Learning Semantic Segmentation of Large-Scale Point Clouds with Random Sampling

TL;DR

RandLA-Net is a fast, efficient neural network for large-scale 3D point cloud semantic segmentation that uses random sampling and local feature aggregation to achieve state-of-the-art results with significantly reduced computation.

Contribution

The paper introduces RandLA-Net, a novel lightweight architecture that employs random sampling and a local feature aggregation module to efficiently process large-scale point clouds.

Findings

Processes 1 million points in a single pass up to 200x faster.

Achieves state-of-the-art segmentation accuracy on five large-scale datasets.

Demonstrates effectiveness of random sampling combined with local feature aggregation.

Abstract

We study the problem of efficient semantic segmentation of 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. Comparative experiments show that our RandLA-Net can process 1 million points in a single pass up to 200x faster than existing approaches. Moreover, extensive experiments on five large-scale point cloud datasets, including Semantic3D, SemanticKITTI, Toronto3D, NPM3D and S3DIS, demonstrate the state-of-the-art semantic segmentation performance of our RandLA-Net.