bit0123 / awesome-depth

A curated list of publication for depth estimation

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

[2] Eigen et al, Predicting Depth, Surface Normals and Semantic Labels with a Common Multi-Scale Convolutional Architecture, ICCV 2015, Web

[3] Laina et al, Deeper Depth Prediction with Fully Convolutional Residual Networks, 3DV 2016, Code

[4] Li et al, A Two-Streamed Network for Estimating Fine-Scaled Depth Maps from Single RGB Images, ICCV 2017, PDF

[5] Xu et al, Structured Attention Guided Convolutional Neural Fields for Monocular Depth Estimation, CVPR 2018, PDF

[6] Xu et al, PAD-Net: Multi-Tasks Guided Prediction-and-Distillation Network, CVPR 2018, PDF

[7] Qi et al, GeoNet: Geometric Neural Network for Joint Depth and Surface Normal Estimation, CVPR 2018, PDF

2. Weakly Supervised Methods

[1] Chen et al, Single-Image Depth Perception in the Wild, NIPS 2016, Web

[2] Fu et al, Deep Ordinal Regression Network for Monocular Depth Estimation, CVPR 2018, PDF

3. Semi-Supervised Methods

[1] Kuznietsov et al, Semi-Supervised Deep Learning for Monocular Depth Map Prediction, CVPR 2017, Code

[2] Luo et al, Single View Stereo Matching, CVPR 2018, Code

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

[2] Godard et al, Unsupervised Monocular Depth Estimation with Left-Right Consistency, CVPR 2017, Web

[3] Godard et al, Digging Into Self-Supervised Monocular Depth Estimation, aXiv 2018, PDF

[4] Im et al, Robust Depth Estimation from Auto Bracketed Images, CVPR 2018, PDF

4.2 Video

[1] Zhou et al, Unsupervised Learning of Depth and Ego-Motion from Video, CVPR 2017, Web

[2] Yin et al, GeoNet: Unsupervised Learning of Dense Depth, Optical Flow and Camera Pose, CVPR 2018,Code

[3] Wang et al, Learning Depth from Monocular Videos using Direct Methods, CVPR 2018, Code

[4] Yang et al, LEGO: Learning Edge with Geometry all at Once by Watching Videos, CVPR 2018, Code

[5] Mahjourian et al, Unsupervised Learning of Depth and Ego-Motion from Monocular Video Using 3D Geometric Constraints, CVPR 2018, PDF

[6] Zhan et al, Unsupervised Learning of Monocular Depth Estimation and Visual Odometry with Deep Feature Reconstruction, CVPR 2018, Web

5. Data Sets

[1] Srinivasan et al, Aperture Supervision for Monocular Depth Estimation, CVPR 2018, Code

[2] Li et al, MegaDepth: Learning Single-View Depth Prediction from Internet Photos, CVPR 2018, Web

[3] Monocular Relative Depth Perception with Web Stereo Data Supervision, CVPR 2018, PDF

[4] See Link 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

[2] Yu et al, Back to basics: Unsupervised learning of optical flow via brightness constancy and motion smoothness, ECCV 2016 Workshop, PDF

[3] Bailer et al, CNN-based Patch Matching for Optical Flow with Thresholded Hinge Embedding Loss, CVPR 2017, PDF

[4] Ranjan et al, Optical Flow Estimation using a Spatial Pyramid Network(SpyNet), CVPR 2017, Code

[5] Ilg et al, FlowNet 2.0: Evolution of Optical Flow Estimation with Deep Networks, CVPR 2017, Code

[6] Sun et al, PWC-Net: CNNs for Optical Flow Using Pyramid, Warping, and Cost Volume, CVPR 2018, Code

[7] Wang et al, Occlusion Aware Unsupervised Learning of Optical Flow, CVPR 2018, PDF

[6] Hui et al, LiteFlowNet: A Lightweight Convolutional Neural Network for Optical Flow Estimation, CVPR 2018, PDF