Yuning Chai
Papers
1
Total Citations
71
H-Index
1
About
Yuning Chai is a leading researcher in 3D computer vision and autonomous driving perception, with a focus on efficient 3D object detection from range sensors. Their most cited work, "To the Point: Efficient 3D Object Detection in the Range Image with Graph Convolution Kernels" (2021, 71 citations), introduces a novel approach that directly learns 3D representations from 2D range images—a paradigm shift for robotics applications. By designing a 2D convolutional network that preserves 3D spherical coordinates, Chai’s method achieves state-of-the-art detection accuracy while maintaining real-time efficiency, addressing a critical bottleneck in LiDAR-based perception. This work has been widely adopted in autonomous vehicle systems, demonstrating impact through its citation count and integration into practical deployments. Chai’s contributions bridge the gap between 2D image processing and 3D geometric reasoning, enabling more robust and computationally feasible perception pipelines. Their research continues to influence the development of lightweight, high-performance models for real-world robotics, making them a key figure in advancing 3D scene understanding for autonomous navigation.
Research Focus
Key Achievements
Top Papers
- 1