Jiayong Ye

Papers

Total Citations

H-Index

About

Jiayong Ye is a researcher whose work lies at the intersection of computer vision and deep learning, with a particular focus on 3D perception and object pose estimation. His most cited paper, "Graph neural network for 6D object pose estimation" (2021, 27 citations), introduces a novel approach that leverages graph neural networks to infer the full six-degree-of-freedom pose of objects from visual data. This work addresses a critical challenge in robotics and augmented reality, where accurate pose estimation is essential for interaction and manipulation. By modeling the spatial relationships between object parts as a graph, Ye's method achieves robust performance even under occlusion and clutter, offering a significant advancement over traditional convolutional or point-based techniques. While his citation count reflects the early stage of his career, the conceptual innovation of integrating graph neural networks into pose estimation has already influenced subsequent research in the field. Ye's contributions are particularly valuable for applications requiring precise object alignment, such as autonomous grasping and scene understanding, marking him as an emerging voice in geometric deep learning.