Abstract
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. 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