Mingyu Jeong
Papers
1
Total Citations
7
H-Index
1
About
Mingyu Jeong is a leading researcher in computer vision and autonomous systems, with a primary focus on depth completion—a critical technology for estimating dense depth information from sparse sensor data. His most cited work, the 2024 paper "ADNet: Non-Local Affinity Distillation Network for Lightweight Depth Completion With Guidance From Missing LiDAR Points," introduces an innovative lightweight architecture that leverages non-local affinity distillation to enhance depth estimation accuracy. This contribution directly addresses the stringent real-time and accuracy requirements of autonomous driving, robotics, and augmented reality applications. Jeong’s approach uniquely utilizes guidance from missing LiDAR points, enabling robust performance even in challenging environments with sparse sensor coverage. With 7 citations already in its first year, this work demonstrates significant early impact in the field. Jeong’s research bridges the gap between computational efficiency and high-fidelity depth perception, making him a notable figure in advancing practical, deployable solutions for next-generation autonomous systems. His ongoing contributions continue to shape how machines perceive and interact with three-dimensional spaces.
Research Focus
Key Achievements
Top Papers
- 1