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We introduce a novel 3D object proposal approach named Generative ShapeProposal Network (GSPN) for instance segmentation in point cloud data. Insteadof treating object proposal as a direct bounding box regression problem, wetake an analysis-by-synthesis strategy and generate proposals by reconstructingshapes from noisy observations in a scene. We incorporate GSPN into a novel 3Dinstance segmentation framework named Region-based PointNet (R-PointNet) whichallows flexible proposal refinement and instance segmentation generation. Weachieve state-of-the-art performance on several 3D instance segmentation tasks.The success of GSPN largely comes from its emphasis on geometric understandingsduring object proposal, which greatly reducing proposals with low objectness.

GSPN: Generative Shape Proposal Network for 3D Instance Segmentation in Point Cloud