Chongpei Liu
Papers
10
Total Citations
191
H-Index
8
About
Chongpei Liu is a leading researcher in robotic manipulation and computer vision, with a primary focus on 6-degree-of-freedom (6DoF) and 9-degree-of-freedom (9DoF) object pose estimation for industrial automation and human-robot interaction. His major contributions include developing robust, category-level pose estimation systems that enable robots to grasp unknown objects without requiring extensive labeled real-world data. Notably, his "Robotic Continuous Grasping System by Shape Transformer-Guided Multiobject Category-Level 6-D Pose Estimation" (2023, 54 citations) and "HFF6D: Hierarchical Feature Fusion Network for Robust 6D Object Pose Tracking" (2022, 41 citations) have set benchmarks in challenging scenes involving occlusions and sudden re-orientations. Liu has also pioneered domain-generalized approaches, such as "Diff9D: Diffusion-Based Domain-Generalized Category-Level 9-DoF Object Pose Estimation" (2025), which reduces reliance on real-world training data. His work extends to tactile sensing for assessing fruit hardness and grasp stability, integrating vision and touch for multimodal perception. With over 190 total citations and a comprehensive survey on deep learning-based pose estimation (2026), Liu's research is pivotal for advancing reliable, generalizable robotic grasping in unstructured environments.
Research Focus
Key Achievements
Top Papers
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- 5Deep Learning-Based Object Pose Estimation: A Comprehensive Survey16 citations · 2026
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