Self-Supervised-depth

by kalilia.

Overview
Datasets
SfM based monocular depth
- 2017-2019
- 2020
- 2021
Multi-view-Stereo
Light-Field-based
SLAM-Odometry
depth complementation

0-depth-estimation-overview

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

*-datasets

Tittle	yaer	mark	note
Vision meets Robotics: The KITTI Dataset	2012		Karlsruhe Institute of Technology
nuScenes: A multimodal dataset for autonomous driving	2018		nuTonomy: an APTIV company

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	🙉	link
CVPR2021	DRO: Deep Recurrent Optimizer for Structure-from-Motion		Alibaba A.I. Labs	🙈	link
CVPR2021	The Temporal Opportunist: Self-Supervised Multi-Frame Monocular Depth	link	Niantic	🙈
CVPR2020	Self-supervised Monocular Trained Depth Estimation using Self-attention and Discrete Disparity Volume	link	Australian Institute for Machine Learning	🙈
ECCV2020	Feature-metric Loss for Self-supervised Learning of Depth and Egomotion	link		🙈

2-Mono-SfM

2017

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

2018

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

2019

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

2020

Conference	Tittle	code	Author	mark	note
ECCV2020	DeepSFM: Structure From Motion Via Deep Bundle Adjustment		Fudan University	🙈
CoRL2020	Unsupervised Monocular Depth Learning in Dynamic Scenes		Google Research	🙈
CoRL2020	Attentional Separation-and-Aggregation Network for Self-supervised Depth-Pose Learning in Dynamic Scenes		Tsinghua University	🙉
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	🙈
CVPR2020	Towards Better Generalization: Joint Depth-Pose Learning without PoseNet	link	Tsinghua University	🙈	link
CVPR2020	3D Packing for Self-Supervised Monocular Depth Estimation		Toyota Research Institute (TRI)	🌟	link
CVPR2020	Self-supervised monocular trained depth estimation using self-attention and discrete disparity volume		University of Adelaide	🙈
2020	SAFENet: Self-Supervised Monocular Depth Estimation with Semantic-Aware Feature Extraction	link	Toyota Research Institute (TRI)	🙈
2020	Self-Supervised Monocular Depth Estimation : Solving the Dynamic Object Problem by Semantic Guidance		Technische Universit¨at Braunschweig, Germany	🙈
IROS2020	Toward Hierarchical Self-Supervised Monocular Absolute Depth Estimation for Autonomous Driving Applications	link	Tongji University	🙈

2021

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

3-Multi-view-stereo

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

4-SLAM-Visual-Odometry

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

2018

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

2019

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

2020

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

Light-Filed-based-depth

Conference	Tittle	Author	mark
TPAMI2021	Revisiting Light Field Rendering with Deep Anti-Aliasing Neural Network	Northeastern University	🙈
CVPR2021	Differentiable Diffusion for Dense Depth Estimation from Multi-view Images	Brown University	🙈
IROS2021	Unsupervised Learning of Depth Estimation and Visual Odometry for Sparse Light Field Cameras	Brown University	🙈
2021	Occlusion-aware Unsupervised Learning of Depth from 4-D Light Fields	University of Sydney	🙈

6-depth-estimation-and-complementation

Conference	Tittle	Author	mark
	Sparse Auxiliary Networks for Unified Monocular Depth Prediction and Completion Vitor	Toyota Research Institute (TRI)	🙈
3DV2019	Enhancing self-supervised monocular depth estimation with traditional visual odometry	Univrses AB	🙈
ECCV2020	S3Net: Semantic-aware self-supervised depth estimation with monocular videos and synthetic data	UCSD	🙈

Self-Supervised depth kalilia

Related tags

Overview

Self-Supervised-depth

Contents

0-depth-estimation-overview

*-datasets

1-Monocular-depth with Cost Volume

2-Mono-SfM

2017

2018

2019

2020

2021

3-Multi-view-stereo

4-SLAM-Visual-Odometry

2018

2019

2020

Light-Filed-based-depth

6-depth-estimation-and-complementation

Owner

Official PyTorch implementation of "Contrastive Learning from Extremely Augmented Skeleton Sequences for Self-supervised Action Recognition" in AAAI2022.

Code for sound field predictions in domains with impedance boundaries. Used for generating results from the paper

Open-source python package for the extraction of Radiomics features from 2D and 3D images and binary masks.

A scikit-learn compatible neural network library that wraps PyTorch

[LREC] MMChat: Multi-Modal Chat Dataset on Social Media

A Closer Look at Invalid Action Masking in Policy Gradient Algorithms

Official implementation of "Accelerating Reinforcement Learning with Learned Skill Priors", Pertsch et al., CoRL 2020

Text-to-Music Retrieval using Pre-defined/Data-driven Emotion Embeddings

RAANet: Range-Aware Attention Network for LiDAR-based 3D Object Detection with Auxiliary Density Level Estimation

DSAC* for Visual Camera Re-Localization (RGB or RGB-D)

OSLO: Open Source framework for Large-scale transformer Optimization

Class-Attentive Diffusion Network for Semi-Supervised Classification [AAAI'21] (official implementation)

Official code for the paper "Why Do Self-Supervised Models Transfer? Investigating the Impact of Invariance on Downstream Tasks".

Pre-Training 3D Point Cloud Transformers with Masked Point Modeling

CoReD: Generalizing Fake Media Detection with Continual Representation using Distillation (ACMMM'21 Oral Paper)

This is an unofficial implementation of the paper “Student-Teacher Feature Pyramid Matching for Unsupervised Anomaly Detection”.

Deep Structured Instance Graph for Distilling Object Detectors (ICCV 2021)

Transformers provides thousands of pretrained models to perform tasks on different modalities such as text, vision, and audio.

Deep Learning Interviews book: Hundreds of fully solved job interview questions from a wide range of key topics in AI.

PyTorch implementation of popular datasets and models in remote sensing