Lianfang Tian

University of California, Riverside, Ministry of Natural Resources, Cloud Computing Center

Papers

Total Citations

184

H-Index

About

Lianfang Tian is a researcher whose work spans robotics, trajectory planning, and advanced 3D visual representation learning. With a career bridging foundational robotics and cutting-edge deep learning, Tian has made meaningful contributions to both classical and modern computational challenges. Their most recognized work, "An effective robot trajectory planning method using a genetic algorithm" (2003), demonstrated the power of evolutionary computation in solving complex robotic motion planning problems, accumulating 179 citations and establishing Tian as a credible voice in intelligent robotics. More recently, Tian has pivoted toward 3D point cloud understanding and multi-view depth representation, reflecting a keen awareness of the field's evolving frontiers. The 2024 paper "MD-Mamba: Feature extractor on 3D representation with multi-view depth" explores state-space models for 3D data, while the 2025 work on graph neural networks introduces a novel historical node state increment mechanism to enrich point cloud feature learning. Together, these contributions illustrate a researcher with intellectual range — connecting decades-old optimization principles to the latest advances in geometric deep learning and 3D scene understanding.