Nannan Qin

Nanjing University of Information Science and Technology

Papers

Total Citations

H-Index

About

Dr. Nannan Qin is a leading researcher in 3D computer vision and geometric deep learning, with a primary focus on point cloud processing and completion. Her work addresses the fundamental challenge of reconstructing complete 3D shapes from partial, noisy sensor data—a critical problem for applications in autonomous driving, robotics, and medical imaging. Her landmark survey, "A Survey of Point Cloud Completion" (2024), has already garnered 32 citations, establishing itself as a definitive reference in the field. This comprehensive work systematically categorizes completion methods, from traditional optimization to deep learning-based approaches, and identifies key challenges such as handling disordered point sets and preserving fine geometric details. Dr. Qin's contributions extend beyond surveys; she has developed novel architectures that leverage attention mechanisms and generative models to achieve state-of-the-art completion accuracy on benchmark datasets. Her research has significant practical implications, enabling more reliable 3D modeling for remote sensing and improving robot perception in cluttered environments. With her work bridging the gap between theoretical advances and real-world deployment, Dr. Qin is shaping the future of 3D scene understanding and reconstruction.