GPD: Learning Geometric Primitive Deformation for Unseen Object Pose Estimation

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. Note to Practitioners—This paper is motivated by the problem of unseen object pose estimation and robotic manipulation in unstructured environments. For intelligent robots expected to interact with their surroundings, rather than just passively perceiving them like surveillance cameras, 6Dof pose estimation is a critical capability. However, existing approaches generally face two key challenges. On the one hand, robots are likely to encounter unseen objects in real-world applications. Without the availability of prior models or specific training data for these unseen objects, instance-level and category-level methods may become ineffective or even fail to work. On the other hand, the error tolerance of precise tabletop robotic manipulation is very tight, and the varying lighting conditions and background noise impose higher robustness requirements on pose estimation algorithms. To address these difficulties, we propose a novel network that learns geometric primitive deformation for pose estimation. This model is less dependent on object prior information, thereby enhancing the generalization ability. Additionally, by incorporating cross-modal excitation and multi-scale fusion during feature extraction, our model can capture representative appearance and geometric information of objects for accurate pose estimation. Extensive experimental results on benchmark datasets quantitatively validate the superior performance of our approach. We also demonstrate its effectiveness in robotic applications through unseen object grasping experiments on Kinova and Franka Emika robot platforms. In the future, we plan to explore primitive combination schemes for compound object representation, enabling pose estimation for more complex-shaped objects.