Luanmin Chen
Papers
1
Total Citations
8
H-Index
1
About
Luanmin Chen is a researcher whose work sits at the intersection of computer graphics, 3D shape analysis, and reinforcement learning. Their primary research focus is on developing intelligent algorithms for 3D geometry processing, with a particular emphasis on automatic orientation estimation of 3D shapes—a fundamental problem in computer vision and graphics. Chen’s most notable contribution is the introduction of "UprightRL," a novel framework that reframes the task of upright orientation estimation as a sequential decision-making problem solved via reinforcement learning. This approach, published in 2021, departs from traditional methods by teaching an agent to iteratively rotate a 3D shape to its correct upright position based on visual observations. While the paper has garnered 8 citations to date, its significance lies in its innovative methodological perspective, opening new avenues for applying RL to geometry understanding. Chen’s work is particularly valuable for students and researchers interested in the intersection of 3D perception and learning-based control, offering a fresh angle on a classic problem. Their research demonstrates how sequential decision-making can be leveraged to solve geometric tasks that were previously tackled with static, one-shot approaches.
Research Focus
Key Achievements
Top Papers
- 1