by kalilia.
- Overview
- Datasets
- 1-Monocular-Video: SfM based monocular depth
- 2-Mutiview: Multi-view-Stereo
- 3-Light-Field-based
- i-Night time depth
- ii-Semantic aware depth
- Related: depth complementation
- Related: Video-depth
- Related: SLAM-Odometry
Conference | Tittle | code | Author | mark | note |
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Single Image Depth Estimation: An Overview | Istanbul Technical University | π |
Tittle | yaer | mark | note |
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Vision meets Robotics: The KITTI Dataset | 2012 | Karlsruhe Institute of Technology | |
NYUDepth-v2οΌIndoor Segmentation and Support Inference from RGBD Images | 2012 | New York University | |
nuScenes: A multimodal dataset for autonomous driving | 2018 | nuTonomy: an APTIV company |
Conference | Tittle | code | Author | mark | note |
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CVPR2017 | Semi-Supervised Deep Learning for Monocular Depth Map Prediction | RWTH Aachen University | π | ||
CVPR2017 | SfMLearner: Unsupervised Learning of Depth and Ego-Motion from Video | link | UC Berkeley | β | link |
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Conference | Tittle | code | Author | mark | note |
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CVPR2018 | DVO: Learning Depth from Monocular Videos using Direct Methods | Carnegie Mellon University | π | ||
CVPR2018 | GeoNet: Unsupervised Learning of Dense Depth, Optical Flow and Camera Pose | link | SenseTime Research | π | |
ECCV2018 | DF-Net: Unsupervised Joint Learning of Depth and Flow using Cross-Task Consistency | link | Virginia Tech | π | |
ECCV2018 | Supervising the new with the old: learning SFM from SFM | ) | University of Oxford | π |
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Conference | Tittle | code | Author | mark | note |
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-update-time-01-19-2022- | |||||
ICCV2019 | Moving Indoor: Unsupervised Video Depth Learning in Challenging Environments | Tsinghua University | |||
ECCV2020 | P2Net: Patch-match and Plane-regularizationfor Unsupervised Indoor Depth Estimation | ShanghaiTech Univsertiy | |||
ICCV2021 | StructDepth: Leveraging the structural regularities for self-supervised indoordepth estimation | ||||
ICCV2021 | MonoIndoor: Towards Good Practice of Self-Supervised Monocular Depth Estimation for Indoor Environments | OPPO US Research Cente | |||
3DV2021 | PLNet: Plane and Line Priors for Unsupervised Indoor Depth Estimation | The Chinese University of Hong Kong | |||
TIPAMI | Auto-Rectify Network for Unsupervised IndoorDepth Estimation | University of Adelaide | |||
2022 | Toward Practical Self-Supervised Monocular Indoor Depth Estimation | University of Southern California | |||
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Conference | Tittle | code | Author | mark |
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PAMI2008 | SGMοΌStereo processing by Semi-Global matching and Mutual Information | German Aerospace Cente | π | |
ECCV2016 | Unsupervised CNN for Single View Depth Estimation: Geometry to the Rescue | University of Adelaide | π | |
CVPR2017 | DispNet: Unsupervised Monocular Depth Estimation with Left-Right Consistency | University College London | π | |
Cost Volume Pyramid Based Depth Inference for Multi-View Stereo Jiayu | link | Northwestern Polytechnical University | π | |
CVPR2020 | Semi-Supervised Deep Learning for Monocular Depth Map Prediction | Australian National University | π | |
AAAI2021 | Self-supervised Multi-view Stereo via Effective Co-Segmentation and Data-Augmentation | South China University of Technology | π | |
CVPR2021 | Differentiable Diffusion for Dense Depth Estimation from Multi-view Images | Brown University | π | |
ICCV2021 | NerfingMVS: Guided Optimization of Neural Radiance Fields for Indoor Multi-view Stereo | Australian National University | β |
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Conference | Tittle | code | Author | mark |
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ECCV2014 | LSD-SLAM: Large-Scale Direct Monocular SLAM | TUM | π | |
TR2015 | ORB-SLAM: A Versatile and Accurate Monocular SLAM System | Universidad de Zaragoza | π | |
2016 | Direct Visual Odometry using Bit-Planes | Carnegie Mellon University | π | |
TR2017 | ORB-SLAM2: An Open-Source SLAM System for Monocular, Stereo, and RGB-D Cameras | Universidad de Zaragoza | π | |
2016 | A Photometrically Calibrated Benchmark For Monocular Visual Odometry | TUM | π |
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Conference | Tittle | code | Author | mark |
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PAMI2018 | DSO: Direct Sparse Odometry | TUM | π | |
