Junhui Wu

Papers

Total Citations

H-Index

About

Junhui Wu’s research lies at the intersection of computer vision, robotics, and autonomous systems, with a particular focus on 3D perception and intelligent navigation. Wu is best known for a highly cited survey on monocular 3D object detection algorithms based on deep learning, which has garnered 20 citations and serves as a foundational reference for researchers working on single-camera depth estimation and spatial reasoning for autonomous vehicles. This work systematically reviews state-of-the-art deep learning approaches, addressing key challenges in inferring 3D object location and pose from 2D images—a critical capability for cost-effective perception in self-driving cars and mobile robots. Wu has also contributed to the application of computer vision in agriculture, exploring how visual recognition can guide fruit-picking robots, and to mobile robotics through studies on complete coverage path planning and obstacle avoidance. By bridging theoretical algorithm analysis with practical robotic tasks, Wu’s work supports the development of more autonomous, perceptive, and efficient machines. Their research continues to influence both academic understanding and real-world deployment in robotics and intelligent transportation.