L. Yi, W. Zhao, H. Wang, M. Sung, L. Guibas, GSPN: Generative Shape Proposal Network for 3D Instance Segmentation in Point Cloud, Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. 2019

Abstract:

We introduce a novel 3D object proposal approach named Generative Shape Proposal Network (GSPN) for in- stance 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 in- stance segmentation framework named Region-based Point- Net (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.

Bibtex: 

@article{yzwsg-ggspn3ispc-19,
  title={GSPN: Generative Shape Proposal Network for 3D Instance Segmentation in Point Cloud},
  author={Yi, Li and Zhao, Wang and Wang, He and Sung, Minhyuk and Guibas, Leonidas},
  booktitle={Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition},
  year={2019}
}