**:running::running::running: TODO: ** Add a description of the highlights for each paper and attach an open source link if it exists.

If you find something wrong or want to add something new, don't hesitate to make an issue or a PR.

This repo is used to record algorithms constructed by neural networks, either about a complete SLAM system, or part of it.

In addition, some related resources, such as hyperlinks to open-source codes and PDF files, are also welcome.

Topics & Quick jump:

• SLAM System
• Self-supervised Structure-from-Motion
• Depth Estimation
• Visual Odometry
• Visual-Inertial Odometry
• Feature Representation
• Camera Location
• Place Recognition (Loop Detection)
• Mapping and Map Compression
• Optimization

• [ICRA 2019] GEN-SLAM: Generative Modeling for Monocular Simultaneous Localization and Mapping

• [CVPR 2017] CNN-SLAM: Real-time dense monocular SLAM with learned depth prediction

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• [ICCV 2019] Self-Supervised Monocular Depth Hints
• [ICCV 2019] Depth from Videos in the Wild: Unsupervised Monocular Depth Learning from Unknown Cameras
• [ICCV 2019] Exploiting temporal consistency for real-time video depth estimation
• [ICCV 2019] Digging Into Self-Supervised Monocular Depth Estimation
• [ICCV 2019] Unsupervised High-Resolution Depth Learning From Videos With Dual Networks
• [ICCV 2019] SynDeMo: Synergistic Deep Feature Alignment for Joint Learning of Depth and Ego-Motion
• [ICCV 2019] Enforcing geometric constraints of virtual normal for depth prediction
• [ICCV 2019] Self-supervised Learning with Geometric Constraints in Monocular Video Connecting Flow, Depth, and Camera
• [ICCV 2019] Moving Indoor: Unsupervised Video Depth Learning in Challenging Environments
• [ICCV 2019] Sequential Adversarial Learning for Self-Supervised Deep Visual Odometry
• [CoRL 2019] Two Stream Networks for Self-Supervised Ego-Motion Estimation
• [IROS 2019] Learning Residual Flow as Dynamic Motion from Stereo Videos
• [NeurIPS 2019] Unsupervised Scale-consistent Depth and Ego-motion Learning from Monocular Video
• [ICRA 2019] Unsupervised Learning of Monocular Depth and Ego-Motion Using Multiple Masks
• [ICRA 2019] GANVO - Unsupervised Deep Monocular Visual Odometry and Depth Estimation with Generative Adversarial Networks
• [CVPR 2019] Competitive Collaboration: Joint Unsupervised Learning of Depth, Camera Motion, Optical Flow and Motion Segmentation
• [CVPR 2019] UnOS: Unified Unsupervised Optical-flow and Stereo-depth Estimation by Watching Videos
• [AAAI 2019] Depth Prediction Without the Sensors: Leveraging Structure for Unsupervised Learning from Monocular Videos
• [3DV 2019] Structured Coupled Generative Adversarial Networks for Unsupervised Monocular Depth Estimation
• [Arxiv 2019] Flow-Motion and Depth Network for Monocular Stereo and Beyond

• [ECCV 2018] DF-Net: Unsupervised Joint Learning of Depth and Flow using Cross-Task Consistency
• [CVPR 2018] Unsupervised Learning of Monocular Depth Estimation and Visual Odometry with Deep Feature Reconstruction
• [CVPR 2018] GeoNet: Unsupervised Learning of Dense Depth, Optical Flow and Camera Pose
• [IROS 2018] UnDEMoN: Unsupervised Deep Network for Depth and Ego-Motion Estimation

• [CVPR 2017] Unsupervised Learning of Depth and Ego-Motion from Video

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• [ICCV 2019] How do neural networks see depth in single images?
• [ICCV 2019] Visualization of Convolutional Neural Networks for Monocular Depth Estimation
• [ICCV 2019] Enforcing geometric constraints of virtual normal for depth prediction
• [TPAMI 2019] Progressive Fusion for Unsupervised Binocular Depth Estimation using Cycled Networks
• [CVPR 2019] Student Becoming the Master: Knowledge Amalgamation for Joint Scene Parsing, Depth Estimation, and More
• [CVPR 2019] Learning Monocular Depth Estimation Infusing Traditional Stereo Knowledge
• [CVPR 2019] CAM-Convs: Camera-Aware Multi-Scale Convolutions for Single-View Depth
• [CVPR 2019] Veritatem Dies Aperit-Temporally Consistent Depth Prediction Enabled by a Multi-Task Geometric and Semantic Scene Understanding Approach
• [CVPR 2019] Pseudo-LiDAR from Visual Depth Estimation: Bridging the Gap in 3D Object Detection for Autonomous Driving
• [CVPR 2019] Bilateral Cyclic Constraint and Adaptive Regularization for Unsupervised Monocular Depth Prediction
• [CVPR 2019] Towards Scene Understanding: Unsupervised Monocular Depth Estimation with Semantic-aware Representation
• [CVPR 2019] Geometry-Aware Symmetric Domain Adaptation for Monocular Depth Estimation
• [CVPR 2019] Recurrent MVSNet for High-resolution Multi-view Stereo Depth Inference
• [CVPR 2019] Refine and Distill: Exploiting Cycle-Inconsistency and Knowledge Distillation for Unsupervised Monocular Depth Estimation
• [CVPR 2019] Neural RGB->D Sensing: Depth and Uncertainty from a Video Camera
• [Arxiv 2019] Attention-based Context Aggregation Network for Monocular Depth Estimation