IROS2018 | LDSO: Direct Sparse Odometry with Loop Closure | TUM | π | |
ECCV2018 | Deep Virtual Stereo Odometry:Leveraging Deep Depth Prediction for Monocular Direct Sparse Odometry | TUM | π | |
2018 | Self-improving visual odometry | Magic Leap, Inc. | π |
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Conference | Tittle | code | Author | mark |
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ICLR2019 | BA-NET: DENSE BUNDLE ADJUSTMENT NETWORKS | Simon Fraser University | π | |
TartanVO: A Generalizable Learning-based VO | link | Carnegie Mellon University | π | |
IROS | D2VO: Monocular Deep Direct Visual Odometry | π |
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Conference | Tittle | code | Author | mark |
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ECCV2020 | Pseudo RGB-D for Self-Improving Monocular SLAM and Depth Prediction | IIIT-Delhi | π | |
CVPR2020 | VOLDOR: Visual Odometry from Log-logistic Dense Optical flow Residuals | Stevens Institute of Technology | π | |
2021 | Generalizing to the Open World: Deep Visual Odometry with Online Adaptation | Peking University | π | |
ICRA2021 | SA-LOAM: Semantic-aided LiDAR SLAM with Loop Closure | Zhejiang University | π |
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Conference | Tittle | code | Author | mark |
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2020 | SAFENet: Self-Supervised Monocular Depth Estimation with Semantic-Aware Feature Extraction | KAIST | ||
AAAI2021 | Learning Monocular Depth in Dynamic Scenes via Instance-Aware Projection Consistency | KAIST | ||
ICCV2021 | Fine-grained Semantics-aware Representation Enhancement for Self-supervised Monocular Depth Estimation | Seoul National University |
Conference | Tittle | code | Author | mark | note |
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NIPS2020 | Forget About the LiDAR: Self-Supervised Depth Estimators with MED Probability Volumes | Korea Advanced Institute of Science and Technology | π | link | |
CVPR2021 | DRO: Deep Recurrent Optimizer for Structure-from-Motion | Alibaba A.I. Labs | π | link | |
CVPR2021 | The Temporal Opportunist: Self-Supervised Multi-Frame Monocular Depth | link | Niantic | π | |
CVPR2020 | Self-supervised Monocular Trained Depth Estimation using Self-attention and Discrete Disparity Volume | link | Australian Institute for Machine Learning | π |
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Conference | Tittle | code | Author | mark |
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-update-time-10-13-2021- | ||||
CVPR 2020 | Consistent Video Depth Estimation | University of Washington | ||
CVPR 2021 | Robust Consistent Video Depth Estimation | |||
SIGGRAPH 2021 | Consistent Depth of Moving Objects in Video |
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Conference | Tittle | code | Author | mark |
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Conference | Tittle | code | Author | mark |
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ICCV 2021 | Regularizing Nighttime Weirdness: Efficient Self-supervised Monocular Depth Estimation in the Dark | Nanjing University of Science and Technology | π | |
ICCV 2021 | Self-supervised Monocular Depth Estimation for All Day Images using Domain Separation | Zhejiang University | π | |
2021 | Unsupervised Depth and Ego-motion Estimation for Monocular Thermal Video using Multi-spectral Consistency Loss | KAIST | π |
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Conference | Tittle | code | Author | mark |
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CVPR2021 | Differentiable Diffusion for Dense Depth Estimation from Multi-view Images | Brown University | π | |
IROS2021 | Unsupervised Learning of Depth Estimation and Visual Odometry for Sparse Light Field Cameras | The University of Sydney | π | |
2021 | Occlusion-aware Unsupervised Learning of Depth from 4-D Light Fields | University of Sydney | π |
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Conference | Tittle | code | Author | mark |
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Sparse Auxiliary Networks for Unified Monocular Depth Prediction and Completion Vitor | Toyota Research Institute (TRI) | π | ||
3DV2019 | Enhancing self-supervised monocular depth estimation with traditional visual odometry | Univrses AB | π | |
ECCV2020 | S3Net: Semantic-aware self-supervised depth estimation with monocular videos and synthetic data | UCSD | π | |
ICCV2021 | Unsupervised Depth Completion with Calibrated Backprojection Layers Alex | UCLA | π | |
-update-time-11-29-2021- | ||||
Self-Supervised Depth Completion for Active Stereo | UCLA | π | ||
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