  We introduce Similarity Group Proposal Network (SGPN), a simple and intuitive
deep learning framework for 3D object instance segmentation on point clouds.
SGPN uses a single network to predict point grouping proposals and a
corresponding semantic class for each proposal, from which we can directly
extract instance segmentation results. Important to the effectiveness of SGPN
is its novel representation of 3D instance segmentation results in the form of
a similarity matrix that indicates the similarity between each pair of points
in embedded feature space, thus producing an accurate grouping proposal for
each point. To the best of our knowledge, SGPN is the first framework to learn
3D instance-aware semantic segmentation on point clouds. Experimental results
on various 3D scenes show the effectiveness of our method on 3D instance
segmentation, and we also evaluate the capability of SGPN to improve 3D object
detection and semantic segmentation results. We also demonstrate its
flexibility by seamlessly incorporating 2D CNN features into the framework to
boost performance.
