4 papers
MLF-4DRCNet: Multi-Level Fusion with 4D Radar and Camera for 3D Object Detection in Autonomous Driving
Yuzhi Wu, Li Xiao, Jun Liu +2
The emerging 4D millimeter-wave radar, measuring the range, azimuth, elevation, and Doppler velocity of objects, is recognized for its cost-effectiveness and robustness in autonomo…
You Only Click Once: Single Point Weakly Supervised 3D Instance Segmentation for Autonomous Driving
Guangfeng Jiang, Jun Liu, Yongxuan Lv +5
Outdoor LiDAR point cloud 3D instance segmentation is a crucial task in autonomous driving. However, it requires laborious human efforts to annotate the point cloud for training a…
Mask-RadarNet: Enhancing Transformer With Spatial-Temporal Semantic Context for Radar Object Detection in Autonomous Driving
Yuzhi Wu, Jun Liu, Guangfeng Jiang +2
As a cost-effective and robust technology, automotive radar has seen steady improvement during the last years, making it an appealing complement to commonly used sensors like camer…
MWSIS: Multimodal Weakly Supervised Instance Segmentation with 2D Box Annotations for Autonomous Driving
Guangfeng Jiang, Jun Liu, Yuzhi Wu +3
Instance segmentation is a fundamental research in computer vision, especially in autonomous driving. However, manual mask annotation for instance segmentation is quite time-consum…