自动驾驶从业者顶流学习笔记，值得收藏！

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第一次介绍，自动驾驶Daily为大家整理了多个领域方向的数据集、综述、经典论文、视频教程，供大家学习。主要涉及目标检测、语义分割、全景/实例分割、目标跟踪、Transformer、关键点检测、深度估计、3D检测、多模态融合、车道线检测、多传感器数据融合、SLAM与高精地图等方向！

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数据集汇总

KITTI、COCO、Cityscape、BDD100K、MOT15/16/17/20、NuScenes等！

视频教程

最新自动驾驶与计算机视觉视频教程！

多模态融合

针对Lidar、Radar、视觉等数据方案进行融合感知；

1. A Survey on Deep Domain Adaptation for LiDAR Perception

2. Automatic Target Recognition on Synthetic Aperture Radar Imagery：A Survey

3. Deep Multi-modal Object Detection and Semantic Segmentation for Autonomous Driving：Datasets, Methods, and Challenges

4. MmWave Radar and Vision Fusion for Object Detection in Autonomous Driving：A Review

5. Multi-Modal 3D Object Detection in Autonomous Driving：A Survey

6. Multi-modal Sensor Fusion for Auto Driving Perception：A Survey

7. Multi-Sensor 3D Object Box Refinement for Autonomous Driving

8. Multi-View Fusion of Sensor Data for Improved Perception and Prediction in Autonomous Driving

3D检测

对基于单目图像、双目图像、点云数据、多模态数据的3D检测方法进行了梳理；

1. 3D Object Detection for Autonomous Driving：A Review and New Outlooks

2. 3D Object Detection from Images for Autonomous Driving A Survey

3. A Survey of Robust LiDAR-based 3D Object Detection Methods for autonomous driving

4. A Survey on 3D Object Detection Methods for Autonomous Driving Applications

5. Deep Learning for 3D Point Cloud Understanding：A Survey

6. Multi-Modal 3D Object Detection in Autonomous Driving：a survey

7. Survey and Systematization of 3D Object Detection Models and Methods

目标检测综述

主要涉及通用目标检测任务、检测任务中的数据不均衡问题、伪装目标检测、自动驾驶领域检测任务、anchor-based、anchor-free、one-stage、two-stage方案等；

