  In this work we introduce Lean Point Networks (LPNs) to train deeper and more
accurate point processing networks by relying on three novel point processing
blocks that improve memory consumption, inference time, and accuracy: a
convolution-type block for point sets that blends neighborhood information in a
memory-efficient manner; a crosslink block that efficiently shares information
across low- and high-resolution processing branches; and a multiresolution
point cloud processing block for faster diffusion of information. By combining
these blocks, we design wider and deeper point-based architectures. We report
systematic accuracy and memory consumption improvements on multiple publicly
available segmentation tasks by using our generic modules as drop-in
replacements for the blocks of multiple architectures (PointNet++, DGCNN,
SpiderNet, PointCNN).
