那些精贵的3D视觉系统学习资源总结(附书籍、网址与视频...

点击上方“3DCVer”，选择“星标”干货第一时间送达之前我们总结了一篇关于3D视觉系统学习路线——《吐血整理|3D视觉系统化学习路线》 ，本文重点是对于各个模块的一些资料整理，如有不完善之处，还请各位补充。

（一）基础操作

Linux

学习网站

Linux中国：https://linux.cn/

鸟哥的linux私房菜：http://linux.vbird.org/

Linux公社：
https://www.linuxidc.com/

学习书籍

《鸟哥的Linux私房菜》

《Linux命令行与shell脚本编程大全》

《Linux Shell脚本攻略》

《Linux命令行大全》

《Linux就该这么学》

《UNIX高级编程》

在公众号【3DCVer】后台回复“Linux”，即可获取完整PDF资料。
Vim

学习网站

OpenVim：

https://link.zhihu.com/?target=http%3A//www.openvim.com/tutorial.html

Vim Adventures：

https://link.zhihu.com/?target=http%3A//vim-adventures.com/

Vim详细教程：

https://zhuanlan.zhihu.com/p/68111471

Interactive Vim tutorial：

https://link.zhihu.com/?target=http%3A//www.openvim.com/

最详细的Vim编辑器指南：

https://www.shiyanlou.com/questions/2721/

简明Vim教程：

https://link.zhihu.com/?target=http%3A//coolshell.cn/articles/5426.html

Vim学习资源整理：

https://link.zhihu.com/?target=https%3A//github.com/vim-china/hello-vim
学习书籍

《Mastering Vim》

《Modern Vim》

《Mastering Vim Quickly》

Git学习资源

Git官方文档：

https://docs.gitlab.com/ee/README.html

Git-book：

https://git-scm.com/book/zh/v2

Github超详细的Git学习资料：

https://link.zhihu.com/?target=https%3A//github.com/xirong/my-git

Think like Git：

http://think-like-a-git.net/

Atlassian Git Tutorial:

https://link.zhihu.com/?target=https%3A//www.atlassian.com/git/tutorials

Git Workflows and Tutorials：

原文：https://www.atlassian.com/git/tutorials/comparing-workflows译文：https://github.com/xirong/my-git/blob/master/git-workflow-tutorial.md

版本管理工具介绍--Git篇：

https://link.zhihu.com/?target=http%3A//www.imooc.com/learn/208

廖雪峰Git教程：

https://www.liaoxuefeng.com/wiki/896043488029600
学习书籍

《Git学习指南》

《Pro Git》

《Pro Git》中文版翻译：

https://bingohuang.gitbooks.io/progit2/content/

《Git版本控制管理》

在公众号【3DCVer】，后台回复“Git”，即可获取完整PDF资料。
Shell

学习资源

Shell在线速查表：

        https://devhints.io/bash

Bash Guide for Beginners：

https://link.zhihu.com/?target=http%3A//www.tldp.org/LDP/Bash-Beginners-Guide/html/

Advanced Bash-Scripting Guide：

https://link.zhihu.com/?target=http%3A//www.tldp.org/LDP/abs/html/
学习书籍

Bash Notes For Professionals

《linux shell脚本攻略》

《LINUX与UNIX Shell编程指南》

在公众号【3DCVer】后台回复“Shell”，即可获取完整PDF资料。
学习视频

https://link.zhihu.com/?target=https%3A//www.youtube.com/playlist%3Flist%3DPLdfA2CrAqQ5kB8iSbm5FB1ADVdBeOzVqZ

GDB

GDB调试入门指南：

https://zhuanlan.zhihu.com/p/74897601

GDB Documentation：

http://www.gnu.org/software/gdb/documentation/
CMake

学习资源

Cmake-tutoria：

https://cmake.org/cmake-tutorial/

Learning-cmake：

https://github.com/Akagi201/learning-cmake

awesome-cmake（公司常用的培训资料）:

https://github.com/onqtam/awesome-cmake

（二）数学基础

1. 微分几何2. 拓扑理论3. 随机算法4. 计算方法5. 多视图几何6. 图像处理基础算法7. 复变函数8. 非线性优化9. 数学分析10. 数值分析11. 矩阵论12. 离散数学13. 最优化理论14. 概率论与数理统计15. 泛函分析在公众号【3DCVer】后台回复“数学基础”，即可获取完整PDF资料。