• [ICRA 2018] Just-in-Time Reconstruction: Inpainting Sparse Maps Using Single View Depth Predictors as Priors
• [CVPR 2018] Learning for Disparity Estimation through Feature Constancy
• [CVPR 2018] Deep Ordinal Regression Network for Monocular Depth Estimation
• [CVPR 2018] Learning Depth from Monocular Videos using Direct Methods
• [CVPR 2018] Structured Attention Guided Convolutional Neural Fields for Monocular Depth Estimation
• [CVPR 2018 Workshop] On the Importance of Stereo for Accurate Depth Estimation: An Efficient Semi-Supervised Deep Neural Network Approach
• [ECCV 2018] Learning Monocular Depth by Distilling Cross-domain Stereo Networks
• [ECCV 2018] Look Deeper into Depth: Monocular Depth Estimation with Semantic Booster and Attention-Driven Loss

• [ICCV 2017] End-to-End Learning of Geometry and Context for Deep Stereo Regression
• [CVPR 2017] Unsupervised Monocular Depth Estimation with Left-Right Consistency

• [ECCV 2016] Unsupervised CNN for Single View Depth Estimation: Geometry to the Rescue
• [NeurIPS 2014] Depth Map Prediction from a Single Image Using a Multi-scale Deep Network

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• [ICRA 2020] Visual Odometry Revisited: What Should Be Learnt?

• [CVPR 2019] MagicVO: End-to-End Monocular Visual Odometry through Deep Bi-directional Recurrent Convolutional Neural Network
• [CVPR 2019] Understanding the Limitations of CNN-based Absolute Camera Pose Regression
• [CVPR 2019] Beyond Tracking: Selecting Memory and Refining Poses for Deep Visual Odometry
• [ICRA 2019] Learning Monocular Visual Odometry through Geometry-Aware Curriculum Learning

• [ICRA 2018] Deep Auxiliary Learning for Visual Localization and Odometry
• [ICRA 2018] UnDeepVO: Monocular Visual Odometry through Unsupervised Deep Learning
• [CVPR 2018 Workshop] Geometric Consistency for Self-Supervised End-to-End Visual Odometry
• [IJRR 2018] End-to-end, sequence-to-sequence probabilistic visual odometry through deep neural networks

• [ICRA 2017] DeepVO: Towards end-to-end visual odometry with deep Recurrent Convolutional Neural Networks
• [IROS 2017] Deep regression for monocular camera-based 6-DoF global localization in outdoor environments

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• [CVPR 2019] Selective Sensor Fusion for Neural Visual-Inertial Odometry

• [IROS 2018] Vision-Aided Absolute Trajectory Estimation Using an Unsupervised Deep Network with Online

• [AAAI 2017] VINet: Visual-Inertial Odometry as a Sequence-to-Sequence Learning ProblemError Correction

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• [3DV 2019] SIPs: Succinct Interest Points from Unsupervised Inlierness Probability Learning

• [CVPR 2018 Workshop] SuperPoint: Self-Supervised Interest Point Detection and Description

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• [Arxiv 2019] AtLoc: Attention Guided Camera Localization
• [Arxiv 2019] Hierarchical Joint Scene Coordinate Classification and Regression for Visual Localization

• [ICRA 2018] Deep Auxiliary Learning for Visual Localization and Odometry

• [IROS 2017] Deep regression for monocular camera-based 6-DoF global localization in outdoor environments

• [ICCV 2015] PoseNet: A Convolutional Network for Real-Time 6-DOF Camera Relocalization

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• [CVPR 2016] NetVLAD: CNN Architecture for Weakly Supervised Place Recognition

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• [ICRA 2019] Pose Graph Optimization for Unsupervised Monocular Visual Odometry Back to top