4 papers
MSDNet: Efficient 4D Radar Super-Resolution via Multi-Stage Distillation
Minqing Huang, Shouyi Lu, Boyuan Zheng +3
4D radar super-resolution, which aims to reconstruct sparse and noisy point clouds into dense and geometrically consistent representations, is a foundational problem in autonomous…
4DRadar-GS: Self-Supervised Dynamic Driving Scene Reconstruction with 4D Radar
Xiao Tang, Guirong Zhuo, Cong Wang +5
3D reconstruction and novel view synthesis are critical for validating autonomous driving systems and training advanced perception models. Recent self-supervised methods have gaine…
TDFANet: Encoding Sequential 4D Radar Point Clouds Using Trajectory-Guided Deformable Feature Aggregation for Place Recognition
Shouyi Lu, Guirong Zhuo, Haitao Wang +6
Place recognition is essential for achieving closed-loop or global positioning in autonomous vehicles and mobile robots. Despite recent advancements in place recognition using 2D c…
R2LDM: An Efficient 4D Radar Super-Resolution Framework Leveraging Diffusion Model
Boyuan Zheng, Shouyi Lu, Renbo Huang +5
We introduce R2LDM, an innovative approach for generating dense and accurate 4D radar point clouds, guided by corresponding LiDAR point clouds. Instead of utilizing range images or…