4 papers
Unlocking Generalization Power in LiDAR Point Cloud Registration
Zhenxuan Zeng, Qiao Wu, Xiyu Zhang +5
In real-world environments, a LiDAR point cloud registration method with robust generalization capabilities (across varying distances and datasets) is crucial for ensuring safety i…
SPU-IMR: Self-supervised Arbitrary-scale Point Cloud Upsampling via Iterative Mask-recovery Network
Ziming Nie, Qiao Wu, Chenlei Lv +4
Point cloud upsampling aims to generate dense and uniformly distributed point sets from sparse point clouds. Existing point cloud upsampling methods typically approach the task as…
3D Single-object Tracking in Point Clouds with High Temporal Variation
Qiao Wu, Kun Sun, Pei An +3
The high temporal variation of the point clouds is the key challenge of 3D single-object tracking (3D SOT). Existing approaches rely on the assumption that the shape variation of t…
MixCycle: Mixup Assisted Semi-Supervised 3D Single Object Tracking with Cycle Consistency
Qiao Wu, Jiaqi Yang, Kun Sun +3
3D single object tracking (SOT) is an indispensable part of automated driving. Existing approaches rely heavily on large, densely labeled datasets. However, annotating point clouds…