SD-Net: Symmetric-Aware Keypoint Prediction and Domain Adaptation for 6D Pose Estimation In Bin-picking Scenarios

Abstract

Despite the success of 6D pose estimation in bin-picking scenarios, existing methods still struggle to produce accurate prediction results for symmetry objects in real-world scenarios. The primary bottlenecks include 1) the ambiguity in keypoints caused by object symmetries; and 2) the domain gap between real and synthetic data. To circumvent these problems, we propose a novel 6D pose estimation network with symmetric-aware keypoint prediction and self-training domain adaptation (SD-Net). SD-Net builds on point-wise keypoint regression and deep hough voting to perform reliable keypoint detection under clutter and occlusion. Specifically, at the keypoint prediction stage, we propose a robust 3D keypoint selection strategy considering the symmetry class of objects and equivalent keypoints, which facilitate locating 3D keypoints even in highly occluded scenes. Additionally, we build an effective filtering algorithm on predicted keypoints to dynamically eliminate multiple ambiguity and outlier key-point candidates. At the domain adaptation stage, we propose the self-training framework using a student-teacher training scheme. To carefully distinguish reliable predictions, we harness tailored heuristics for 3D geometry pseudo labelling based on semi-chamfer distance. On the public Siléane dataset, SD-Net achieves state-of-the-art results, obtaining an average precision of 96%. Testing learning and generalization abilities on public Parametric datasets, SD-Net is 8% higher than the state-of-the-art method.