  The ability to segment unknown objects in depth images has potential to
enhance robot skills in grasping and object tracking. Recent computer vision
research has demonstrated that Mask R-CNN can be trained to segment specific
categories of objects in RGB images when massive hand-labeled datasets are
available. As generating these datasets is time consuming, we instead train
with synthetic depth images. Many robots now use depth sensors, and recent
results suggest training on synthetic depth data can transfer successfully to
the real world. We present a method for automated dataset generation and
rapidly generate a synthetic training dataset of 50,000 depth images and
320,000 object masks using simulated heaps of 3D CAD models. We train a variant
of Mask R-CNN with domain randomization on the generated dataset to perform
category-agnostic instance segmentation without any hand-labeled data and we
evaluate the trained network, which we refer to as Synthetic Depth (SD) Mask
R-CNN, on a set of real, high-resolution depth images of challenging,
densely-cluttered bins containing objects with highly-varied geometry. SD Mask
R-CNN outperforms point cloud clustering baselines by an absolute 15% in
Average Precision and 20% in Average Recall on COCO benchmarks, and achieves
performance levels similar to a Mask R-CNN trained on a massive, hand-labeled
RGB dataset and fine-tuned on real images from the experimental setup. We
deploy the model in an instance-specific grasping pipeline to demonstrate its
usefulness in a robotics application. Code, the synthetic training dataset, and
supplementary material are available at https://bit.ly/2letCuE.
