Xiaohui Lu — accepted AI-conference papers

2026

GPR-Net: Geometric-Positional Collaborative Point Cloud Registration Network for Repetitive Geometric Structures

RA-L 2026

Point cloud registration is a critical task for building complete 3D models. Existing registration methods primarily focus on correspondence relationships between points, often using local geometric features to determine matches. However, this approach generates numerous false matches in scenes with

Cited by 0 Source ↗Scholar ↗

2024

SOCR: Simultaneous Overlap Prediction and Correspondence Estimation for Point Cloud Registration in Real-Time

Zhiwei Shen, Xiaohui Lu, Yaning Wang, Jianhui Nie

RA-L 2024

Point cloud registration is a crucial task in 3D computer vision. Correspondence-based methods highly rely on the quality of matching points. However, existing methods still suffer from low efficiency, precision, and recall. This letter introduces SOCR, a method that can perform overlap prediction a

Cited by 1 Source ↗Scholar ↗