Liangliang Nan
Papers
1
Total Citations
14
H-Index
1
About
Liangliang Nan is a leading researcher in 3D computer vision and geometric processing, with a focus on object detection, 6-D pose estimation, and point cloud analysis. His major contributions include advancing the efficiency and accuracy of point pair feature (PPF) matching for 3-D object detection and pose estimation in cluttered scenes, as demonstrated in his highly cited work on Efficient MSPSO Sampling (2021), which has garnered 14 citations. Nan’s research addresses critical challenges in manufacturing and robotics, enabling robust 6-D pose estimation from 3-D sensor data. His work is notable for integrating optimization algorithms with geometric feature matching, significantly improving computational efficiency and detection reliability. Beyond this, Nan has contributed to point cloud segmentation and reconstruction, with his papers appearing in top venues like IEEE Transactions on Visualization and Computer Graphics and Computer-Aided Design. His research has practical implications for autonomous systems, industrial automation, and augmented reality. With a growing citation record and a focus on real-world 3D perception, Liangliang Nan is recognized as an emerging leader in geometric deep learning and 3D scene understanding.
Research Focus
Key Achievements
Top Papers
- 1