Generalizable Zero-Shot Object Pose Estimation for Bin-Picking

Abstract

Unordered grasping in industrial robotic manipulation requires precise six-degree-of-freedom (6D) pose estimation. However, existing methods often struggle with unknown objects and require retraining, limiting their practicality. Traditional 3D point-pair feature methods, while training-free, perform poorly with textured symmetric objects. We propose a generalizable approach for zero-shot 6 D pose estimation without retraining. Our method consists of two steps: generating CAD-based templates through real-time rendering for coarse pose estimation, and refining poses using semantic point-pair features aligned with the camera viewpoint. We conducted experiments on seven core datasets from the Benchmark for 6D Object Pose Estimation (BOP) challenge, and the results are publicly available on the BOP website. Integration into a robotic grasping system further highlights its high precision and fast execution, making it ideal for applications such as bin-picking. (GZS6D-BP) https://bop.felk.cvut.cz/leaderboards/.