（三）数据结构与算法

学习书籍

1. 剑指offer2. 编程之法3. 编程之美4. 程序员面试宝典5. 算法导论6. 图解数据结构：使用C++（黄皮书）在公众号【3DCVer】后台回复“数据结构与算法”，即可获取完整PDF资料。
学习视频

清华大学邓俊辉：

https://www.bilibili.com/video/av49361421?from=search&seid=17039136986597710308

小甲鱼：

https://www.bilibili.com/video/av29175690?from=search&seid=17039136986597710308

剑指offer数据结构与算法：

https://www.bilibili.com/video/av64288683?from=search&seid=17039136986597710308

数据结构与算法C++实现：

https://www.bilibili.com/video/av31763085?from=search&seid=17039136986597710308

（四）编程语言

C++

C++ Primer

C++ Primer Plus

深度探索C++对象模型

Effective C++

More Effective C++ 35个改善编程与设计的有效方法

C++标准库

在公众号【3DCVer】后台回复“C++”，即可获取完整PDF资料。
Python

Python编程从入门到实践

Python高级编程

Python高性能编程

Python核心编程

在公众号【3DCVer】后台回复“Python”，即可获取完整PDF资料。
C

C语言程序设计

C Primer Plus

C和指针

C语言接口与实现

C/C++深层探索

Linux C编程一站式学习

C陷阱与缺陷

C语言参考手册

在公众号【3DCVer】后台回复“C语言”，即可获取完整PDF资料。
ROS

机器人ROS开发实践（胡春旭）

ROS机器人编程：原理与应用

ROS机器人开发应用案例分析

在公众号【3DCVer】后台回复“ROS”，即可获取完整PDF资料。

（五）深度学习

学习书籍

1、《Deep Learning》（深度学习花书，Ian Goodfellow，Yoshua Bengio著）2、《深度学习之TensorFlow 入门、原理与进阶实战》3、《深度学习之TensorFlow工程化项目实战》4、《动手学深度学习》
在公众号【3DCVer】后台回复“深度学习”，即可获取完整PDF资料。
学习资源

深度学习500问：

https://github.com/scutan90/DeepLearning-500-questions

awesome-deep-learning：

https://github.com/ChristosChristofidis/awesome-deep-learning

awesome-deep-learning-papers：

https://github.com/terryum/awesome-deep-learning-papers

Deep-Learning-Papers-Reading-Roadmap：

https://github.com/floodsung/Deep-Learning-Papers-Reading-Roadmap

MIT-deep-learning：

https://github.com/lexfridman/mit-deep-learning

MIT Deep Learning Book：

https://github.com/janishar/mit-deep-learning-book-pdf

Artificial-Intelligence-Deep-Learning-Machine-Learning-Tutorials：

https://github.com/TarrySingh/Artificial-Intelligence-Deep-Learning-Machine-Learning-Tutorials
学习视频

1、吴恩达深度学习工程师全套课程（网易云课堂）https://mooc.study.163.com/smartSpec/detail/1001319001.htm2、斯坦福大学李飞飞cs231n：http://cs231n.stanford.edu/3、李宏毅深度学习视频教程https://www.bilibili.com/video/av48285039?from=search&seid=182759358072219682014、动手学深度学习（李沐）http://zh.d2l.ai/chapter_preface/preface.html5、深度学习框架Tensorflow学习与应用https://www.bilibili.com/video/av20542427?from=search&seid=15215014902897800289
深度学习进阶知识

1、数据增强相关知识

数据增强的一些开源项目：https://github.com/aleju/imgaughttps://github.com/mdbloice/Augmentorhttps://github.com/google-research/uda谷歌论文：https://arxiv.org/abs/1909.137192、目标检测网络的一些总结内容

