Yik Hong Cai
Papers
1
Total Citations
4
H-Index
1
About
Yik Hong Cai is a rising researcher at the forefront of efficient deep learning for edge computing, with a focus on point cloud analytics and 3D object detection. His most-cited work, "Moby: Empowering 2D Models for Efficient Point Cloud Analytics on the Edge" (2023, 4 citations), addresses a critical challenge in autonomous driving and robotics: deploying accurate 3D perception on resource-constrained edge devices. Cai’s key contribution lies in bridging the gap between powerful 2D models and 3D point cloud data, enabling near real-time object detection without sacrificing performance. By leveraging 2D architectures, his approach reduces computational overhead while maintaining high accuracy, making it practical for real-world applications where latency and power are limited. This work has already garnered attention for its potential to democratize 3D perception in edge environments. Cai’s research sits at the intersection of computer vision, embedded systems, and efficient AI, offering scalable solutions for autonomous systems. As a young scholar, his innovative methodology—repurposing mature 2D models for 3D tasks—marks a significant step toward practical, low-latency autonomous navigation, positioning him as a promising voice in edge AI and robotics.
Research Focus
Key Achievements
Top Papers
- 1