The post contains papers-with-code about SLAM, Pose/Object tracking, Depth/Disparity/Flow Estimation, 3D-graphic, Machine Learning, Deep Learning etc.

Here is the repo：Recent-Stars-2022

I posted the content of the repo as follows.

SLAM related

• [SLAM][ORB-SLAM3: An Accurate Open-Source Library for Visual, Visual-Inertial and Multi-Map SLAM](https://github.com/UZ-SLAMLab/ORB\_SLAM3), [PDF]
• [SLAM][LIO-SAM](https://github.com/TixiaoShan/LIO-SAM), 激光雷达IMU紧耦合SLAM
• [Tool][Robotics Toolbox for Python](https://github.com/petercorke/robotics-toolbox-python), a Python implementation of the Robotics Toolbox for MATLAB®
• [Matching][LISRD](https://github.com/rpautrat/LISRD),ECCV 2020, [PDF]，在线局部不变特征匹配！重要！
• [Matching][AdaLAM](https://github.com/cavalli1234/AdaLAM),特征匹配快速滤除外点
• [Calib][fisheye_pinhole_calib_demo](https://github.com/3DCVer/fisheye\_pinhole\_calib\_demo), 包括鱼眼模型、针孔模型的相机标定，封装了自动编译、库的打包以及外部库的调用测试
• [Calib][SensorCalibration](https://github.com/FENGChenxi0823/SensorCalibration), IMU雷达标定
• [VO][Low-Drift Visual Odometry in Structured Environments by Decoupling Rotational and Translational Motion](https://github.com/PyojinKim/LPVO),ICRA 2018, [PDF], 结构化环境中将旋转量与平移量进行分离优化
• [VIO][VIO-SLAM](https://github.com/iamwangyabin/VIO-SLAM), 从零开始手写VIO课后作业
• [Matching][TFMatch: Learning-based image matching in TensorFlow](https://github.com/lzx551402/tfmatch),TensorFlow 实现的 GeoDesc,ASLFeat以及ContextDesc
• [Tutorial][SLAM-BOOK](https://github.com/yanyan-li/SLAM-BOOK), 一本关于SLAM的书稿，清楚的介绍SLAM系统中的使用的几何方法和深度学习方法，持续更新中
• [Loop Closing][OverlapNet - Loop Closing for 3D LiDAR-based SLAM](https://github.com/PRBonn/OverlapNet), RSS 2020, [PDF], 3D激光雷达SLAM闭环
• [SLAM][VDO_SLAM](https://github.com/halajun/VDO\_SLAM), RGB-D相机数据作为输入，实现追踪动态物体SLAM的功能, [PDF]
• [SLAM][orbslam-map-saving-extension](https://github.com/TUMFTM/orbslam-map-saving-extension)，在ORB-SLAM的基础上增加保存+加载地图功能
• [Tutorial][Modern Robotics: Mechanics, Planning, and Control Code Library](https://github.com/NxRLab/ModernRobotics), 现代机器人学, [Homepage]
• [Matching][image-matching-benchmark-baselines](https://github.com/vcg-uvic/image-matching-benchmark-baselines), 图像特征匹配挑战赛主页
• [Matching][GraphLineMatching](https://github.com/mameng1/GraphLineMatching)
• [Matching][Locality Preserving Matching](https://github.com/jiayi-ma/LPM), IJCAI 2017, [PDF]
• [IMU][IMUOrientationEstimator](https://github.com/ydsf16/IMUOrientationEstimator)
• [Feature][BEBLID: Boosted Efficient Binary Local Image Descriptor](https://github.com/iago-suarez/BEBLID)
• [Relocalization][KFNet: Learning Temporal Camera Relocalization using Kalman Filtering](https://github.com/zlthinker/KFNet),CVPR 2020,[PDF]
• [Matching][image-matching-benchmark](https://github.com/vcg-uvic/image-matching-benchmark)