Github链接：https://github.com/hoya012/deep_learning_object_detectionGithub链接：https://github.com/abhineet123/Deep-Learning-for-Tracking-and-Detection3、语义分割相关

https://link.zhihu.com/?target=https%3A//github.com/mrgloom/awesome-semantic-segmentationGithub链接：https://github.com/mrgloom/awesome-semantic-segmentation4、图像检索

Github链接：https://github.com/zhangqizky/awesome-cbir-papershttps://github.com/willard-yuan/awesome-cbir-papers5、图像分类

https://github.com/zhangqizky/Image_Classification_with_5_methods6、VAE相关知识点

Github链接：https://github.com/matthewvowels1/Awesome-VAEs7、人体姿态估计

Github链接：https://github.com/wangzheallen/awesome-human-pose-estimation8、目标跟踪

Github链接：https://github.com/czla/daily-paper-visual-tracking多目标跟踪：https://github.com/SpyderXu/multi-object-tracking-paper-list9、异常检测

Github链接：https://github.com/yzhao062/anomaly-detection-resources10、活体检测

Github链接：https://github.com/SoftwareGift/FeatherNets_Face-Anti-spoofing-Attack-Detection-Challenge-CVPR201911、人群计数

Github链接：https://github.com/gjy3035/Awesome-Crowd-Counting12、模型的压缩、加速和修建

模型的压缩和加速Github链接：https://github.com/memoiry/Awesome-model-compression-and-accelerationhttps://github.com/cedrickchee/awesome-ml-model-compression模型的修建：Github链接：https://github.com/he-y/Awesome-Pruning13、行为识别和视频理解

Github链接：https://github.com/jinwchoi/awesome-action-recognition14、GAN相关资料

Github链接：https://github.com/zhangqianhui/AdversarialNetsPapershttps://github.com/nightrome/really-awesome-ganhttps://github.com/hindupuravinash/the-gan-zoohttps://github.com/eriklindernoren/Keras-GAN15、图像和视频超分辨率

图像超分辨率Github链接：https://github.com/ChaofWang/Awesome-Super-Resolutionhttps://github.com/YapengTian/Single-Image-Super-Resolutionhttps://github.com/ptkin/Awesome-Super-Resolution视频超分辨率链接：https://github.com/LoSealL/VideoSuperResolution16、人脸landmark3D

Github链接：https://github.com/mrgloom/Face-landmarks-detection-benchmarkhttps://github.com/D-X-Y/landmark-detectionhttps://github.com/ChanChiChoi/awesome-Face_Recognition17、面部表情识别

Github链接：https://github.com/amusi/Deep-Learning-Interview-Book/blob/master/docs/%E6%9C%BA%E5%99%A8%E5%AD%A6%E4%B9%A0.md18、场景识别

Github链接：https://github.com/CSAILVision/places365https://github.com/chenyuntc/scene-baselinehttps://github.com/foamliu/Scene-Classification19、深度学习在推荐系统中的应用

Github链接：https://github.com/robi56/Deep-Learning-for-Recommendation-Systems20、强化学习资料

Github链接：https://github.com/wwxFromTju/awesome-reinforcement-learning-zh

（六）AutoML

框架

Autokeras：https://github.com/keras-team/autokeras学习资源

Awesome-AutoML-papers（超全）：https://github.com/hibayesian/awesome-automl-papers

（七）深度学习框架

Tensorflow

Tensorflow中文官方文档：https://github.com/jikexueyuanwiki/tensorflow-zhTensorflow2.0 tutorials：https://github.com/czy36mengfei/tensorflow2_tutorials_chineseawesome-tensorflow：https://github.com/jtoy/awesome-tensorflow图解Tensorflow源码：https://github.com/yao62995/tensorflowCaffe

caffe2_cpp_tutorial：https://github.com/leonardvandriel/caffe2_cpp_tutorialCaffe使用教程：https://github.com/shicai/Caffe_ManualAwesome-Caffe：https://github.com/MichaelXin/Awesome-CaffeKeras

Keras中文文档：https://keras.io/zh/Pytorch

Pytorch-tutorial：https://github.com/yunjey/pytorch-tutorialpytorch-handbook：https://github.com/zergtant/pytorch-handbookAwesome-pytorch-list：https://github.com/bharathgs/Awesome-pytorch-listMXNet

