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+# awesome-depth
+A curated list of publication for depth estimation
+s
+## 1. Supervised Methods
+[1] Eigen et al, Depth Map Prediction from a Single Image using a Multi-Scale Deep Network, NIPS 2014, [Web](https://cs.nyu.edu/~deigen/depth/)
+
+[2] Eigen et al, Predicting Depth, Surface Normals and Semantic Labels with a Common Multi-Scale Convolutional Architecture, ICCV 2015, [Web](https://cs.nyu.edu/~deigen/dnl/)
+
+[3] Laina et al, Deeper Depth Prediction with Fully Convolutional Residual Networks, 3DV 2016, [Code](https://github.com/iro-cp/FCRN-DepthPrediction
+)
+
+[4] Li et al, A Two-Streamed Network for Estimating Fine-Scaled Depth Maps from Single RGB Images, ICCV 2017, [PDF](http://arxiv.org/abs/1607.00730)
+
+[5] Xu et al, Structured Attention Guided Convolutional Neural Fields for Monocular Depth Estimation, CVPR 2018, [PDF](https://arxiv.org/abs/1803.11029)
+
+[6] Xu et al, PAD-Net: Multi-Tasks Guided Prediction-and-Distillation Network, CVPR 2018, [PDF](https://arxiv.org/abs/1805.04409)
+
+[7] Qi et al, GeoNet: Geometric Neural Network for Joint Depth and Surface Normal Estimation, CVPR 2018, [PDF](https://xjqi.github.io/geonet.pdf)
+
+
+## 2. Weakly Supervised Methods
+[1] Chen et al, Single-Image Depth Perception in the Wild, NIPS 2016, [Web](http://www-personal.umich.edu/~wfchen/depth-in-the-wild/)
+
+[2] Fu et al, Deep Ordinal Regression Network for Monocular Depth Estimation, CVPR 2018, [PDF](https://arxiv.org/abs/1806.02446)
+
+
+
+## 3. Semi-Supervised Methods
+
+
+[1] Kuznietsov et al, Semi-Supervised Deep Learning for Monocular Depth Map Prediction, CVPR 2017, [Code](https://github.com/Yevkuzn/semodepth)
+
+[2] Luo et al, Single View Stereo Matching, CVPR 2018, [Code](https://github.com/lawy623/SVS)
+
+## 4. Unsupervised (Self-Supervised) Methods
+### 4.1 Image
+
+[1] Garg et al, Unsupervised CNN for Single View Depth Estimation: Geometry to the Rescue, ECCV 2016,  [Code](https://github.com/Ravi-Garg/Unsupervised_Depth_Estimation)
+
+[2] Godard et al, Unsupervised Monocular Depth Estimation with Left-Right Consistency, CVPR 2017, [Web](http://visual.cs.ucl.ac.uk/pubs/monoDepth/)
+
+[3] Godard et al, Digging Into Self-Supervised Monocular Depth Estimation, aXiv 2018, [PDF](https://arxiv.org/abs/1711.07933)
+
+[4] Im et al, Robust Depth Estimation from Auto Bracketed Images, CVPR 2018, [PDF](https://arxiv.org/abs/1803.07702)
+
+### 4.2 Video
+[1] Zhou et al, Unsupervised Learning of Depth and Ego-Motion from Video, CVPR 2017, [Web](https://people.eecs.berkeley.edu/~tinghuiz/projects/SfMLearner/)
+
+[2] Yin et al, GeoNet: Unsupervised Learning of Dense Depth, Optical Flow and Camera Pose, CVPR 2018,[Code](https://github.com/yzcjtr/GeoNet)
+
+[3] Wang et al, Learning Depth from Monocular Videos using Direct Methods, CVPR 2018, [Code](https://github.com/MightyChaos/LKVOLearner)
+
+[4] Yang et al, LEGO: Learning Edge with Geometry all at Once by Watching Videos, CVPR 2018, [Code](https://github.com/zhenheny/LEGO)
+
+[5] Mahjourian et al, Unsupervised Learning of Depth and Ego-Motion from Monocular Video
+Using 3D Geometric Constraints, CVPR 2018, [PDF](https://arxiv.org/abs/1802.05522)
+
+[6] Zhan et al, Unsupervised Learning of Monocular Depth Estimation and Visual Odometry
+with Deep Feature Reconstruction, CVPR 2018, [Web](https://github.com/Huangying-Zhan/Depth-VO-Feat)
+
+
+## 5. Data Sets
+
+[1] Srinivasan et al, Aperture Supervision for Monocular Depth Estimation, CVPR 2018, [Code](https://github.com/google/aperture_supervision)
+
+[2] Li et al, MegaDepth: Learning Single-View Depth Prediction from Internet Photos, CVPR 2018, [Web](http://www.cs.cornell.edu/projects/megadepth/)
+
+[3] Monocular Relative Depth Perception with Web Stereo Data Supervision, CVPR 2018, [PDF](http://openaccess.thecvf.com/content_cvpr_2018/papers/Xian_Monocular_Relative_Depth_CVPR_2018_paper.pdf)
+
+[4] See [Link](https://scott89.github.io/depth-talk/#/6/1) for more conventional data sets.
+
+## 6. RGB-D Application
+
+## 7. Optical Flow & Scene Flow
+
+[1] Dosovitskiy et al, FlowNet: Learning optical flow with convolutional networks, CVPR 2015, [PDF](https://www.cv-foundation.org/openaccess/content_iccv_2015/papers/Dosovitskiy_FlowNet_Learning_Optical_ICCV_2015_paper.pdf)
+
+[2] Yu et al, Back to basics: Unsupervised learning of optical flow via brightness constancy and motion smoothness, ECCV 2016 Workshop, [PDF](https://arxiv.org/pdf/1608.05842v1.pdf)
+
+[3] Bailer et al, CNN-based Patch Matching for Optical Flow with Thresholded Hinge Embedding Loss, CVPR 2017, [PDF](http://arxiv.org/abs/1607.08064)
+
+[4] Ranjan et al, Optical Flow Estimation using a Spatial Pyramid Network(SpyNet), CVPR 2017, [Code](https://github.com/anuragranj/spynet)
+
+[5] Ilg et al, FlowNet 2.0: Evolution of Optical Flow Estimation with Deep Networks, CVPR 2017, [Code](https://github.com/lmb-freiburg/flownet2)
+
+[6] Sun et al, PWC-Net: CNNs for Optical Flow Using Pyramid, Warping, and Cost Volume, CVPR 2018, [Code](https://github.com/NVlabs/PWC-Net)
+
+[7] Wang et al, Occlusion Aware Unsupervised Learning of Optical Flow, CVPR 2018, [PDF](http://arxiv.org/abs/1711.05890)
+
+[6] Hui et al, LiteFlowNet: A Lightweight Convolutional Neural Network for Optical Flow Estimation, CVPR 2018, [PDF](http://openaccess.thecvf.com/content_cvpr_2018/papers/Hui_LiteFlowNet_A_Lightweight_CVPR_2018_paper.pdf)