持续更新中!
交流可加v:naca_yu,加入毫米波雷达相关交流群(非广告、盈利),请备注:csdn+名称,谢谢!
一个人更新不易,欢迎大家补充,帮助到你的话,感谢你能点个Star!
https://github.com/nacayu/awsome-deeplearning-based-radar-perception
近年毫米波雷达相关论文 & 多模态融合相关论文
论文名称 | 任务类别 | 传感器 | 源码地址 | 已更新解读 | 备注 |
---|---|---|---|---|---|
MmWave Radar and Vision Fusion for Object Detection in Autonomous Driving: A Review | 综述 | radar&camera | 自动驾驶中radar相关的多传感器融合综述 | ||
Towards Deep Radar Perception for Autonomous Driving: Datasets, Methods, and Challenges | 综述 | radar | 面向自动驾驶的深度雷达感知:数据集、方法和挑战综述 | ||
Radar-PointGNN: Graph Based Object Recognition for Unstructured Radar Point-cloud Data | 3D检测 | radar | https://zhuanlan.zhihu.com/p/549641548 | 基于GNN | |
2D Car Detection in Radar Data with PointNets | 3D检测 | radar | https://zhuanlan.zhihu.com/p/568160922 | 改进PointNets | |
Bridging the View Disparity of Radar and Camera Features for Multi-modal Fusion 3D Object Detection | 3D检测 | camera&radar | https://zhuanlan.zhihu.com/p/568160922 | ||
RadSegNet: A Reliable Approach to Radar Camera Fusion | 分割 | camera&radar | https://zhuanlan.zhihu.com/p/568160922 | ||
Depth Estimation From Monocular Images and Sparse Radar Using Deep Ordinal Regression Network | 深度估计 | camera&radar | https://github.com/lochenchou/DORN_radar | https://zhuanlan.zhihu.com/p/568160922 | 序数回归,改进自DORN |
A Simple Baseline for BEV Perception Without LiDAR(2022, MIT) | 分割 | camera&radar | https://github.com/aharley/simple_bev | https://zhuanlan.zhihu.com/p/568160922 | 对比LSS… |
See Through Smoke: Robust Indoor Mapping with Low-cost mmWave Radar | 密集点云生成GAN | lidar & radar | https://zhuanlan.zhihu.com/p/568160922 | ||
Radar Occupancy Prediction With Lidar Supervision While Preserving Long-Range Sensing and Penetrating Capabilities | 可行区域生成:freespace | lidar & radar | https://zhuanlan.zhihu.com/p/568160922 | ||
RADIANT: Radar-Image Association Network for 3D Object Detection | 3D检测 | camera&radar | https://github.com/longyunf/radiant | https://zhuanlan.zhihu.com/p/597739906 | 一种全新的毫米波雷达图像关联网络用于3D目标检测 |
CRFNet for Object Detection (Camera and Radar Fusion Network) | 2D检测 | camera&radar | https://github.com/nacayu/CRFNet_Tensorflow2.4.1 | https://zhuanlan.zhihu.com/p/112578232 | 基于yolov3 |
A frustum proposal-based 3D object detection network for multi-stage fusion in autonomous driving | 3D检测 | camera&radar | https://github.com/brandesjj/centerfusionpp | https://zhuanlan.zhihu.com/p/603398636 | 基于centerfusion改进的下一代毫米波雷达与视觉融合方案 |
CRAFT: Camera-Radar 3D Object Detection with Spatio-Contextual Fusion Transformer | 3D检测 | camera&radar | https://zhuanlan.zhihu.com/p/581055339 | 一种基于空间-语义信息互补的毫米波雷达与相机融合3D检测方法 | |
SAF-FCOS: Spatial Attention Fusion for Obstacle Detection using MmWave Radar and Vision Sensor | 2D检测 | camera&radar | https://github.com/Singingkettle/SAF-FCOS | 即将更新 | 基于FCOS |
