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

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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.
Publisher
IEEE COMPUTER SOC
Issue Date
2019-06
Language
English
Citation

32nd IEEE/CVF Conference on Computer Vision and Pattern Recognition, CVPR 2019, pp.3942 - 3951

ISSN
1063-6919
DOI
10.1109/CVPR.2019.00407
URI
http://hdl.handle.net/10203/280956
Appears in Collection
CS-Conference Papers(학술회의논문)
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