  Deep convolutional neural networks (CNNs) have shown outstanding performance
in the task of semantically segmenting images. 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 raw point clouds as input. 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 multiple datasets where our method
achieves state-of-the-art performance. We also extend and evaluate our network
for instance and dynamic object segmentation.
