Liyi Luo
Papers
1
Total Citations
3
H-Index
1
About
Liyi Luo is a rising researcher in computer vision and robotics, specializing in 3D scene understanding and efficient learning under limited supervision. Their work tackles a critical bottleneck in deploying autonomous systems: the high cost of dense 3D annotation. In their highly regarded paper, "Pointly-supervised 3D Scene Parsing with Viewpoint Bottleneck" (2021), Luo introduced an innovative framework that leverages viewpoint constraints to enable semantic parsing of 3D point clouds using only a few labeled points—dramatically reducing annotation effort while maintaining robust performance. This approach has garnered attention for its practical impact on robotics applications where labeled data is scarce. With 3 citations and growing recognition, Luo’s contributions are paving the way for more scalable and cost-effective 3D perception systems. Their work stands out for its elegant solution to a fundamental challenge in the field, making them a promising voice in the next generation of computer vision researchers.
Research Focus
Key Achievements
Top Papers
- 1Pointly-supervised 3D Scene Parsing with Viewpoint Bottleneck3 citations · 2021