• [Matching][GMS: Grid-based Motion Statistics for Fast, Ultra-robust Feature Correspondence](https://github.com/JiawangBian/GMS-Feature-Matcher),CVPR 17 & IJCV 19,[PDF],[Project page]
• [Reloc][GN-Net-Benchmark](https://github.com/Artisense-ai/GN-Net-Benchmark), CVPR 2020,GN-Net: The Gauss-Newton Loss for Multi-Weather Relocalization, [PDF],[Project page]
• [Matching][SuperGluePretrainedNetwork](https://github.com/magicleap/SuperGluePretrainedNetwork), CVPR 2020, [PDF], 划重点！2020年sota超大视角2D特征匹配，Blog
• [Feature][D3Feat](https://github.com/XuyangBai/D3Feat), CVPR 2020, [PDF]
• [Feature][ASLFeat](https://github.com/lzx551402/ASLFeat), CVPR 2020, ASLFeat: Learning Local Features of Accurate Shape and Localization, [PDF]
• [Feature][GMS-Feature-Matcher](https://github.com/XuyangBai/D3Feat), CVPR 2018, GMS: Grid-based Motion Statistics for Fast, Ultra-robust Feature Correspondence, [PDF],[Project page]
• [Feature][D3Feat](https://github.com/XuyangBai/D3Feat), CVPR 2020, [PDF]
• [Feature][3DFeatNet](https://github.com/yewzijian/3DFeatNet), ECCV 2018, [PDF]
• [Tutorial][AutonomousDrivingCookbook](https://github.com/microsoft/AutonomousDrivingCookbook)，Scenarios, tutorials and demos for Autonomous Driving
• [Tutorial][SLAMPaperReading](https://github.com/PaoPaoRobot/SLAMPaperReading)，泡泡机器人北京线下SLAM论文分享资料
• [Tutorial][VIO_Tutotial_Course](https://github.com/lishuwei0424/VIO\_Tutotial\_Course)
• [Tutorial][VO-SLAM-Review](https://github.com/MichaelBeechan/VO-SLAM-Review)
• [Tutorial][VINS-Mono-code-annotation](https://github.com/QingSimon/VINS-Mono-code-annotation),VINS-Mono代码注释以及公式推导
• [Tutorial][VINS-Mono-Learning](https://github.com/ManiiXu/VINS-Mono-Learning),VINS-Mono代码注释
• [Tutorial][VINS-Course](https://github.com/HeYijia/VINS-Course),VINS-Mono code without Ceres or ROS
• [Tutorial][VIO-Doc](https://github.com/StevenCui/VIO-Doc),主流VIO论文推导及代码解析
• [VO][CNN-DSO](https://github.com/muskie82/CNN-DSO), Direct Sparse Odometry with CNN Depth Prediction
• [VO][fisheye-ORB-SLAM](https://github.com/lsyads/fisheye-ORB-SLAM), A real-time robust monocular visual SLAM system based on ORB-SLAM for fisheye cameras, without rectifying or cropping the input images
• [VO][ORB_Line_SLAM](https://github.com/robotseu/ORB\_Line\_SLAM), Real-Time SLAM with BoPLW Pairs for Stereo Cameras, with Loop Detection and Relocalization Capabilities
• [VO][DeepVO-pytorch](https://github.com/ChiWeiHsiao/DeepVO-pytorch.git), ICRA 2017 DeepVO: Towards end-to-end visual odometry with deep Recurrent Convolutional Neural Networks
• [Calib][CamOdomCalibraTool](https://github.com/MegviiRobot/CamOdomCalibraTool), The tool to calibrate extrinsic param between camera and wheel.
