https://github.com/m6c7l/pymmw
@inproceedings{constapel2019practical, title={A Practical Toolbox for Getting Started with mmWave FMCW Radar Sensors}, author={Constapel, Manfred and Cimdins, Marco and Hellbr{"u}ck, Horst}, booktitle={Proceedings of the 4th KuVS/GI Expert Talk on Localization}, year={2019} }
https://github.com/rookiepeng/radarsimpy
https://github.com/chstetco/virtualradar
@article{schoffmann2021virtual, title={Virtual Radar: Real-Time Millimeter-Wave Radar Sensor Simulation for Perception-Driven Robotics}, author={Sch{"o}ffmann, Christian and Ubezio, Barnaba and B{"o}hm, Christoph and M{"u}hlbacher-Karrer, Stephan and Zangl, Hubert}, journal={IEEE Robotics and Automation Letters}, volume={6}, number={3}, pages={4704--4711}, year={2021}, publisher={IEEE} }
https://github.com/PreSenseRadar/OpenRadar
https://github.com/JaydenG1019/HawkEye-Data-Code
@InProceedings{Guan_2020_CVPR, author = {Guan, Junfeng and Madani, Sohrab and Jog, Suraj and Gupta, Saurabh and Hassanieh, Haitham}, title = {Through Fog High-Resolution Imaging Using Millimeter Wave Radar}, booktitle = {IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)}, month = {June}, year = {2020} }
https://github.com/e-271/GridBasedDBSCAN
Non-official implementaion
@inproceedings{kellner2012grid, title={Grid-based DBSCAN for clustering extended objects in radar data}, author={Kellner, Dominik and Klappstein, Jens and Dietmayer, Klaus}, booktitle={2012 IEEE Intelligent Vehicles Symposium}, pages={365--370}, year={2012}, organization={IEEE} }
https://github.com/ZhangAoCanada/RADDet
Dataset: RADDet
@article{zhang2021raddet, title={RADDet: Range-Azimuth-Doppler based Radar Object Detection for Dynamic Road Users}, author={Zhang, Ao and Nowruzi, Farzan Erlik and Laganiere, Robert}, journal={arXiv e-prints}, pages={arXiv--2105}, year={2021} }
https://github.com/tudelft-iv/RTCnet
@article{palffy2020cnn, title={CNN based road user detection using the 3D radar cube}, author={Palffy, Andras and Dong, Jiaao and Kooij, Julian FP and Gavrila, Dariu M}, journal={IEEE Robotics and Automation Letters}, volume={5}, number={2}, pages={1263--1270}, year={2020}, publisher={IEEE} }
https://github.com/yizhou-wang/RODNet
Dataset: CRUW
@article{wang2021rodnet, title={RODNet: A Real-Time Radar Object Detection Network Cross-Supervised by Camera-Radar Fused Object 3D Localization}, author={Wang, Yizhou and Jiang, Zhongyu and Li, Yudong and Hwang, Jenq-Neng and Xing, Guanbin and Liu, Hui}, journal={IEEE Journal of Selected Topics in Signal Processing}, year={2021}, publisher={IEEE} }
https://github.com/jesse1029/RADER-DCSN
Dataset: CRUW
@inproceedings{hsu2021efficient, title={Efficient-ROD: Efficient Radar Object Detection based on Densely Connected Residual Network}, author={Hsu, Chih-Chung and Lee, Chieh and Chen, Lin and Hung, Min-Kai and Lin, Yu-Lun and Wang, Xian-Yu}, booktitle={Proceedings of the 2021 International Conference on Multimedia Retrieval}, pages={526--532}, year={2021} }
https://github.com/sunpengliang/modelConfusion
@inproceedings{sun2021squeeze, title={Squeeze-and-Excitation network-Based Radar Object Detection With Weighted Location Fusion}, author={Sun, Pengliang and Niu, Xuetong and Sun, Pengfei and Xu, Kele}, booktitle={Proceedings of the 2021 International Conference on Multimedia Retrieval}, pages={545--552}, year={2021} }
