High Resolution Radar Dataset for Semi-Supervised Learning of Dynamic Objects-Reference-Cited by-同舟云学术

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High Resolution Radar Dataset for Semi-Supervised Learning of Dynamic Objects

Published:2020-06 Issue: Volume: Page:
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Container-title:2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW)
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Author:

Mostajabi Mohammadreza,Wang Ching Ming,Ranjan Darsh,Hsyu Gilbert

Publisher

IEEE

Link

http://xplorestaging.ieee.org/ielx7/9142289/9150305/09150648.pdf?arnumber=9150648

Cited by 27 articles. 订阅此论文施引文献订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献

1. LiRaFusion: Deep Adaptive LiDAR-Radar Fusion for 3D Object Detection;2024 IEEE International Conference on Robotics and Automation (ICRA);2024-05-13

2. Automatic Multipath Annotation for Conventional Automotive Radar Datasets;IEEE Sensors Journal;2024-04-15

3. Geometric information constraint 3D object detection from LiDAR point cloud for autonomous vehicles under adverse weather;Transportation Research Part C: Emerging Technologies;2024-04

4. Survey of Deep Learning-Based Methods for FMCW Radar Odometry and Ego-Localization;Applied Sciences;2024-03-08

5. Utilizing Super-Resolution for Enhanced Automotive Radar Object Detection;2023 IEEE International Conference on Image Processing (ICIP);2023-10-08

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