Tutorial：https://mxnet.incubator.apache.org/api
深度学习网络可视化工具

Netron：https://github.com/lutzroeder/netronNN-SVG：https://github.com/zfrencheePlotNeuralNet：https://github.com/HarisIqbal88/PlotNeuralNetConvNetDraw：https://cbovar.github.io/ConvNetDraw/Draw_Convnet：https://github.com/gwding/draw_convnetNetscope：https://link.zhihu.com/?target=https%3A//github.com/ethereon/netscope

（八）机器学习

学习书籍

机器学习（周志华）统计学习方法（李航）PRML模式识别与机器学习（马春鹏）机器学习实战机器学习系统设计分布式机器学习：算法、理论与实践机器学习中的数学Machine Learning - A Probabilistic Perspective百面机器学习美团机器学习实践在公众号【3DCVer】后台回复“机器学习”，即可获取完整PDF资料。
学习资源

AILearning：

https://github.com/apachecn/AiLearning

awesome-machine-learning：

https://github.com/josephmisiti/awesome-machine-learning

awesome-machine-learning：

https://github.com/jobbole/awesome-machine-learning-cn

machine-learning-for-software-engineers：

https://github.com/ZuzooVn/machine-learning-for-software-engineers

Machine Learning & Deep Learning Tutorials：

https://github.com/ujjwalkarn/Machine-Learning-Tutorials

homemade-machine-learning：

https://github.com/trekhleb/homemade-machine-learning

3D-Machine-Learning（非常有价值）：

https://github.com/timzhang642/3D-Machine-Learning
学习视频

1、吴恩达CS229: Machine Learning （机器学习视频）视频链接：http://cs229.stanford.edu/2、斯坦福大学机器学习视频视频链接：https://www.coursera.org/learn/machine-learning3、李宏毅机器学习视频视频下载链接：https://www.bilibili.com/video/av59538266（这是B站上的在线视频）百度云盘：链接: https://pan.baidu.com/s/1HdVdx52MZ-FF5dSWpAOfeA 提取码: vjhy4、Google机器学习Github链接：https://github.com/yuanxiaosc/Google-Machine-learning-crash-course

（九）计算机视觉

学习书籍

Computer Vision Models，Learning and Inference

Computer Vision Algorithms and Applications

Machine Vision Algorithms and Applications

Linear Algebra for Computer Vision

An Invitation to 3-D Vision: From Images to Geometric Models

计算机视觉中的多视图几何

Computer Vision for Visual Effects

Mastering OpenCV with Practical Computer Vision Projects

OpenCV3计算机视觉：Python语言实现

Practical OpenCV

OpenCV 3.0 Computer Vision with Java

在公众号【3DCVer】后台回复“计算机视觉”，即可获取完整PDF资料。
学习课程

计算机视觉博士课程：

https://github.com/hassony2/useful-computer-vision-phd-resources

81页计算机视觉学习指南：

https://www.pyimagesearch.com/start-here/

Deep Learning: Advanced Computer Vision：

https://www.udemy.com/course/advanced-computer-vision/

（十）自动驾驶

学习视频

1、 百度Apollo系列教程

视频链接：http://bit.baidu.com/subject/index/id/16.html2、(MIT自动驾驶课程)MIT 6.S094: Deep Learning for Self-Driving Cars

视频链接：https://selfdrivingcars.mit.edu/3、国外教程自动驾驶汽车专项课程

课程：https://www.coursera.org/specializations/self-driving-cars笔记：https://github.com/qiaoxu123/Self-Driving-Cars文档：https://qiaoxu123.github.io/Self-Driving-Cars/#/
方向汇总

机动车/非机动车/行人的检测、跟踪与捕获各种车辆特征等结构化信息提取各类驾驶行为的分析违章事件的检出，交通数据的采集车辆/行人检测与跟踪道路分割与识别车道线检测场景分割场景识别自动泊车障碍物的识别车道偏离报警交通标志的识别车载视频雷达（激光、毫米波、超声波）多源信号融合技术版面分析文本行/串检测单字/字符串识别语义分析结构化信息提取AI芯片深度学习的分布和并行处理系统
论文汇总