https://github.com/TUMFTM/CameraRadarFusionNet
Dataset: nuScenes
@inproceedings{nobis2019deep, title={A deep learning-based radar and camera sensor fusion architecture for object detection}, author={Nobis, Felix and Geisslinger, Maximilian and Weber, Markus and Betz, Johannes and Lienkamp, Markus}, booktitle={2019 Sensor Data Fusion: Trends, Solutions, Applications (SDF)}, pages={1--7}, year={2019}, organization={IEEE} }
https://github.com/mrnabati/RRPN
Dataset: nuScenes
@inproceedings{nabati2019rrpn, title={Rrpn: Radar region proposal network for object detection in autonomous vehicles}, author={Nabati, Ramin and Qi, Hairong}, booktitle={2019 IEEE International Conference on Image Processing (ICIP)}, pages={3093--3097}, year={2019}, organization={IEEE} }
https://github.com/mrnabati/CenterFusion
Dataset: nuScenes
@inproceedings{nabati2021centerfusion, title={Centerfusion: Center-based radar and camera fusion for 3d object detection}, author={Nabati, Ramin and Qi, Hairong}, booktitle={Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision}, pages={1527--1536}, year={2021} }
https://github.com/RituYadav92/Radar-RGB-Attentive-Multimodal-Object-Detection
Dataset: nuScenes
@inproceedings{yadav2020radar+, title={Radar+ RGB Fusion For Robust Object Detection In Autonomous Vehicle}, author={Yadav, Ritu and Vierling, Axel and Berns, Karsten}, booktitle={2020 IEEE International Conference on Image Processing (ICIP)}, pages={1986--1990}, year={2020}, organization={IEEE} }
https://github.com/TUMFTM/RadarVoxelFusionNet
Dataset: nuScenes
@Article{nobis21fusion, author={Felix Nobis, Ehsan Shafiei, Phillip Karle, Johannes Betz and Markus Lienkamp}, title={Radar Voxel Fusion for 3D Object Detection}, journal={Applied Sciences}, volume={11}, year={2021}, number={12}, article-number={5598}, doi={https://doi.org/10.3390/app11125598} }
https://github.com/brade31919/radar_depth
Dataset: nuScenes
@article{lin2020depth, title={Depth estimation from monocular images and sparse radar data}, author={Lin, Juan-Ting and Dai, Dengxin and Van Gool, Luc}, journal={arXiv preprint arXiv:2010.00058}, year={2020} }
https://github.com/sxontheway/milliEye
Dataset: self-collected
@article{shuai2021millieye, title={milliEye: A Lightweight mmWave Radar and Camera Fusion System for Robust Object Detection}, author={Shuai, Xian and Shen, Yulin and Tang, Yi and Shi, Shuyao and Ji, Luping and Xing, Guoliang}, year={2021} }
https://github.com/qiank10/MVDNet
Dataset: Oxford Robotcar
@inproceedings{qian2021robust, title={Robust Multimodal Vehicle Detection in Foggy Weather Using Complementary Lidar and Radar Signals}, author={Qian, Kun and Zhu, Shilin and Zhang, Xinyu and Li, Li Erran}, booktitle={Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition}, pages={444--453}, year={2021} }
https://github.com/kathy-lee/voxelnet-astyx
Dataset: Astyx HiRes
Non-official implementaion
@inproceedings{zhou2018voxelnet, title={Voxelnet: End-to-end learning for point cloud based 3d object detection}, author={Zhou, Yin and Tuzel, Oncel}, booktitle={Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition}, pages={4490--4499}, year={2018} }
https://github.com/BerensRWU/Complex_YOLO
Dataset: Astyx HiRes
Non-official implementaion
