A 6DoF Pose Estimation Dataset and Network for Multiple Parametric Shapes in Stacked Scenarios

Abstract

Most industrial parts are instantiated from different parametric templates. The 6DoF (6D) pose estimation tasks are challenging, since some part objects from a known template may be unseen before. This paper releases a new and well-annotated 6D pose estimation dataset for multiple parametric templates in stacked scenarios donated as Multi-Parametric Dataset, where a training set (50K scenes) and a test set (2K scenes) are obtained by automatical labeling techniques. In particular, the test set is further divided into a TEST-L dataset for learning evaluation and a TEST-G dataset for generalization evaluation. Since the part objects from the same template are regarded as a class in the Multi-Parametric Dataset and the number of part objects is infinite, we propose a new 6D pose estimation network as our baseline method, Multi-templates Parametric Pose Network (MPP-Net), aiming to have sufficient generalization ability for parametric part objects in stacked scenarios. To our best knowledge, our dataset and method are the first to jointly achieve 6D pose estimation and parameter values prediction for multiple parametric templates. Many experiments are conducted on the Multi-Parametric Dataset. The mIoU and Overall Accuracy of foreground segmentation and template segmentation on the two test datasets exceed 99.0%. Besides, MPP-Net achieves 92.9% and 90.8% on mAP under the threshold of 0.5cm for translation prediction, achieves 41.9% and 36.8% under the threshold of 5∘ for rotation prediction, and achieves 51.0% and 6.0% under the threshold of 5% for parameter values prediction, on the two test set, respectively. The results have shown that our dataset has exploratory value for 6D pose estimation and parameter values prediction tasks.