  Despite the success in 6D pose estimation in bin-picking scenarios, existing
methods still struggle to produce accurate prediction results for symmetry
objects and real world scenarios. The primary bottlenecks include 1) the
ambiguity keypoints caused by object symmetries; 2) the domain gap between real
and synthetic data. To circumvent these problem, we propose a new 6D pose
estimation network with symmetric-aware keypoint prediction and self-training
domain adaptation (SD-Net). SD-Net builds on pointwise keypoint regression and
deep hough voting to perform reliable detection keypoint under clutter and
occlusion. Specifically, at the keypoint prediction stage, we designe a robust
3D keypoints 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 keypoint to dynamically eliminate multiple ambiguity and outlier
keypoint candidates. At the domain adaptation stage, we propose the
self-training framework using a student-teacher training scheme. To carefully
distinguish reliable predictions, we harnesses a tailored heuristics for 3D
geometry pseudo labelling based on semi-chamfer distance. On public Sil'eane
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. The
code is available at https://github.com/dingthuang/SD-Net.
