  We study the problem of efficient semantic segmentation for large-scale 3D
point clouds. By relying on expensive sampling techniques or computationally
heavy pre/post-processing steps, most existing approaches are only able to be
trained and operate over small-scale point clouds. In this paper, we introduce
RandLA-Net, an efficient and lightweight neural architecture to directly infer
per-point semantics for large-scale point clouds. The key to our approach is to
use random point sampling instead of more complex point selection approaches.
Although remarkably computation and memory efficient, random sampling can
discard key features by chance. To overcome this, we introduce a novel local
feature aggregation module to progressively increase the receptive field for
each 3D point, thereby effectively preserving geometric details. Extensive
experiments show that our RandLA-Net can process 1 million points in a single
pass with up to 200X faster than existing approaches. Moreover, our RandLA-Net
clearly surpasses state-of-the-art approaches for semantic segmentation on two
large-scale benchmarks Semantic3D and SemanticKITTI.
