Synthetic data enables faster annotation and robust segmentation for
  multi-object grasping in clutter
Dongmyoung Lee Wei Chen Nicolas Rojas
Object recognition and object pose estimation in robotic grasping continue to
be significant challenges, since building a labelled dataset can be time
consuming and financially costly in terms of data collection and annotation. In
this work, we propose a synthetic data generation method that minimizes human
intervention and makes downstream image segmentation algorithms more robust by
combining a generated synthetic dataset with a smaller real-world dataset
(hybrid dataset). Annotation experiments show that the proposed synthetic scene
generation can diminish labelling time dramatically. RGB image segmentation is
trained with hybrid dataset and combined with depth information to produce
pixel-to-point correspondence of individual segmented objects. The object to
grasp is then determined by the confidence score of the segmentation algorithm.
Pick-and-place experiments demonstrate that segmentation trained on our hybrid
dataset (98.9%, 70%) outperforms the real dataset and a publicly available
dataset by (6.7%, 18.8%) and (2.8%, 10%) in terms of labelling and grasping
success rate, respectively. Supplementary material is available at
https://sites.google.com/view/synthetic-dataset-generation.