• [Calib][lidar_camera_calibration](https://github.com/heethesh/lidar\_camera\_calibration),[another version](https://github.com/ankitdhall/lidar_camera_calibration)
• [Calib][OdomLaserCalibraTool](https://github.com/MegviiRobot/OdomLaserCalibraTool.git)，相机与2D雷达标定
• [Calib][extrinsic_lidar_camera_calibration](https://github.com/UMich-BipedLab/extrinsic\_lidar\_camera\_calibration), LiDARTag: A Real-Time Fiducial Tag using Point Clouds, arXiv 2019, [PDF]
• [Calib][velo2cam_calibration](https://github.com/beltransen/velo2cam\_calibration), Automatic Calibration algorithm for Lidar-Stereo camera, [Project page]
• [Dataset][IRS: A Large Synthetic Indoor Robotics Stereo Dataset for Disparity and Surface Normal Estimation](https://github.com/HKBU-HPML/IRS.git)
• [Tools][averaging-quaternions](https://github.com/christophhagen/averaging-quaternions),四元数平均

分割线，以下是2019年的星标项目，上面是2020年新星标的。

• R2D2: Reliable and Repeatable Detector and Descriptor,NeurIPS 2019,[PDF],[Project page]，深度学习特征点+描述子
• Semantic_SLAM,语义SLAM：ROS + ORB SLAM + PSPNet101
• PlaceRecognition-LoopDetection, Light-weight place recognition and loop detection using road markings
• DOOR-SLAM: Distributed, online, and outlier resilient SLAM for robotic teams,[PDF],[Project page]，多机器人协作SLAM，增强了场景的适用性
• awesome-local-global-descriptor, 超详细深度学习特征点描述子集合，需要重点关注一下这个repo
• GIFT: Learning Transformation-Invariant Dense Visual Descriptors via Group CNNs, NeurIPS 2019，[PDF], [Project page]，浙大CAD+商汤联合实验室出品，利用Group CNN来改进superpoint描述子（仅描述，特征点提取可任意选择），可以大幅度增强视角变化时的特征点复检率与匹配点数
• Key.Net: Keypoint Detection by Handcrafted and Learned CNN Filters,ICCV 2019, [PDF], 深度学习特征点
• Self-Supervised 3D Keypoint Learning for Ego-motion Estimation,[PDF],[Youtube], 深度学习特征点
• VINS-Mono-Optimization, 实现点线紧耦合优化的VINS-Mono
• msckf_vio注释版本
• NetVLAD-pytorch, NetVLAD场景识别的pytorch实现
• High-Precision Localization Using Ground Texture (Micro-GPS),ECCV 2018,[PDF],[Project page],[code]，地向（摄像机朝向地面）SLAM，获得高精度重定位效果。
• PlaneSLAM, Paper: “On the Representation of Planes for Efficient Graph-based SLAM with High-level Features”
• XIVO: X Inertial-aided Visual Odometry and Sparse Mapping, an open-source repository for visual-inertial odometry/mapping.
• DeepTAM,ECCV 2018,[PDF],[Project page],a learnt system for keyframe-based dense camera tracking and mapping.
• iRotAvg, Why bundle adjust?,ICRA 2019,[PDF]
• Tactical Rewind: Self-Correction via Backtracking in Vision-and-Language Navigation,CVPR 2019,[PDF]，视觉+语言导航
• DOOR-SLAM
• An Evaluation of Feature Matchers for Fundamental Matrix Estimation,BMVC 2019,[PDF],[Project Page]，特征匹配
• A Tightly Coupled 3D Lidar and Inertial Odometry and Mapping Approach,ICRA 2019,[PDF],[Project Page]，紧耦合雷达+IMU SLAM
• On the Representation of Planes for Efficient Graph-based SLAM with High-level Features,利用平面信息的SLAM
• Visual Odometry Revisited: What Should Be Learnt?,arXiv 2019,[PDF], 深度学习深度+光流进行VO
• RF-Net: An End-to-End Image Matching Network based on Receptive Field,CVPR 2019,[PDF], 端到端图像匹配
• Fast-Planner,IEEE Robotics and Automation Letters (RA-L), 2019,[PDF], 无人机轨迹生成