@inproceedings{simony2018complex, title={Complex-yolo: An euler-region-proposal for real-time 3d object detection on point clouds}, author={Simony, Martin and Milzy, Stefan and Amendey, Karl and Gross, Horst-Michael}, booktitle={Proceedings of the European Conference on Computer Vision (ECCV) Workshops}, pages={0--0}, year={2018} }
Dataset: Astyx HiRes
Documents: https://www.xilinx.com/html_docs/vitis_ai/1_4/lib_samples.html#nef1622520899596
https://github.com/itaiorr/radar_road_seg
Dataset: Self-collected but not public
@article{orr2021high, title={High-resolution radar road segmentation using weakly supervised learning}, author={Orr, Itai and Cohen, Moshik and Zalevsky, Zeev}, journal={Nature Machine Intelligence}, volume={3}, number={3}, pages={239--246}, year={2021}, publisher={Nature Publishing Group} }
https://github.com/TUMFTM/RadarSeg
Dataset: nuScenes
@article{nobis2021kernel, title={Kernel Point Convolution LSTM Networks for Radar Point Cloud Segmentation}, author={Nobis, Felix and Fent, Felix and Betz, Johannes and Lienkamp, Markus}, journal={Applied Sciences}, volume={11}, number={6}, pages={2599}, year={2021}, publisher={Multidisciplinary Digital Publishing Institute} }
https://github.com/radar-lab/traffic_monitoring
Dataset: Self-collected
@inproceedings{jin2020mmwave, title={MmWave Radar Point Cloud Segmentation using GMM in Multimodal Traffic Monitoring}, author={Jin, Feng and Sengupta, Arindam and Cao, Siyang and Wu, Yao-Jan}, booktitle={2020 IEEE International Radar Conference (RADAR)}, pages={732--737}, year={2020}, organization={IEEE} }
https://github.com/valeoai/MVRSS
Dataset: Carrada
@article{ouaknine2021multi, title={Multi-View Radar Semantic Segmentation}, author={Ouaknine, Arthur and Newson, Alasdair and P{'e}rez, Patrick and Tupin, Florence and Rebut, Julien}, journal={arXiv preprint arXiv:2103.16214}, year={2021} }
Dataset: MulRan & Oxford Robotcar
@article{yin2021rall, title={RaLL: End-to-end Radar Localization on Lidar Map Using Differentiable Measurement Model}, author={Yin, Huan and Chen, Runjian and Wang, Yue and Xiong, Rong}, journal={IEEE Transactions on Intelligent Transportation Systems}, year={2021}, publisher={IEEE} }
https://github.com/ZJUYH/radar-to-lidar-place-recognition
Dataset: MulRan & Oxford Robotcar
@article{yin2021radar, title={Radar-to-Lidar: Heterogeneous Place Recognition via Joint Learning}, author={Yin, Huan and Xu, Xuecheng and Wang, Yue and Xiong, Rong}, journal={Frontiers in Robotics and AI}, year={2021}, status={Accept} }
https://github.com/utiasASRL/hero_radar_odometry
Dataset: Oxford Robotcar
https://github.com/ChristopherLu/milliMap
@inproceedings{lu2020millimap,
title={See Through Smoke: Robust Indoor Mapping with Low-cost mmWave Radar},
author={Chris Xiaoxuan Lu, Stefano Rosa, Peijun Zhao, Bing Wang, Changhao Chen, John A. Stankovic, Niki Trigoni and Andrew Markham},
booktitle={ACM International Conference on Mobile Systems, Applications, and Services (MobiSys)},
year={2020}
}
https://github.com/ChristopherLu/milliEgo
@inproceedings{lu2020milliego, title={milliEgo: single-chip mmWave radar aided egomotion estimation via deep sensor fusion}, author={Lu, Chris Xiaoxuan and Saputra, Muhamad Risqi U and Zhao, Peijun and Almalioglu, Yasin and de Gusmao, Pedro PB and Chen, Changhao and Sun, Ke and Trigoni, Niki and Markham, Andrew}, booktitle={Proceedings of the 18th Conference on Embedded Networked Sensor Systems (SenSys)}, year={2020} }
