ParametricNet++: A 6DoF Pose Estimation Network with Sparse Keypoint Recovery for Parametric Shapes in Stacked Scenarios

Abstract

Most industrial parts are designed from parametric shapes with the properties of diversity and uncertainty. We propose a 6DoF pose estimation network, ParametricNet++, based on pointwise regression and sparse keypoint recovery, which is extended from ParametricNet to include optimizations of keypoint selection and prediction. Keypoint selection optimization selects geometrically unique keypoints and keypoint groups to reduce the difficulty of scene keypoint prediction and template keypoint recovery. Keypoint prediction optimization predicts keypoints from rough to precise, which improves the accuracy of scene keypoint prediction and template keypoint recovery. Compared with other state-of-the-art methods, the average of APs of ParametricNet++ is improved by over 15% on the public Siléane dataset, and the average of mAPs is improved by 12% and 14% on L-dataset and G-dataset from Parametric dataset, respectively. In particular, ParametricNet++ outperforms our original ParametricNet by 5% for both learning and generalization ability evaluation on the Parametric dataset. The experimental results demonstrate that ParametricNet++ lays a solid foundation for robot grasping in industrial scenarios.