• A general and flexible factor graph non-linear least square optimization framework,CoRR 2019,[PDF],[Project Page]
• Demo for Kalman filter in ranging system,卡尔曼滤波原理演示
• A Holistic Visual Place Recognition Approach using Lightweight CNNs for Severe ViewPoint and Appearance Changes，场景识别（外观与视角变化时）,训练和部署源码
• SIPs: Succinct Interest Points from Unsupervised Inlierness Probability Learning,3D Vision (3DV) 2019,[PDF]，RPG实验室出品，深度学习特征点（有特征描述子）
• Matching Features Without Descriptors: Implicitly Matched Interest Points,BMVC 2019,[PDF],RPG实验室出品，无需特征描述即可进行特征匹配
• Learning Lightweight Lane Detection CNNs by Self Attention Distillation (ICCV 2019),ICCV 2019,[PDF]，深度学习道路检测
• Awesome SLAM Datasets,史上最全SLAM数据集， 公众号说明: 最全 SLAM 开源数据集
• GNSS-INS-SIM,惯导融合模拟器，支持IMU数据，轨迹生成等
• Multi-Sensor Combined Navigation Program(GNSS, IMU, Camera and so on) 多源多传感器融合定位 GPS/INS组合导航
• SOSNet: Second Order Similarity Regularization for Local Descriptor Learning,CVPR 2019,[Project page] [Paper] [Poster] [Slides]，一种深度学习特征描述子
• Look No Deeper: Recognizing Places from Opposing Viewpoints under Varying Scene Appearance using Single-View Depth Estimation,ICRA 2019,[PDF],利用深度图像实现了大视角长时间的场景识别（根据深度图筛选得到不同深度层次的特征点然后与当前帧进行匹配，提高了场景召回率）
• CALC2.0,Convolutional Autoencoder for Loop Closure 2.0,用于闭环检测
• SegMap,RSS 2018,[PDF], 一种基于3D线段的地图表示，可用于场景识别/机器人定位/环境重建等
• MSCKF_VIO, a stereo version of MSCKF，基于MSCKF的双目VIO
• NetVLAD: CNN architecture for weakly supervised place recognition，CVPR 2016, CNN框架弱监督学习场景识别,[Project Page]
• easy_handeye,Simple, straighforward ROS library for hand-eye calibration
• SuperPoint-SLAM,利用SuperPoint替换ORB特征点
• PyRobot: An Open Source Robotics Research Platform
• From Coarse to Fine: Robust Hierarchical Localization at Large Scale with HF-Net,[PDF]
• Super fast implementation of ICP in CUDA
• A generic interface for disparity map and pointcloud insertion
• SPHORB: A Fast and Robust Binary Feature on the Sphere,International Journal of Computer Vision 2015,[PDF],[Project Page]
• BADSLAM: Bundle Adjusted Direct RGB-D SLAM,CVPR 2019,[PDF]
• High Speed and High Dynamic Range Video with an Event Camera,arXiv,[PDF],[Project Page]
• Awesome-VIO,Discuss about VIO in PaoPaoRobot group
• GyroAllan,陀螺仪随机误差的 Allan 方差分析, Another version
• Self-supervised Sparse-to-Dense: Self-supervised Depth Completion from LiDAR and Monocular Camera,ICRA 2019,[PDF], 优化LiDAR以及单目得到的深度图
• PlaneRCNN: 3D Plane Detection and Reconstruction from a Single Image,CVPR 2019,[PDF],[Project Page],通过单幅图像进行3D平面检测以及重建
• DBow3,注释版的DBow3代码
• Visual-Inertial Mapping with Non-Linear Factor Recovery,[PDF],[Project Page], 时空联合的VIO优化方案
• ICRA2019-paper-list,ICRA 2019论文列表（泡泡机器人出品暂时无链接）
• Fast Cylinder and Plane Extraction from Depth Cameras for Visual Odometry, IROS 2018,[PDF],利用深度图进行圆柱检测以及平面检测进行VO
• Solutions to assignments of Robot Mapping Course WS 2013/14 by Dr. Cyrill Stachniss at University of Freiburg,SLAM算法学习课后作业答案
• Direct sparse odometry combined with stereo cameras and IMU,双目DSO+IMU
• Direct Sparse Odometry with Stereo Cameras,双目DSO
