3 papers
cs.CV2025
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…
eess.IV2025
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…
cs.CV2024
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…