TGVPose: Lightweight Category-Level Object Pose Estimation via Topology Graph-Guided Adaptive Graph Vector Networks

Object pose estimation serves as a crucial approach to enabling reliable robotic grasping. To achieve high generalization in real-world scenarios, existing pose estimation methods commonly adopt category-level object pose estimation strategies. However, current high-precision methods often suffer from large parameter volumes, rendering them difficult to deploy on computation-constrained edge devices such as mobile robots and unmanned aerial vehicles (UAVs). To address this issue, this paper proposes TGVPose, a high-precision and lightweight category-level object pose estimation method via topology graph-guided adaptive graph vector networks. First, to overcome the fixed-graph limitation inherent in conventional graph vector networks, we introduce an Adaptive Multi-Relation Gated Vector Neuron Layer (AMG-VN), which enables the network to adaptively extract both local and global features. Second, to enhance the network's understanding of object shapes, we present a category-prototype persistent topology conditioning method, thereby achieving accurate object pose estimation. Finally, we evaluate TGVPose across multiple datasets and real-world object pose estimation tasks. The experimental results demonstrate that TGVPose effectively balances accuracy and lightweight efficiency, delivering superior pose estimation performance compared to existing lightweight methods.