It mainly includes radar-related multi-mode detection, segmentation, tracking, freespace space detection papers, datasets, projects, related docs
paper | task | sensors | github link | 中文解读 | remarks |
---|---|---|---|---|---|
MmWave Radar and Vision Fusion for Object Detection in Autonomous Driving: A Review | summary | radar&camera | 自动驾驶中radar相关的多传感器融合summary | ||
Towards Deep Radar Perception for Autonomous Driving: Datasets, Methods, and Challenges | summary | radar | 面向自动驾驶的深度雷达感知:数据集、方法和挑战summary | ||
Radar-PointGNN: Graph Based Object Recognition for Unstructured Radar Point-cloud Data | 3D detection | radar | https://zhuanlan.zhihu.com/p/549641548 | 基于GNN | |
2D Car Detection in Radar Data with PointNets | 3D detection | 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 detection | camera&radar | https://zhuanlan.zhihu.com/p/568160922 | 利用conv-lstm融合多帧radar LSS方式完成FOV-BEV特征转换 |
|
RadSegNet: A Reliable Approach to Radar Camera Fusion | segmentation | camera&radar | https://zhuanlan.zhihu.com/p/568160922 | RADIATE数据集 人工生成雨雪雾数据集增强 点云语义segmentation渲染 |
|
Depth Estimation From Monocular Images and Sparse Radar Using Deep Ordinal Regression Network | depth estimation | 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) | segmentation | camera&radar | https://github.com/aharley/simple_bev | https://zhuanlan.zhihu.com/p/568160922 | 基于nuscenes对radar数据做了丰富的消融实验 以BEVFormer的方式完成图像FOV-BEV的投影 |
See Through Smoke: Robust Indoor Mapping with Low-cost mmWave Radar | density radar points generation | lidar & radar | https://zhuanlan.zhihu.com/p/568160922 | ||
Radar Occupancy Prediction With Lidar Supervision While Preserving Long-Range Sensing and Penetrating Capabilities | freespace generation | lidar & radar | https://zhuanlan.zhihu.com/p/568160922 | Lidar监督radar生成密集occupy freespace | |
RADIANT: Radar-Image Association Network for 3D Object Detection | 3D detection | 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 detection | camera&radar | https://github.com/nacayu/CRFNet_Tensorflow2.4.1 | https://zhuanlan.zhihu.com/p/112578232 | 基于YOLOV3 RV特征融合的经典网络 |
A frustum proposal-based 3D object detection network for multi-stage fusion in autonomous driving | 3D detection | 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 detection | 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 detection方法 | |
SAF-FCOS: Spatial Attention Fusion for Obstacle Detection using MmWave Radar and Vision Sensor | 2D detection | 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 | Freespace generation | 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 detection | 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 | route prediction | 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 detection | 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 detection 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 detection | 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 detection | 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 detection | 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 | Monocular depth estimation | radar&camera | 即将更新 | ||
Radar Voxel Fusion for 3D Object Detection | 3D detection | lidar&camera&radar | 即将更新 | ||
CFTrack: Center-based Radar and Camera Fusion for 3D Multi-Object Tracking | 3D object tracking | radar&camera | 即将更新 | end-to-end跟踪方法 基于centerfusion |
|
SAF-FCOS | 2D detection | radar&camera | https://github.com/Singingkettle/SAF-FCOS | 链接 | early-fusion 点云增强对应的RGB区域阈值 对ADD,MULTILY,CONCATE多种融合方式进行消融实验 |