Congratulations to Mr. Qiwei Meng, student of Prof. Yun-Hui Liu, on receiving the Best AI-based Smart Manufacturing Systems Paper in Technology from the IEEE Systems, Man, and Cybernetics Society Technical Committee on AI-based Smart Manufacturing Systems!

Authors:
Qiwei Meng (Ph.D. student, Department of Mechanical and Automation Engineering, CUHK)
Jason Gu (Professor, Department of Electrical and Computer Engineering, Dalhousie University)
Yun-Hui Liu (Professor, Department of Mechanical and Automation Engineering, CUHK)

Paper Title: GPD: Learning Geometric Primitive Deformation for Unseen Object Pose Estimation

Published in: IEEE Transactions on Automation Science and Engineering, vol. 22, pp. 9903-9922, 2025

Abstract:
Witnessing the rapid progress and development in instance-level object pose estimation, increasing attention has shifted to the more challenging problem for unseen objects, which is in great demand for various robotic applications. In this paper, we propose the GPD, a novel framework for unseen object pose estimation, including both category-level and cross-category objects. The key innovation of the GPD model is the effective utilization of geometric primitives in target reconstruction and pose estimation, as it can generalize the learned primitive deformation across intra-class and inter-class instances. Additionally, we also design an advanced scheme for representative object feature extraction, including attention-aware excitation, multi-scale fusion, and semantic feature encoding. Extensive evaluations validate the effectiveness of individual innovation modules and the overall superior performance of the GPD. It not only achieves the SOTA results on category-level benchmarks CAMERA25 and REAL275 but also demonstrates impressive generalization ability across novel objects on the GraspNet-1Billion dataset. Furthermore, we deploy the trained GPD model for vision-guided robotic grasping experiments in simulation and real-world settings, again exhibiting its outstanding robustness and practicability in robotic manipulations.

The full paper is available at https://ieeexplore.ieee.org/abstract/document/10795245.

Experimental platform from the Hong Kong Centre for Logistics Robotics and the CUHK T Stone Robotics Institute.