Papers
6
Total Citations
81
H-Index
5
About
Ligang Liu is a leading researcher in geometric modeling, computer graphics, and robotics, with a particular focus on autonomous reconstruction and 3D printing. His work bridges the gap between computational geometry and practical robotic applications, addressing fundamental challenges in how machines perceive and interact with physical environments. Liu's most impactful contributions include pioneering methods for grasp planning through hand-object geometric fitting (26 citations) and developing shape context-based mesh saliency detection techniques (21 citations). His recent groundbreaking work on ScanBot (2023, 11 citations) introduces deep reinforcement learning for autonomous environment reconstruction, tackling the critical challenge of balancing efficiency and quality in unknown environments. Liu has also advanced multi-robot collaborative scanning systems (2022, 10 citations) that intelligently switch between exploration and reconstruction modes. In additive manufacturing, his innovative work on robotic 3D printed polygon mesh (2016, 9 citations) proposes a novel strategy for spatial printing that moves beyond traditional layering approaches. As editor of the 2021 special section on Geometric Modeling and Processing, Liu continues to shape the field, demonstrating how geometric algorithms can drive next-generation autonomous systems and manufacturing technologies.
Research Focus
Key Achievements
Top Papers
- 1Grasp planning via hand-object geometric fitting26 citations · 2016
- 2Shape context based mesh saliency detection and its applications: A survey21 citations · 2016
- 3ScanBot: Autonomous Reconstruction via Deep Reinforcement Learning11 citations · 2023
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- 6Geometric Modeling and Processing4 citations · 2021