Xiaoyuan Luo

Fudan University, Shanghai Medical College of Fudan University

Papers

Total Citations

278

H-Index

About

Xiaoyuan Luo is a leading researcher in computer vision and robotics, with a primary focus on 3D point cloud registration—a fundamental challenge for applications like autonomous navigation and 3D reconstruction. Luo’s most impactful contribution is the development of a robust point cloud registration framework based on deep graph matching, which directly addresses the critical problem of outlier sensitivity in learning-based methods. This work, published in 2021, has garnered 233 citations, underscoring its significance in advancing the field. By integrating graph neural networks with correspondence matching, Luo’s framework achieves superior resilience to noise and mismatches, setting a new standard for accuracy in real-world scenarios. A subsequent 2022 iteration (45 citations) further refines these techniques, demonstrating ongoing innovation. Luo’s research bridges theoretical advances and practical deployment, making them a key figure in the evolution of 3D vision systems. Their work is essential reading for students and researchers tackling robust geometric alignment, offering both foundational insights and state-of-the-art solutions.