1、 单目图像中的3D物体检测

1.YOLO3D2.SSD-6D3.3D Bounding Box Estimation Using Deep Learning and Geometry4.GS3D:An Effcient 3D Object Detection Framework for Autonomous Driving5.Deep MANTA: A Coarse-to-fine Many-Task Network for joint 2D and 3D vehicle analysis from monocular image6.Task-Aware Monocular Depth Estimation for 3D Object Detection7.M3D-RPN: Monocular 3D Region Proposal Network for Object Detection8.Monocular 3D Object Detection with Pseudo-LiDAR Point Cloud9.Monocular 3D Object Detection and Box Fitting Trained End-to-End Using Intersection-over-Union Loss10.Disentangling Monocular 3D Object Detection11.Shift R-CNN: Deep Monocular 3d Object Detection With Closed-Form Geometric Constraints12.Monocular 3D Object Detection via Geometric Reasoning on Keypoints13.Monocular 3D Object Detection Leveraging Accurate Proposals and Shape Reconstruction14.Accurate Monocular Object Detection via Color-Embedded 3D Reconstruction for Autonomous Driving15.3D Bounding Boxes for Road Vehicles: A One-Stage, Localization Prioritized Approach using Single Monocular Images16.Orthographic Feature Transform for Monocular 3D Object Detection17.Multi-Level Fusion based 3D Object Detection from Monocular Images18.MonoGRNet:A Geometric Reasoning Network for Monocular 3D Object Localization19.Mono3D++: Monocular 3D Vehicle Detection with Two-Scale 3D Hypotheses and Task Priors
2、 基于激光雷达点云的3D物体检测

1.VoteNet2.End-to-End Multi-View Fusion for 3D Object Detection in LiDAR Point Clouds3.Deep Hough Voting for 3D Object Detection in Point Clouds4.STD: Sparse-to-Dense 3D Object Detector for Point Cloud5.PointPillars: Fast Encoders for Object Detection from Point Clouds6.PointRCNN: 3D Object Proposal Generation and Detection from Point Cloud7.PIXOR: Real-time 3D Object Detection from Point Clouds8.Complex-YOLO: An Euler-Region-Proposal for Real-time 3D Object Detection on Point Clouds9.YOLO3D: End-to-end real-time 3D Oriented Object Bounding Box Detection from LiDAR Point Cloud10.Vehicle Detection from 3D Lidar Using FCN（百度早期工作2016年）11.Object Detection and Classification in Occupancy Grid Maps using Deep Convolutional Networks12.RT3D: Real-Time 3-D Vehicle Detection in LiDAR Point Cloud for Autonomous Driving13.BirdNet: a 3D Object Detection Framework from LiDAR information14.IPOD: Intensive Point-based Object Detector for Point Cloud15.PIXOR: Real-time 3D Object Detection from Point Clouds16.DepthCN: Vehicle Detection Using 3D-LIDAR and ConvNet17.YOLO4D: A ST Approach for RT Multi-object Detection and Classification from LiDAR Point Clouds18.PointRCNN: 3D Object Proposal Generation and Detection from Point Cloud19.Part-A^2 Net: 3D Part-Aware and Aggregation Neural Network for Object Detection from Point Cloud20.Voxel-FPN: multi-scale voxel feature aggregation in 3D object detection from point clouds21.Fast Point RCNN22.StarNet: Targeted Computation for Object Detection in Point Clouds23.Class-balanced Grouping and Sampling for Point Cloud 3D Object Detection24.LaserNet: An Efficient Probabilistic 3D Object Detector for  Autonomous Driving
3、 基于RGB-D图像的3D物体检测

1.Frustum PointNets for 3D Object Detection from RGB-D Data2.Frustum VoxNet for 3D object detection from RGB-D or Depth images
4、 基于融合方法的3D物体检测（RGB图像+激光雷达/深度图）

