自动驾驶感知/定位/部署全栈算法综述汇总
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虽然已经从事自动驾驶领域多年，但一直忙于项目调研与落地，近期才有时间将自动驾驶领域方向的一些综述、经典论文进行汇总，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；
1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
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2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
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3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
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4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
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5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
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6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
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7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
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83D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
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9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
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10Transformer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
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11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
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12多传感器融合
多维度多传感器融合方案汇总；
1.Multi-modal Sensor Fusion for Auto Driving Perception：A Survey
2.Multisensor data fusion：A review of the state-of-the-art
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13SLAM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
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14模型部署
1.TensorRT
官网链接：https://github.com/NVIDIA/TensorRT
2.NCNN
官网链接：https://github.com/Tencent/ncnn
3.MNN
官网链接：https://github.com/alibaba/MNN
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