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 Point-Net (R-PointNet) which allows flexible proposal refinement and instance segmentation generation. We achieve state-ofthe-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.
GSPN REF introduces a generative shape proposal network and relies on object proposals to identify instances in 3D point clouds.
GSPN: Generative Shape Proposal Network for 3D Instance Segmentation in Point Cloud
2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)
2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)
Computer Science