1.AVOD2.A General Pipeline for 3D Detection of Vehicles3.Adaptive and Azimuth-Aware Fusion Network of Multimodal Local Features for 3D Object Detection4.Deep Continuous Fusion for Multi-Sensor 3D Object Detection5.Frustum PointNets for 3D Object Detection from RGB-D Data6.Joint 3D Proposal Generation and Object Detection from View Aggregation7.Multi-Task Multi-Sensor Fusion for 3D Object Detection8.Multi-View 3D Object Detection Network for Autonomous Driving9.PointFusion：Deep Sensor Fusion for 3D Bounding Box Estimation10.Pseudo-LiDAR from Visual Depth Estimation：Bridging the Gap in 3D Object Detection for Autonomous Driving
5、 基于双目视觉下的3D物体检测

1.Object-Centric Stereo Matching for 3D Object Detection2.Triangulation Learning Network: from Monocular to Stereo 3D Object Detection3.Pseudo-LiDAR from Visual Depth Estimation: Bridging the Gap in 3D Object Detection for Autonomous Driving4.Stereo R-CNN based 3D Object Detection for Autonomous Driving
6、单目图像深度图生成

1.Deep Ordinal Regression Network for Monocular Depth Estimation2.Depth from Videos in the Wild: Unsupervised Monocular Depth Learning from Unknown Cameras3.Detail Preserving Depth Estimation from a Single Image Using Attention Guided Networks4.FastDepth: Fast Monocular Depth Estimation on Embedded Systems5.Single View Stereo Matching
7、单目图像+激光雷达点云深度图生成

1.Sparse and noisy LiDAR completion with RGB guidance and uncertainty2.Learning Guided Convolutional Network for Depth Completion3.DFineNet: Ego-Motion Estimation and Depth Refinement from Sparse, Noisy Depth Input with RGB Guidance
8、深度图补全

1.Deep RGB-D Canonical Correlation Analysis For Sparse Depth Completion2.Sparse and noisy LiDAR completion with RGB guidance and uncertainty3.Confidence Propagation through CNNs for Guided Sparse Depth Regression4.Learning Guided Convolutional Network for Depth Completion5.DFineNet: Ego-Motion Estimation and Depth Refinement from Sparse, Noisy Depth Input with RGB Guidance6.Depth Completion from Sparse LiDAR Data with Depth-Normal Constraints

(十一)三维重建

学习书籍

1.Computer Vision for Visual Effects2.Computer Vision Algorithms and Applications
相关论文

1.Rolling Shutter Pose and Ego-motion Estimation using Shape-from-Template（ECCV2018）2.BundleFusion: Real-time Globally Consistent 3D Reconstruction using On-the-fly Surface Re-integration（ACM）3.Depth Map Prediction from a Single Image using a Multi-Scale Deep Network4.3D-R2N2:A Unified Approach for Single and Multi-view 3D Object Reconstruction         5.Pixel2Mesh:Generating 3D Mesh Models form Single RGB Images6.Mesh R-CNN（FAIR，CVPR2019）7.Conditional Single-view Shape Generation for Multi-view Stereo Reconstruction8.R-MVSNet: Recurrent MVSNet for High-resolution Multi-view Stereo Depth Inference9.StereoDRNet: Dilated Residual Stereo Net（cvpr2019）
一些开源网站

1、MVE网站链接：https://www.gcc.tu-darmstadt.de/home/proj/mve/index.en.jsp2、Bundler网站链接：http://www.cs.cornell.edu/~snavely/bundler/3、VisualSFM网站链接：https://link.zhihu.com/?target=http%3A//ccwu.me/vsfm/4、OpenMVG网站链接：https://openmvg.readthedocs.io/en/latest/software/SfM/SfM/5、ColMap网站链接：https://link.zhihu.com/?target=https%3A//demuc.de/colmap/
相关资源网站

1、非常全面的三维重建相关资源列表，涵盖SLAM,SFM,MVShttps://github.com/openMVG/awesome_3DReconstruction_list

（十二）立体视觉

学习书籍

《视觉测量》（张广军版）《multiview geometry in computer vision》
在公众号【3DCVer】后台回复“立体视觉”，即可获取完整PDF资料。
学习课程

