Papers › LatticeNet: Fast Point Cloud Segmentation Using Permutohedral Lattices

LatticeNet: Fast Point Cloud Segmentation Using Permutohedral Lattices

12 Dec 2019arXiv:1912.05905archive 2025-07-28

Radu Alexandru Rosu, Peer Schütt, Jan Quenzel, Sven Behnke

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 convolutions while keeping a low memory footprint. Further, we introduce DeformSlice, a novel learned data-dependent interpolation for projecting lattice features back onto the point cloud. We present results of 3D segmentation on various datasets where our method achieves state-of-the-art performance.

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