1. A Survey of Deep Learning for Low-Shot Object Detection

2. A Survey of Deep Learning-based Object Detection

3. Camouflaged Object Detection and Tracking：A Survey

4. Deep Learning for Generic Object Detection：A Survey

5. Imbalance Problems in Object Detection：A survey

6. Object Detection in 20 Years：A Survey

7. Object Detection in Autonomous Vehicles：Status and Open Challenges

8. Recent Advances in Deep Learning for Object Detection

目标检测数据增强与不均衡问题

主要涉及目标检测任务中的数据增强、小目标检测、小样本学习、autoargument等工作；

1. A survey on Image Data Augmentation for Deep Learning

2. Augmentation for small object detection

3. Bag of Freebies for Training Object Detection Neural Networks

4. Generalizing from a Few Examples：A Survey on Few-Shot

5. Learning Data Augmentation Strategies for Object Detection

分割综述

主要对实时图像分割、视频分割、实例分割、弱监督/无监督分割、点云分割等方案展开讨论；

1. A Review of Point Cloud Semantic Segmentation

2. A SURVEY ON DEEP LEARNING METHODS FOR SEMANTIC IMAGE SEGMENTATION IN REAL-TIME

3. A SURVEY ON DEEP LEARNING METHODS FOR SEMANTIC

4. A Survey on Deep Learning Technique for Video Segmentation

5. A Survey on Instance Segmentation State of the art

6. A Survey on Label-efficient Deep Segmentation-Bridging the Gap between Weak Supervision and Dense Prediction

7. A Technical Survey and Evaluation of Traditional Point Cloud Clustering  for LiDAR Panoptic Segmentation

8. Evolution of Image Segmentation using Deep Convolutional Neural Network A Survey

9. On Efficient Real-Time Semantic Segmentation

10. Unsupervised Domain Adaptation for Semantic Image Segmentation-a Comprehensive Survey

多任务学习

对检测+分割+关键点+车道线联合任务训练方法进行了汇总；

1. Cascade R-CNN

2. Deep Multi-Task Learning for Joint Localization, Perception, and Prediction

3. Mask R-CNN

4. Mask Scoring R-CNN

5. Multi-Task Multi-Sensor Fusion for 3D Object Detection

6. MultiTask-CenterNet

7. OmniDet

8. YOLOP

9. YOLO-Pose

目标跟踪

对单目标和多目标跟踪、滤波和端到端方法进行了汇总；

1. Camouflaged Object Detection and Tracking：A Survey

2. Deep Learning for UAV-based Object Detection and Tracking：A Survey

3. Deep Learning on Monocular Object Pose Detection and Tracking：A Comprehensive Overview

4. Detection, Recognition, and Tracking：A Survey

5. Infrastructure-Based Object Detection and Tracking for Cooperative Driving Automation：A Survey

6. Recent Advances in Embedding Methods for Multi-Object Tracking：A Survey

7. Single Object Tracking：A Survey of Methods, Datasets, and Evaluation Metrics

8. Visual Object Tracking with Discriminative Filters and Siamese Networks：A Survey and Outlook

深度估计

针对单目、双目深度估计方法进行了汇总，对户外常见问题与精度损失展开了讨论；

1. A Survey on Deep Learning Techniques for Stereo-based Depth Estimation

2. Deep Learning based Monocular Depth Prediction：Datasets, Methods and Applications

3. Monocular Depth Estimation Based On Deep Learning：An Overview

4. Monocular Depth Estimation：A Survey

5. Outdoor Monocular Depth Estimation：A Research Review

6. Towards Real-Time Monocular Depth Estimation for Robotics：A Survey

关键点检测

人体关键点检测方法汇总，对车辆关键点检测具有一定参考价值；

1. 2D Human Pose Estimation：A Survey

2. A survey of top-down approaches for human pose estimation

3. Efficient Annotation and Learning for 3D Hand Pose Estimation：A Survey

4. Recent Advances in Monocular 2D and 3D Human Pose Estimation：A Deep Learning Perspective

Transformer综述

视觉transformer、轻量级transformer方法汇总；

1. A Survey of Visual Transformers

2. A Survey on Visual Transformer

3. Efficient Transformers：A Survey

车道线检测

对2D/3D车道线检测方法进行了汇总，基于分类、检测、分割、曲线拟合等；

2D车道线

1. A Keypoint-based Global Association Network for Lane Detection

2. CLRNet：Cross Layer Refinement Network for Lane Detection

3. End-to-End Deep Learning of Lane Detection and Path Prediction for Real-Time Autonomous Driving

4. End-to-end Lane Detection through Differentiable Least-Squares Fitting

5. Keep your Eyes on the Lane：Real-time Attention-guided Lane Detection

6. LaneNet：Real-Time Lane Detection Networks for Autonomous Driving

7. Towards End-to-End Lane Detection：an Instance Segmentation Approach

8. Ultra Fast Structure-aware Deep Lane Detection

3D车道线

1. 3D-LaneNet+：Anchor Free Lane Detection using a Semi-Local Representation

2. Deep Multi-Sensor Lane Detection

3. FusionLane：Multi-Sensor Fusion for Lane Marking Semantic Segmentation Using Deep Neural Networks

4. Gen-LaneNet：A Generalized and Scalable Approach for 3D Lane Detection

5. ONCE-3DLanes：Building Monocular 3D Lane Detection

6. 3D-LaneNet：End-to-End 3D Multiple Lane Detection

SLAM综述

定位与建图方案汇总；

1. A Survey on Active Simultaneous Localization and Mapping-State of the Art and New Frontiers

2. The Revisiting Problem in Simultaneous Localization and Mapping-A Survey on Visual Loop Closure Detection

3. From SLAM to Situational Awareness-Challenges

4. Simultaneous Localization and Mapping Related Datasets-A Comprehensive Survey

模型量化

1. A Survey on Deep Neural Network CompressionChallenges, Overview, and Solutions

2. Pruning and Quantization for Deep Neural Network Acceleration A Survey