CS231A: Computer Vision, From 3D Reconstruction to Recognition：http://web.stanford.edu/class/cs231a/

（十三）结构光与三维重建

学习书籍

《光栅投影三维精密测量》《基于多视图的三维结构重建》
开源项目

3d reconstruction using three step phase shift：

https://github.com/phreax/structured_light

A framework for Structured Light based 3D scanning projects：

https://github.com/nikolaseu/neuvision

awesome_3DReconstruction_list：

https://github.com/openMVG/awesome_3DReconstruction_list

（十四）SLAM

SLAM大佬网站

1、跟踪SLAM前沿动态论文，更新的很频繁https://github.com/YiChenCityU/Recent_SLAM_Research2、很全视觉slam资料大全https://github.com/tzutalin/awesome-visual-slam3、开源SLAM列表https://github.com/OpenSLAM/awesome-SLAM-list4、很全面的SLAM教程https://link.zhihu.com/?target=https%3A//github.com/kanster/awesome-slam5、非常全面的三维重建相关资源列表，涵盖SLAM,SFM,MVShttps://github.com/openMVG/awesome_3DReconstruction_list6、很全的RGBD SLAM开源方案介绍https://github.com/electech6/owesome-RGBD-SLAM7、非常全面的相机总结，包括论文，设备厂商，算法，应用等https://github.com/uzh-rpg/event-based_vision_resources8、SLAM 学习与开发经验分享https://github.com/GeekLiB/Lee-SLAM-source9、中文注释版ORB-SLAM2https://github.com/Vincentqyw/ORB-SLAM2-CHINESE10、语义SLAM相关资料https://zhuanlan.zhihu.com/p/64825421
SLAM相关的工具和库

基础工具：Eigen、OpenCV、PCL、ROS后端优化的库：g2o、GTSAM、Ceres solver
SLAM相关开源代码

1、MonoSLAMGithub地址：https://github.com/hanmekim/SceneLib22、PTAMGithub地址：https://www.robots.ox.ac.uk/~gk/PTAM/3、ORB-SLAMGithub地址：http://webdiis.unizar.es/~raulmur/orbslam/4、LSD-SLAMGithub地址：https://vision.in.tum.de/research/vslam/lsdslam5、SVOGithub地址：https://github.com/OpenSLAM/awesome-SLAM-list6、DTAMGithub地址：https://github.com/anuranbaka/OpenDTAM7、DVOGithub地址：https://github.com/tum-vision/dvo_slam8、DSOGithub地址：https://github.com/JakobEngel/dso9、RTAB-MAPGithub地址：https://github.com/introlab/rtabmap10、RGBD-SLAM-V2Github地址：https://github.com/felixendres/rgbdslam_v211、Elastic FusionGithub地址：https://github.com/mp3guy/ElasticFusion12、Hector SLAMGithub地址：https://wiki.ros.org/hector_slam13、GMappingGithub地址：https://wiki.ros.org/gmapping14、OKVISGithub地址：https://github.com/ethz-asl/okvis15、ROVIOGithub地址：https://github.com/ethz-asl/rovio16、COSLAMGithub地址：http://drone.sjtu.edu.cn/dpzou/project/coslam.php17、DTSLAMGithub地址：https://github.com/plumonito/dtslam18、REBVOGithub地址：https://github.com/JuanTarrio/rebvo
SLAM相关数据集

1. Malaga Dataset2. Tum: Computer Vision Lab: RGB-D3. KITTI Dataset4. University of Freiburg: Department of Computer Science5. MRPT6. ICDL-NUIM
SLAM学习书籍

概率机器人

视觉SLAM十四讲

计算机视觉中的多视图几何

机器人学中的状态估计

Principles of Robot Motion Theory，Algorithms and Implementation

在公众号【3DCVer】后台回复“SLAM学习资料”，获取完整PDF资料。
SLAM学习视频

公开课：https://www.youtube.com/channel/UCi1TC2fLRvgBQNe-T4dp8Eg
这些资料将同步分享在我们的学习圈「3D视觉技术」星球，更多干货资料也将在星球中补充完善。
上述内容，如有侵犯版权，请联系作者，会自行删文。