• Python binding of SLAM graph optimization framework g2o,python版本的g2o实现
• SuperPoint: Self-Supervised Interest Point Detection and Description, CVPR 2018, [Paper], 深度学习描述子+描述
• ContextDesc: Local Descriptor Augmentation with Cross-Modality Context, CVPR 2019, [Paper], 深度学习描述子
• D2-Net: A Trainable CNN for Joint Description and Detection of Local Features, CVPR 2019, [Paper], [Project Page], 深度学习关键点+描述
• ROS interface for ORBSLAM2,ROS版本的ORBSLAM2
• CNN-SVO: Improving the Mapping in Semi-Direct Visual Odometry Using Single-Image Depth Prediction， [Paper]
• VINS-Mono-Learning，代码注释版VINS-Mono，初学者学习
• OpenVSLAM: Versatile Visual SLAM Framework, [Project Page]
• RESLAM: A real-time robust edge-based SLAM system, ICRA 2019, [Paper]
• PL-SLAM: a Stereo SLAM System through the Combination of Points and Line Segments, [Paper]，线特征SLAM
• Good Line Cutting: towards Accurate Pose Tracking of Line-assisted VO/VSLAM, ECCV 2018, [Project Page], 改进的PL-SLAM
• Spherical Regression: Learning Viewpoints, Surface Normals and 3D Rotations on n-Spheres, CVPR 2019, [Paper]
• svo_edgelet, 在线轨迹生成
• Drone SLAM project for Caltech’s ME 134 Autonomy class, [PDF]
• Online Trajectory Generation of a MAV for Chasing a Moving Target in 3D Dense Environments, [Paper]
• PythonRobotics,[Paper], CppRobotics
• Bundle adjustment demo using Ceres Solver, [Blog], ceres实现BA
• CubeSLAM: Monocular 3D Object Detection and SLAM, [Paper]
• PointRCNN: 3D Object Proposal Generation and Detection from Point Cloud, CVPR 2019, [Paper]
• GIST-Global Image Descriptor, GIST描述子
• mav voxblox planning, MAV planning tools using voxblox as the map representation.
• Python Kalman Filter, 30行实现卡尔曼滤波
• vicalib, 视觉惯导系统标定工具
• BreezySLAM, 基于雷达的SLAM，支持Python(&Matlab, C++, and Java)
• Probabilistic-Robotics, 《概率机器人》中文版，书和课后习题
• Stanford Self Driving Car Code, [Paper], 斯坦福自动驾驶车代码
• Udacity Self-Driving Car Engineer Nanodegree projects
• Artificial Intelligence in Automotive Technology, TUM自动驾驶技术中的人工智能课程
• DeepMatchVO: Beyond Photometric Loss for Self-Supervised Ego-Motion Estimation,ICRA 2019, [Paper]
• GSLAM: A General SLAM Framework and Benchmark, CVPR 2019, [Paper], 集成了各种传感器输入的SLAM统一框架
• Visual-Odometric Localization and Mapping for Ground Vehicles Using SE(2)-XYZ Constraints，ICRA 2019,基于SE(2)-XYZ约束的VO系统
• Simple bag-of-words loop closure for visual SLAM, [Blog], 回环
• FBOW (Fast Bag of Words), an extremmely optimized version of the DBow2/DBow3 libraries,优化版本的DBow2/DBow3
• Multi-State Constraint Kalman Filter (MSCKF) for Vision-aided Inertial Navigation(master’s thesis)
• MSCKF, MSCKF中文注释版
• Calibration algorithm for a camera odometry system, VO系统的标定程序
• Modified version of VINS-Mono, 注释版本VINS Mono
• Extreme Relative Pose Estimation for RGB-D Scans via Scene Completion,[Paper]
• Implementation of EPnP algorithm with Eigen,利用Eigen编写的EPnP
• Real-time SLAM system with deep features, 深度学习描述子(ORB vs. GCNv2)
• Unsupervised Learning of Monocular Depth Estimation and Visual Odometry with Deep Feature Reconstruction, CVPR 2018, 无监督单目深度恢复以及VO
