Yiheng Huang
Papers
1
Total Citations
4
H-Index
1
About
Yiheng Huang is a researcher focused on advancing computer vision and robotic perception, with a particular emphasis on depth sensing and manipulation for transparent objects. His key contributions lie in addressing the fundamental challenge of depth completion for transparent surfaces, which are notoriously difficult for standard RGB-D cameras due to reflection and refraction artifacts. Huang’s most notable work, "DistillGrasp: Integrating Features Correlation With Knowledge Distillation for Depth Completion of Transparent Objects" (2024, 4 citations), introduces an innovative framework that leverages feature correlation and knowledge distillation to reconstruct missing depth data, enabling more reliable robotic grasping. This approach not only improves depth map accuracy but also reduces model complexity, making it practical for real-world applications. Huang’s research has significant implications for automation in industries like logistics and manufacturing, where transparent materials are common. His work demonstrates a strong ability to bridge the gap between theoretical advances and deployable solutions, earning recognition for its novelty and potential impact. As an emerging voice in perception-driven robotics, Huang continues to push the boundaries of how machines interpret and interact with visually challenging environments.
Research Focus
Key Achievements
Top Papers
- 1