4 citations · 7 across the 10 of their papers we have counts for
4 papers · 1 filter
SegNet4D: Efficient Instance-Aware 4D Semantic Segmentation for LiDAR Point Cloud
Neng Wang, Ruibin Guo, Chenghao Shi +5
4D LiDAR semantic segmentation, also referred to as multi-scan semantic segmentation, plays a crucial role in enhancing the environmental understanding capabilities of autonomous v…
Diffusion-Based Point Cloud Super-Resolution for mmWave Radar Data
Kai Luan, Chenghao Shi, Neng Wang +3
The millimeter-wave radar sensor maintains stable performance under adverse environmental conditions, making it a promising solution for all-weather perception tasks, such as outdo…
RDMNet: Reliable Dense Matching Based Point Cloud Registration for Autonomous Driving
Chenghao Shi, Xieyuanli Chen, Huimin Lu +3
Point cloud registration is an important task in robotics and autonomous driving to estimate the ego-motion of the vehicle. Recent advances following the coarse-to-fine manner show…
InsMOS: Instance-Aware Moving Object Segmentation in LiDAR Data
Neng Wang, Chenghao Shi, Ruibin Guo +3
Identifying moving objects is a crucial capability for autonomous navigation, consistent map generation, and future trajectory prediction of objects. In this paper, we propose a no…