Learning Symmetry-Aware Geometry Correspondences for 6D Object Pose Estimation

Current 6D pose estimation methods focus on handling objects that are previously trained, which limits their applications in real dynamic world. To this end, we propose a geometry correspondence-based framework, termed GCPose, to estimate 6D pose of arbitrary unseen objects without any re-training. Specifically, the proposed method draws the idea from point cloud registration and resorts to object-agnostic geometry features to establish the 3D-3D correspondences between the object-scene point cloud and object-model point cloud. Then the 6D pose parameters are solved by a least-squares fitting algorithm. Taking the symmetry properties of objects into consideration, we design a symmetry-aware matching loss to facilitate the learning of dense point-wise geometry features and improve the performance considerably. Moreover, we introduce an online training data generation with special data augmentation and normalization to empower the network to learn diverse geometry prior. With training on synthetic objects from ShapeNet, our method outperforms previous approaches for unseen object pose estimation by a large margin on T-LESS, LINEMOD, Occluded-LINEMOD, and TUD-L datasets. Code is available at https://github.com/hikvision-research/GCPose.