NVRadarNet: Real-Time Radar Obstacle and Free Space Detection for Autonomous Driving | 可行区域生成:Freespace | lidar&radar | https://zhuanlan.zhihu.com/p/575385783 | 实时(1.5ms)BEV多任务 | |
CenterFusion: Center-based Radar and Camera Fusion for 3D Object Detection | 3D检测 | camera&radar | https://github.com/mrnabati/CenterFusion | https://zhuanlan.zhihu.com/p/508905129 | 基于CenterNet |
Multi-Modal Fusion Transformer for End-to-End Autonomous Driving | 自动驾驶车辆路径预测 | camera&radar | https://github.com/autonomousvision/transfuser | https://zhuanlan.zhihu.com/p/508898376 | 基于transformer 语义信息注意力关联 |
K-Radar: 4D Radar Object Detection for Autonomous Driving in Various Weather Conditions | 3D检测 | camera&4d radar | https://github.com/kaist-avelab/k-radar | 即将更新 | 4D雷达 |
Improved Orientation Estimation and Detection with Hybrid Object Detection Networks for Automotive Radar | 2D检测 in BEV | radar | 即将更新 | 结合基于网格和基于点的处理方法 | |
CramNet: Camera-Radar Fusion with Ray-Constrained Cross-Attention for Robust 3D Object Detection | 3D检测 | radar&camera | 即将更新 | 射线约束交叉注意机制 考虑传感器短时间失灵 |
|
DeepFusion: A Robust and Modular 3D Object Detector for Lidars, Cameras and Radars | 3D检测 | lidar&camera&radar | https://zhuanlan.zhihu.com/p/578655032 | 特征提取模块化设计 每个传感器模块设计auxiliary loss |
|
GRIF Net: Gated Region of Interest Fusion Network for Robust 3D Object Detection from Radar Point Cloud and Monocular Image | 3D检测 | radar&camera | 即将更新 | 设置模态融合阈值,自适应地选择较优输入,调节最终贡献 二阶段检测 |
|
RCDPT: RADAR-CAMERA FUSION DENSE PREDICTION TRANSFORMER | 单目深度估计 | radar&camera | 即将更新 | ||
Radar Voxel Fusion for 3D Object Detection | 3D检测 | lidar&camera&radar | 即将更新 | ||
CFTrack: Center-based Radar and Camera Fusion for 3D Multi-Object Tracking | 3D目标跟踪 | radar&camera | 即将更新 | end-to-end跟踪方法 基于centerfusion |
数据集
数据集名称 | 传感器 | 解读 | 地址 | 备注 |
---|---|---|---|---|
原始数据 | ||||
CRUW | radar | https://zhuanlan.zhihu.com/p/508896576 | https://www.cruwdataset.org/ | 原始数据的自动驾驶数据集 支持tracking |
CARRADA | camera radar | https://github.com/valeoai/carrada_dataset | 支持tracking,segmentation | |
RADDet | radar | https://github.com/ZhangAoCanada/RADDet#Dataset | ||
radar点云数据集 | ||||
nuscenes | radar camera lidar | https://zhuanlan.zhihu.com/p/508912923 | ||
RadarScenes | radar camera | https://zhuanlan.zhihu.com/p/424535286 | https://radar-scenes.com/ | 提供point-wise点级别细粒度的标注 |
RADIATE | radar camera lidar | https://blog.csdn.net/CCChester/article/details/127565100 | http://pro.hw.ac.uk/radiate/ | 支持tracking |
Pointillism | radar camera lidar | https://github.com/Kshitizbansal/pointillism-multi-radar-data | ||
Zendar | radar camera lidar | https://storage.googleapis.com/www.archive.zendar.io/dataset.html | 支持tracking | |
4D雷达数据集 | ||||
TJ4DRadSet | radar camera lidar | https://blog.csdn.net/u013019296/article/details/127219137 | https://github.com/TJRadarLab/TJ4DRadSet | 支持tracking |
K-Radar Dataset | camera images, Lidar point cloud, RTK-GPS, and Radar tensor | https://github.com/kaist-avelab/K-Radar | ||