RandLA-Net: Efficient Semantic Segmentation of Large-Scale Point Clouds

Understand

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.