• ORB-SLAM-windows, Windows版本的ORB-SLAM
• StructVIO : Visual-inertial Odometry with Structural Regularity of Man-made Environments,[Project Page]
• KalmanFiltering, 各种卡尔曼滤波器的demo
• Stereo Odometry based on careful Feature selection and Tracking, [Paper], C++ OpenCV实现SOFT
• Visual SLAM with RGB-D Cameras based on Pose Graph Optimization
• Multi-threaded generic RANSAC implemetation, 多线程RANSAC
• Visual Odometry with Drift-Free Rotation Estimation Using Indoor Scene Regularities, BMVC 2017, [Project Page]，利用平面正交信息进行VO
• ICE-BA, CVPR 2018, [Paper]
• GraphSfM: Robust and Efficient Graph-based Structure from Motion, [Project Page]
• LOAM_NOTED, loam中文注解版
• Divide and Conquer: Effcient Density-Based Tracking of 3D Sensors in Manhattan Worlds,ACCV 2016,[Project Page],曼哈顿世界利用深度传感器进行旋转量平移量分离优化
• Real-time Manhattan World Rotation Estimation in 3D,IROS 2015,实时曼哈顿世界旋转估计
• Event-based Vision Resources，关于事件相机的资源
• AutonomousVehiclePaper，无人驾驶相关论文速递
• Segmentation.X, Segmentation相关论文&代码
• CVPR-2019, CVPR 2019 论文开源项目合集
• awesome-slam, SLAM合集
• awesome-visual-slam, 视觉SLAM合集
• Papers with code, 周更论文with代码
• Awesome Human Pose Estimation,awesome-object-pose, 位姿估计合集
• MVision, 大礼包：机器人视觉 移动机器人 VS-SLAM ORB-SLAM2 深度学习目标检测 yolov3 行为检测 opencv PCL 机器学习 无人驾驶

Pose/Object tracking

• Unsupervised person re-identification by soft multilabel learning,CVPR 2019, [Paper]
• FCOS: Fully Convolutional One-Stage Object Detection,ICCV 2019, [Paper]
• Hand Detection and Orientation Estimation
• Spatial-Temporal Person Re-identification,AAAI 2019,[Paper]
• A tiny, friendly, strong pytorch implement of person re-identification baseline. Tutorial,CVPR 2019, [Paper]
• Progressive Pose Attention for Person Image Generation,CVPR 2019,[Paper]
• FSA-Net: Learning Fine-Grained Structure Aggregation for Head Pose Estimation from a Single Image, CVPR 2019,[Paper]
• An unoffical implemention for paper “Fast Human Pose Estimation”, CVPR 2019,[Paper]
• Real-time single person pose estimation for Android and iOS,手机端实现人体位姿估计
• Basics of 2D and 3D Human Pose Estimation,人体姿态估计入门
• Libra R-CNN: Towards Balanced Learning for Object Detection
• High-resolution networks (HRNets) for object detection, [Paper]
• Learning Correspondence from the Cycle-Consistency of Time, CVPR 2019, [Paper]
• PVNet: Pixel-wise Voting Network for 6DoF Pose Estimation, CVPR 2019, [Paper], [Project Page]
• Self-Supervised Learning of 3D Human Pose using Multi-view Geometry, CVPR 2018, [Paper]
• PifPaf: Composite Fields for Human Pose Estimation, [Paper]
• Deep High-Resolution Representation Learning for Human Pose Estimation,CVPR 2019, [Paper], [Project Page]
• PoseFlow: Efficient Online Pose Tracking), BMVC 2018, [Paper]
• A Bottom-Up Clustering Approach to Unsupervised Person Re-identification，AAAI 2019, 重定位
• Fast Online Object Tracking and Segmentation: A Unifying Approach,CVPR 2019,[Paper] [Video] [Project Page]
• SimpleDet - A Simple and Versatile Framework for Object Detection and Instance Recognition,[Paper]

Depth/Disparity & Flow estimation

