• DOI:10.1007/978-3-030-01267-0_37
Corpus ID: 50770050

3DFeat-Net: Weakly Supervised Local 3D Features for Point Cloud Registration

@article{Yew20183DFeatNetWS,
  title={3DFeat-Net: Weakly Supervised Local 3D Features for Point Cloud Registration},
  author={Zi Jian Yew and Gim Hee Lee},
  journal={ArXiv},
  year={2018},
  volume={abs/1807.09413}
}

Zi Jian Yew, Gim Hee Lee

Published 2018

Computer Science

ArXiv

In this paper, we propose the 3DFeat-Net which learns both 3D feature detector and descriptor for point cloud matching using weak supervision. [...] Key Result We create training and benchmark outdoor Lidar datasets, and experiments show that 3DFeat-Net obtains state-of-the-art performance on these gravity-aligned datasets.Expand Abstract

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