  Segmenting unseen objects in cluttered scenes is an important skill that
robots need to acquire in order to perform tasks in new environments. In this
work, we propose a new method for unseen object instance segmentation by
learning RGB-D feature embeddings from synthetic data. A metric learning loss
function is utilized to learn to produce pixel-wise feature embeddings such
that pixels from the same object are close to each other and pixels from
different objects are separated in the embedding space. With the learned
feature embeddings, a mean shift clustering algorithm can be applied to
discover and segment unseen objects. We further improve the segmentation
accuracy with a new two-stage clustering algorithm. Our method demonstrates
that non-photorealistic synthetic RGB and depth images can be used to learn
feature embeddings that transfer well to real-world images for unseen object
instance segmentation.