• [Depth][SemiGlobalMatching](https://github.com/ethan-li-coding/SemiGlobalMatching), SGM双目立体匹配算法完整实现，代码规范，注释丰富且清晰，CSDN同步教学
• PointMVSNet: Point-based Multi-view Stereo Network,ICCV 2019,[Paper]
• DeepLiDAR,CVPR 2019, [Paper], 单张RGB图像+稀疏雷达数据进行室外场景深度估计
• Real-Time Monocular Depth Estimation using Synthetic Data with Domain Adaptation via Image Style Transfer,CVPR 2018, [Paper]
• Learning Single-Image Depth from Videos using Quality Assessment Networks,CVPR 2019, [Paper], [Project Page]
• SCDA: Adapting Object Detectors via Selective Cross-Domain Alignment,CVPR 2019, [Paper], [Project Page]
• Learning monocular depth estimation infusing traditional stereo knowledge,CVPR 2019,[PDF]
• HPLFlowNet: Hierarchical Permutohedral Lattice FlowNet for Scene Flow Estimation on Large-scale Point Clouds,CVPR 2019,[Paper]
• GA-Net: Guided Aggregation Net for End-to-end Stereo Matching,CVPR 2019,[Paper]
• DPSNet: End-to-end Deep Plane Sweep Stereo,ICLR 2019,[Paper]
• Fast Depth Densification for Occlusion-aware Augmented Reality, SIGGRAPH-Asia 2018, [Project Page],another version
• Learning To Adapt For Stereo, CVPR 2019, [Paper]
• Pyramid Stereo Matching Network,[Paper]
• Bridging Stereo Matching and Optical Flow via Spatiotemporal Correspondence, [Paper]
• Sparse Depth Completion, [Paper], RGB图像辅助雷达深度估计
• GASDA, CVPR 2019, [Paper]
• MVSNet: Depth Inference for Unstructured Multi-view Stereo, [Paper], 非官方实现版本的MVSNet
• Stereo R-CNN based 3D Object Detection for Autonomous Driving, CVPR 2019, [Paper]
• Real-time self-adaptive deep stereo, CVPR 2019, [Paper]
• High Quality Monocular Depth Estimation via Transfer Learning,CVPR 2019, [Paper], [Project Page]
• Group-wise Correlation Stereo Network,CVPR 2019, [Paper]
• DeepMVS: Learning Multi-View Stereopsis, CVPR 2018,[Project Page],多目深度估计
• FlowNet 2.0: Evolution of Optical Flow Estimation with Deep Networks, CVPR 2017, 深度学习光流恢复
• StereoVision-ADCensus,深度恢复代码集合(ADCensus, SGBM, BM)
• SegStereo: Exploiting Semantic Information for Disparity Estimation, 探究语义信息在深度估计中的作用
• Light Filed Depth Estimation using GAN，利用GAN进行光场深度恢复
• EV-FlowNet: Self-Supervised Optical Flow for Event-based Cameras,Proceedings of Robotics 2018,[Paper]
• DF-Net: Unsupervised Joint Learning of Depth and Flow using Cross-Task Consistency, ECCV 2018, [Paper]
• GeoNet: Unsupervised Learning of Dense Depth, Optical Flow and Camera Pose, CVPR 2018, [Paper]

3D & Graphic

• PRNet: Self-Supervised Learning for Partial-to-Partial Registration,NeurIPS 2019
• Learning to Reconstruct 3D Human Pose and Shape via Model-fitting in the Loop,ICCV 2019, [Paper] , [Project Page]
• Cross View Fusion for 3D Human Pose Estimation,ICCV 2019, [Paper] ,跨视角3D位姿估计
• MVF-Net: Multi-View 3D Face Morphable Model Regression,多视角3D人脸重建, [Paper]
• KillingFusion
• ReFusion: 3D Reconstruction in Dynamic Environments for RGB-D Cameras Exploiting Residuals, [Paper]
• densebody_pytorch, [Paper]
• Single-Image Piece-wise Planar 3D Reconstruction via Associative Embedding,CVPR 2019, [Paper], 单目3D重建
