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Self-Supervised depth kalilia

Self-Supervised-depth

by kalilia.

Contents

  • Overview
  • Datasets
  • 1-Monocular-Video: SfM based monocular depth
    • 2017-2019
    • 2020
    • 2021
    • Indoor
  • 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

0-depth-estimation-overview

Conference Tittle code Author mark note
Single Image Depth Estimation: An Overview Istanbul Technical University :hear_no_evil:

*-datasets

Tittle yaer mark note
Vision meets Robotics: The KITTI Dataset 2012 Karlsruhe Institute of Technology
NYUDepth-v2：Indoor Segmentation and Support Inference from RGBD Images 2012 New York University
nuScenes: A multimodal dataset for autonomous driving 2018 nuTonomy: an APTIV company

Mono-SfM

2017

Conference Tittle code Author mark note
CVPR2017 Semi-Supervised Deep Learning for Monocular Depth Map Prediction RWTH Aachen University :see_no_evil:
CVPR2017 SfMLearner: Unsupervised Learning of Depth and Ego-Motion from Video link UC Berkeley :star: link

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2018

Conference Tittle code Author mark note
CVPR2018 DVO: Learning Depth from Monocular Videos using Direct Methods Carnegie Mellon University :see_no_evil:
CVPR2018 GeoNet: Unsupervised Learning of Dense Depth, Optical Flow and Camera Pose link SenseTime Research :see_no_evil:
ECCV2018 DF-Net: Unsupervised Joint Learning of Depth and Flow using Cross-Task Consistency link Virginia Tech :see_no_evil:
ECCV2018 Supervising the new with the old: learning SFM from SFM ) University of Oxford :see_no_evil:

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2019

Conference Tittle code Author mark note
2019 Self-Supervised 3D Keypoint Learning for Ego-motion Estimation Toyota Research Institute (TRI) :see_no_evil:
ICRA2019 SuperDepth: Self-Supervised, Super-Resolved Monocular Depth Estimation Toyota Research Institute (TRI) :see_no_evil:
AAAI2019 Depth prediction without the sensors: Leveraging structure for unsupervised learning from monocular videos link Harvard University/Google Brain :see_no_evil:
ICCV2019 Moving indoor: Unsupervised video depth learning in challenging environments. Tsinghua University :see_no_evil:
ICCV2019 Unsupervised High-Resolution Depth Learning From Videos With Dual Networks Tsinghua University :see_no_evil:
ICCV2019 Self-Supervised Monocular Depth Hints link Niantic :see_no_evil:
ICCV2019 Monodepth2: Digging into self-supervised monocular depth estimation link UCL/niantic :star2:
NIPS2019 SC-SfMLearner: Unsupervised scale-consistent depth and ego-motion learning from monocular video University of Adelaide, Australia :see_no_evil:
CVPR2019 Competitive Collaboration: Joint Unsupervised Learning of Depth, Camera Motion, Optical Flow and Motion Segmentation link Max Planck Institute for Intelligent Systems :see_no_evil:
CoRL2019 Robust Semi-Supervised Monocular Depth Estimation with Reprojected Distances Toyota Research Institute (TRI) :see_no_evil:

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2020

Conference Tittle code Author mark note
ECCV2020 DeepSFM: Structure From Motion Via Deep Bundle Adjustment Fudan University :see_no_evil:
ECCV2020 P2Net: Patch-match and Plane-regularization for Unsupervised Indoor Depth Estimation ShanghaiTech Univsertiy :see_no_evil:
ECCV2020 Feature-metric Loss for Self-supervised Learning of Depth and Egomotion link :see_no_evil:
CoRL2020 Unsupervised Monocular Depth Learning in Dynamic Scenes Google Research :see_no_evil:
CoRL2020 Attentional Separation-and-Aggregation Network for Self-supervised Depth-Pose Learning in Dynamic Scenes Tsinghua University :hear_no_evil:
3DV2020 Neural Ray Surfaces for Self-Supervised Learning of Depth and Ego-motion Toyota Research Institute (TRI)
ICLR2020 Semantically-Guided Representation Learning for Self-Supervised Monocular Depth Toyota Research Institute (TRI)
CVPR2020 On the uncertainty of self-supervised monocular depth estimation link University of Bologna, Italy :see_no_evil:
CVPR2020 Towards Better Generalization: Joint Depth-Pose Learning without PoseNet link Tsinghua University :see_no_evil: link
CVPR2020 3D Packing for Self-Supervised Monocular Depth Estimation Toyota Research Institute (TRI) :star2: link
CVPR2020 Self-supervised monocular trained depth estimation using self-attention and discrete disparity volume University of Adelaide :see_no_evil:
2020 SAFENet: Self-Supervised Monocular Depth Estimation with Semantic-Aware Feature Extraction link Toyota Research Institute (TRI) :see_no_evil:
2020 Self-Supervised Monocular Depth Estimation : Solving the Dynamic Object Problem by Semantic Guidance Technische Universit¨at Braunschweig, Germany :see_no_evil:
IROS2020 Toward Hierarchical Self-Supervised Monocular Absolute Depth Estimation for Autonomous Driving Applications link Tongji University :see_no_evil:

