Ting Luo
Papers
1
Total Citations
6
H-Index
1
About
Ting Luo is a rising star in computer vision, whose work tackles critical challenges in perception for autonomous systems and robotics. Her research centers on developing robust deep learning models for scene understanding, with a particular focus on handling difficult reflective and transparent surfaces. Luo’s most notable contribution is her pioneering work on RGB-D mirror segmentation, where she proposed a novel Neighborhood-Matching and Demand-Modal Adaptive Network. This architecture, enhanced by knowledge distillation, achieves state-of-the-art performance in identifying mirrors—a notoriously difficult task due to occlusion, reflection, and distortion. Her 2025 paper on this topic has already garnered 6 citations, signaling its immediate impact on the field. By enabling autonomous vehicles and robots to better perceive their environments, Luo’s research addresses a fundamental gap in computer vision. Her innovative approach to multi-modal learning and knowledge transfer positions her as a key contributor to safer, more reliable autonomous navigation systems.
Research Focus
Key Achievements
Top Papers
- 1