https://github.com/tcboy88/solinteractiondata
@article{yeo2018exploring, title={Exploring tangible interactions with radar sensing}, author={Yeo, Hui-Shyong and Minami, Ryosuke and Rodriguez, Kirill and Shaker, George and Quigley, Aaron}, journal={Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies}, volume={2}, number={4}, pages={1--25}, year={2018}, publisher={ACM New York, NY, USA} }
https://github.com/sadreazami/RadarBreath
@article{nejadgholi2019classification, title={Classification of Doppler radar reflections as preprocessing for breathing rate monitoring}, author={Nejadgholi, Isar and Sadreazami, Hamidreza and Rajan, Sreeraman and Bolic, Miodrag}, journal={IET Signal Processing}, volume={13}, number={1}, pages={21--28}, year={2019}, publisher={IET} }
https://github.com/radar-lab/patient_monitoring
@inproceedings{jin2019multiple, title={Multiple patients behavior detection in real-time using mmWave radar and deep CNNs}, author={Jin, Feng and Zhang, Renyuan and Sengupta, Arindam and Cao, Siyang and Hariri, Salim and Agarwal, Nimit K and Agarwal, Sumit K}, booktitle={2019 IEEE Radar Conference (RadarConf)}, pages={1--6}, year={2019}, organization={IEEE} }
https://github.com/radar-lab/mmfall
@article{jin2020mmfall, title={mmFall: Fall Detection Using 4-D mmWave Radar and a Hybrid Variational RNN AutoEncoder}, author={Jin, Feng and Sengupta, Arindam and Cao, Siyang}, journal={IEEE Transactions on Automation Science and Engineering}, year={2020}, publisher={IEEE} }
https://github.com/senguptaa/mmpose
@article{sengupta2020mm, title={Mm-pose: Real-time human skeletal posture estimation using mmWave radars and CNNs}, author={Sengupta, Arindam and Jin, Feng and Zhang, Renyuan and Cao, Siyang}, journal={IEEE Sensors Journal}, volume={20}, number={17}, pages={10032--10044}, year={2020}, publisher={IEEE} }
https://github.com/radar-lab/mmPose-NLP
@inproceedings{sengupta2020nlp, title={NLP based Skeletal Pose Estimation using mmWave Radar Point-Cloud: A Simulation Approach}, author={Sengupta, Arindam and Jin, Feng and Cao, Siyang}, booktitle={2020 IEEE Radar Conference (RadarConf20)}, pages={1--6}, year={2020}, organization={IEEE} }
https://github.com/nesl/RadHAR
@inproceedings{singh2019radhar, title={Radhar: Human activity recognition from point clouds generated through a millimeter-wave radar}, author={Singh, Akash Deep and Sandha, Sandeep Singh and Garcia, Luis and Srivastava, Mani}, booktitle={Proceedings of the 3rd ACM Workshop on Millimeter-wave Networks and Sensing Systems}, pages={51--56}, year={2019} }
https://github.com/FIGLAB/Vid2Doppler
@inproceedings{ahuja2021vid2doppler, title={Vid2Doppler: Synthesizing Doppler Radar Data from Videos for Training Privacy-Preserving Activity Recognition}, author={Ahuja, Karan and Jiang, Yue and Goel, Mayank and Harrison, Chris}, booktitle={Proceedings of the 2021 CHI Conference on Human Factors in Computing Systems}, pages={1--10}, year={2021} }
https://github.com/JunshengFu/tracking-with-Extended-Kalman-Filter
https://github.com/JunshengFu/tracking-with-Unscented-Kalman-Filter
https://github.com/ApolloAuto/apollo/tree/master/modules/perception/radar