• HorizonNet: Learning Room Layout with 1D Representation and Pano Stretch Data Augmentation,CVPR 2019, [Paper], 深度学习全景转3D
• Adaptive O-CNN: A Patch-based Deep Representation of 3D Shapes,SIGGRAPH Asia 2018, [Project Page]

Other Collections

• chinese-independent-blogs, 中文独立博客集锦
• StructureFlow: Image Inpainting via Structure-aware Appearance Flow,图像inpainting
• free-books,互联网上的免费书籍
• AcademicPages,通用的学术主页模版
• MMdnn,实现深度学习模型之间的相互转换
• tensorflow2caffemodel,tensorflow模型转caffemodel
• lihang-code,《统计学习方法》的代码实现
• sse2neon,sse2neon,SSE转neon，嵌入式移植时可能会用到;
• Production-Level-Deep-Learning,深度学习模型部署流程
• 动手学深度学习Dive-into-DL-PyTorch
• machine-learning-yearning-cn，Machine Learning Yearning 中文版 - 《机器学习训练秘籍》 - Andrew Ng 著
• academicpages.github.io，学术主页模板
• Coursera-ML-AndrewNg-Notes,吴恩达老师的机器学习课程个人笔记
• machine-learning-notes,机器学习，概率模型和深度学习的讲义(1500+页)和视频链接
• CNN-Visualization,CNN可视化、理解CNN
• Awesome Semantic Segmentation, 语义分割集合
• IROS2018 SLAM Collections, IROS 2018集合
• VP-SLAM-SC-papers,Visual Positioning & SLAM & Spatial Cognition 论文统计与分析
• Awesome System for Machine Learning
• Machine-Learning-With-Python, 《机器学习实战》python代码实现
• How to learn robotics, 开源机器人学学习指南
• Awesome Deep Vision,DL在CV领域的应用
• Single-Image-Super-Resolution, 一个有关图像超分辨的合集
• ai report, AI相关的研究报告
• State-of-the-art papers and code,搜集了目前sota的论文以及代码
• CVPR 2019 (Papers/Codes/Project/Paper reading)
• A curated list of papers & resources linked to 3D reconstruction from images,有关三维重建的论文汇总
• SLAM-Jobs, SLAM/SFM求职指南
• Spatial Attentive Single-Image Deraining with a High Quality Real Rain Dataset,CVPR 2019,去雨
• Densely Connected Pyramid Dehazing Network,CVPR 2018,去雾
• MMSR，MMLAB推出的超分辨工具箱
• 深度学习OCR
• 西瓜书🍉学习笔记
• awesome-reinforcement-learning-zh,强化学习从入门到放弃的资料
• Deep Plug-and-Play Super-Resolution for Arbitrary Blur Kernels,CVPR 2019,超分辨
• Cool Fashion Papers, Cool resources about Fashion + AI.
• Deep Flow-Guided Video Inpainting,CVPR 2019, [Paper] ,图像修复
• YOLACT: Real-time Instance Segmentation
• LPRNet: License Plate Recognition via Deep Neural Networks, [Paper]
• CHINESE-OCR, 运用tf实现自然场景文字检测
• BeautyCamera, 美颜相机，具有人脸检测、磨皮美白人脸、滤镜、调节图片、摄像功能
• CV-arXiv-Daily, 分享计算机视觉每天的arXiv文章
• Pluralistic-Inpainting, ArXiv | Project Page | Online Demo | Video(demo)
• An Interactive Introduction to Fourier Transforms, 超棒的傅里叶变换图形化解释
• pumpkin-book, 《机器学习》（西瓜书）公式推导解析
• Julia
• A Julia machine learning framework，一种基于Julia的机器学习框架
• High-Performance Face Recognition Library on PyTorch，人脸识别库
• Deep-Learning-Coursera，深度学习教程（deeplearning.ai）
• The best resources around Machine Learning
• VGGFace2: A dataset for recognising faces across pose and age
• Statistical learning methods，统计学习方法
• End-to-end Adversarial Learning for Generative Conversational Agents，2017，介绍了一种端到端的基于GAN的聊天机器人
• Residual Non-local Attention Networks for Image Restoration,ICLR 2019.
• MSGAN: Mode Seeking Generative Adversarial Networks for Diverse Image Synthesis, CVPR 2019,[Paper]
• SPADE: Semantic Image Synthesis with Spatially-Adaptive Normalization,CVPR 2019, [Project Page]
• Faceswap with Pytorch or DeepFake with Pytorch, 换脸
• DeepFaceLab, 换脸