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2021

Conference Tittle code Author mark note
AAAI2021 HR-Depth : High Resolution Self-Supervised Monocular Depth Estimation link Zhejiang University :star: link
AAAI2021 Learning Monocular Depth in Dynamic Scenes via Instance-Aware Projection Consistency link KAIST :star: link
CVPR2021 Manydepth:The Temporal Opportunist: Self-Supervised Multi-Frame Monocular Depth link Niantic :see_no_evil:
CVPR2021 MonoRec: Semi-Supervised Dense Reconstruction in Dynamic Environments from a Single Moving Camera link TUM :see_no_evil:
IROS2021 Self-Supervised Scale Recovery for Monocular Depth and Egomotion Estimation link University of Toronto :see_no_evil:
2021 Self-supervised Depth Estimation Leveraging Global Perception and Geometric Smoothness Using On-board Videos Hong Kong Polytechnic University :see_no_evil:
2021 Self-Supervised Structure-from-Motion through Tightly-Coupled Depth and Egomotion Networks University of Toronto :see_no_evil:
2021 Moving SLAM: Fully Unsupervised Deep Learning in Non-Rigid Scenes HKUST :see_no_evil:
2021 Unsupervised Joint Learning of Depth, Optical Flow, Ego-motion from Video Tongji University :see_no_evil:
2021 Monocular Depth Estimation through Virtual-world Supervision and Real-world SfM Self-Supervision :see_no_evil:
2021 Self-Supervised Learning of Depth and Ego- Motion from Video by Alternative Training and Geometric Constraints from 3D to 2D :see_no_evil:
-update-time-09-13-2021-
ICCV2021 Fine-grained Semantics-aware Representation Enhancement for Self-supervised Monocular Depth Estimation Seoul National University :see_no_evil:
ICCV2021 Regularizing Nighttime Weirdness: Efficient Self-supervised Monocular Depth Estimation in the Dark Nanjing University of Science and Technology :see_no_evil:
ICCV2021 Self-supervised Monocular Depth Estimation for All Day Images using Domain Separation Zhejiang University :see_no_evil:
ICCV2021 StructDepth: Leveraging the structural regularities for self-supervised indoor depth estimation Shanghai Jiao Tong University :see_no_evil:
ICCV2021 MonoIndoor: Towards Good Practice of Self-Supervised Monocular Depth Estimation for Indoor Environments OPPO US Research Center :see_no_evil:
Sensors Journal 2021 Unsupervised Monocular Depth Perception: Focusing on Moving Objects Chinese University of Hong Kong :see_no_evil:
2021 R4Dyn: Exploring Radar for Self-Supervised Monocular Depth Estimation of Dynamic Scenes TUM :star:
2021 Unsupervised Monocular Depth Estimation in Highly Complex Environments East China University of Science and Technology :see_no_evil:
-update-time-10-13-2021-
3DV 2021 PLNet: Plane and Line Priors for Unsupervised Indoor Depth Estimation The Chinese University of Hong Kong :see_no_evil:
-update-time-11-29-2021-
2021 BMVC X-Distill: Improving Self-Supervised Monocular Depth via Cross-Task Distillation
2021 BMVC Self-Supervised Monocular Depth Estimation with Internal Feature Fusion
2021 3DV Attention meets Geometry: Geometry Guided Spatial-Temporal Attention for Consistent Self-Supervised Monocular Depth Estimation
ICCV2021 Excavating the Potential Capacity of Self-Supervised Monocular Depth Estimation Peking University :see_no_evil:
2021 SUB-Depth: Self-distillation and Uncertainty Boosting Self-supervised Monocular Depth Estimation

indoor

Conference Tittle code Author mark note
-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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3-Multi-view-stereo

Conference Tittle code Author mark
PAMI2008 SGM：Stereo processing by Semi-Global matching and Mutual Information German Aerospace Cente :see_no_evil:
ECCV2016 Unsupervised CNN for Single View Depth Estimation: Geometry to the Rescue University of Adelaide :see_no_evil:
CVPR2017 DispNet: Unsupervised Monocular Depth Estimation with Left-Right Consistency University College London :see_no_evil:
Cost Volume Pyramid Based Depth Inference for Multi-View Stereo Jiayu link Northwestern Polytechnical University :see_no_evil:
CVPR2020 Semi-Supervised Deep Learning for Monocular Depth Map Prediction Australian National University :see_no_evil:
AAAI2021 Self-supervised Multi-view Stereo via Effective Co-Segmentation and Data-Augmentation South China University of Technology :see_no_evil:
CVPR2021 Differentiable Diffusion for Dense Depth Estimation from Multi-view Images Brown University :see_no_evil:
ICCV2021 NerfingMVS: Guided Optimization of Neural Radiance Fields for Indoor Multi-view Stereo Australian National University :star:

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4-SLAM-Visual-Odometry

Conference Tittle code Author mark
ECCV2014 LSD-SLAM: Large-Scale Direct Monocular SLAM TUM :see_no_evil:
TR2015 ORB-SLAM: A Versatile and Accurate Monocular SLAM System Universidad de Zaragoza :see_no_evil:
2016 Direct Visual Odometry using Bit-Planes Carnegie Mellon University :see_no_evil:
TR2017 ORB-SLAM2: An Open-Source SLAM System for Monocular, Stereo, and RGB-D Cameras Universidad de Zaragoza :see_no_evil:
2016 A Photometrically Calibrated Benchmark For Monocular Visual Odometry TUM :see_no_evil:

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2018

Conference Tittle code Author mark
PAMI2018 DSO: Direct Sparse Odometry TUM :see_no_evil:
IROS2018 LDSO: Direct Sparse Odometry with Loop Closure TUM :see_no_evil:
ECCV2018 Deep Virtual Stereo Odometry:Leveraging Deep Depth Prediction for Monocular Direct Sparse Odometry TUM :see_no_evil:
2018 Self-improving visual odometry Magic Leap, Inc. :see_no_evil:

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2019

Conference Tittle code Author mark
ICLR2019 BA-NET: DENSE BUNDLE ADJUSTMENT NETWORKS Simon Fraser University :see_no_evil:
TartanVO: A Generalizable Learning-based VO link Carnegie Mellon University :see_no_evil:
IROS D2VO: Monocular Deep Direct Visual Odometry :see_no_evil:

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2020

Conference Tittle code Author mark
ECCV2020 Pseudo RGB-D for Self-Improving Monocular SLAM and Depth Prediction IIIT-Delhi :see_no_evil:
CVPR2020 VOLDOR: Visual Odometry from Log-logistic Dense Optical flow Residuals Stevens Institute of Technology :see_no_evil:
2021 Generalizing to the Open World: Deep Visual Odometry with Online Adaptation Peking University :see_no_evil:
ICRA2021 SA-LOAM: Semantic-aided LiDAR SLAM with Loop Closure Zhejiang University :see_no_evil:

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Semantic-aware-depth

Conference Tittle code Author mark
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

1-Monocular-depth with Cost Volume

Conference Tittle code Author mark note
NIPS2020 Forget About the LiDAR: Self-Supervised Depth Estimators with MED Probability Volumes Korea Advanced Institute of Science and Technology :hear_no_evil: link
CVPR2021 DRO: Deep Recurrent Optimizer for Structure-from-Motion Alibaba A.I. Labs :see_no_evil: link
CVPR2021 The Temporal Opportunist: Self-Supervised Multi-Frame Monocular Depth link Niantic :see_no_evil:
CVPR2020 Self-supervised Monocular Trained Depth Estimation using Self-attention and Discrete Disparity Volume link Australian Institute for Machine Learning :see_no_evil:

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Video-depth

Conference Tittle code Author mark
-update-time-10-13-2021-
CVPR 2020 Consistent Video Depth Estimation University of Washington
CVPR 2021 Robust Consistent Video Depth Estimation Facebook
SIGGRAPH 2021 Consistent Depth of Moving Objects in Video

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Virtual2Real-depth

Conference Tittle code Author mark

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NightTime-depth

Conference Tittle code Author mark
ICCV 2021 Regularizing Nighttime Weirdness: Efficient Self-supervised Monocular Depth Estimation in the Dark Nanjing University of Science and Technology :see_no_evil:
ICCV 2021 Self-supervised Monocular Depth Estimation for All Day Images using Domain Separation Zhejiang University :see_no_evil:
2021 Unsupervised Depth and Ego-motion Estimation for Monocular Thermal Video using Multi-spectral Consistency Loss KAIST :see_no_evil:

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Light-Filed-based-depth

Conference Tittle code Author mark
CVPR2021 Differentiable Diffusion for Dense Depth Estimation from Multi-view Images Brown University :see_no_evil:
IROS2021 Unsupervised Learning of Depth Estimation and Visual Odometry for Sparse Light Field Cameras The University of Sydney :see_no_evil:
2021 Occlusion-aware Unsupervised Learning of Depth from 4-D Light Fields University of Sydney :see_no_evil:

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6-depth-estimation-and-complementation

Conference Tittle code Author mark
Sparse Auxiliary Networks for Unified Monocular Depth Prediction and Completion Vitor Toyota Research Institute (TRI) :see_no_evil:
3DV2019 Enhancing self-supervised monocular depth estimation with traditional visual odometry Univrses AB :see_no_evil:
ECCV2020 S3Net: Semantic-aware self-supervised depth estimation with monocular videos and synthetic data UCSD :see_no_evil:
ICCV2021 Unsupervised Depth Completion with Calibrated Backprojection Layers Alex UCLA :see_no_evil:
-update-time-11-29-2021-
Self-Supervised Depth Completion for Active Stereo UCLA :see_no_evil:
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