  We introduce a novel 3D object proposal approach named Generative Shape
Proposal Network (GSPN) for instance segmentation in point cloud data. Instead
of treating object proposal as a direct bounding box regression problem, we
take an analysis-by-synthesis strategy and generate proposals by reconstructing
shapes from noisy observations in a scene. We incorporate GSPN into a novel 3D
instance segmentation framework named Region-based PointNet (R-PointNet) which
allows flexible proposal refinement and instance segmentation generation. We
achieve state-of-the-art performance on several 3D instance segmentation tasks.
The success of GSPN largely comes from its emphasis on geometric understandings
during object proposal, which greatly reducing proposals with low objectness.
