo Depth Inference(cvpr2019)
20.Region Deformer Networks for Unsupervised Depth Estimation from Unconstrained Monocular Videos(IJCAI2019)
21.SENSE：a Shared Encoder Network for Scene-flow Estimation(Iccv2019oral)
22.struct2depth：Depth Prediction Without the Sensors Leveraging Structure for Unsupervised Learning from Monocular Videos(AAAI2019)
23.TW-SMNet：Deep Multitask Learning of Tele-Wide Stereo Matching
24.Unsupervised Monocular Depth and Ego-motion Learning with Structure and Semantics(cvprw2019)
25.unsupervised monocular depth eatimation with clear boundaries(ICLR2019)
26.Neural rgb (r) d sensing： Depth and uncertainty from a video camera(cvpr2019oral)
27.Generating and Exploiting Probabilistic Monocular Depth Estimates(2019)
28.Learning Dense Wide Baseline Stereo Matching for People(ICCVW2019)
29.Depth from Videos in the Wild： Unsupervised Monocular Depth Learning from Unknown Cameras(ICCV2019)
30.Learning Single Camera Depth Estimation using Dual-Pixels(ICCV2019oral)
31.Single-Image Depth Inference Using Generative Adversarial Networks(sensors2019)
32.Structured Coupled Generative Adversarial Networks for Unsupervised Monocular Depth Estimation(2019)
33.EdgeStereo： An Effective Multi-Task Learning Network for Stereo Matching and Edge Detection(2019)
34.AMNet:Deep Atrous Multiscale Stereo Disparity Estimation Networks(2019)
35.Learning to Adapt for Stereo(cvpr2019)
36.Unsupervised Cross-Spectral Stereo Matching by Learning to Synthesize(AAAI2019)
37.Semantic Stereo Matching with Pyramid Cost Volumes(ICCV2019)

1.AcfNet：Adaptive Unimodal Cost Volume Filtering for Deep Stereo Matching(AAAI2020)
2.Du2Net： Learning Depth Estimation from Dual-Cameras and Dual-Pixels
3.FADNet：A Fast and Accurate Network for Disparity Estimation（ICRA2020）
4.Fast_DS：Fast Deep Stereo with 2D Convolutional Processing of Cost Signatures(WACV2020)
5.LFattNet：Attention-based View Selection Networks for Light-field Disparity Estimation(AAAI2020)
6.CasMVSNet：Cascade Cost Volume for High-Resolution Multi-View Stereo and Stereo Matching(cvpr2020oral)
7.Fast-MVSNet：Sparse-to-Dense Multi-View Stereo With Learned Propagation and Gauss-Newton Refinement(cvpr2020)
8.Real-Time Semantic Stereo Matching(ICRA2020)
9.Self-supervised Monocular Trained Depth Estimation using Self-attention and Discrete Disparity Volume
10.Uncertainty Estimation for End-To-End Learned Dense Stereo Matching via Probabilistic Deep Learning
11.3D Packing for Self-Supervised Monocular Depth Estimation(2020cvproral)
12.MaskFlownet: Asymmetric Feature Matching with Learnable Occlusion Mask(cvpr2020oral)
13.A Survey on Deep Learning Techniques for Stereo-based Depth Estimation(arxiv2020)