• DOI:10.15607/rss.2020.xvi.006
Corpus ID: 209324054

LatticeNet: Fast Point Cloud Segmentation Using Permutohedral Lattices

@article{Rosu2019LatticeNetFP,
  title={LatticeNet: Fast Point Cloud Segmentation Using Permutohedral Lattices},
  author={Radu Alexandru Rosu and Peer Schutt and Jan Quenzel and Sven Behnke},
  journal={ArXiv},
  year={2019},
  volume={abs/1912.05905}
}

Radu Alexandru Rosu, Peer Schutt, +1 author Sven Behnke

Published 2019

Computer Science, Engineering, Mathematics

ArXiv

Deep convolutional neural networks (CNNs) have shown outstanding performance in the task of semantically segmenting images. However, applying the same methods on 3D data still poses challenges due to the heavy memory requirements and the lack of structured data. Here, we propose LatticeNet, a novel approach for 3D semantic segmentation, which takes as input raw point clouds. A PointNet describes the local geometry which we embed into a sparse permutohedral lattice. The lattice allows for fast… CONTINUE READING

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