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.

本研究旨在提出一种减少计算量、以大规模点云直接推断每个点的语意的神经架构，该方法采用随机采样而非较复杂的点选择方法，然后配备一种新的局部特征汇聚模块以生成具有准确语意的结果，演示数据表明我们的方法具有比现有方法高 200 倍的速度和最先进的分类精度。

使用随机采样学习大规模点云的语义分割

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

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.

介绍了一种名为 RandLA-Net 的高效轻量级神经架构，在大规模点云中通过使用随机采样和引入新的局部特征聚合模块来实现高效的语义分割。经过实验表明，RandLA-Net 在处理 100 万点的规模时比现有方法快 200 倍以上，并在两个大规模基准数据集 Semantic3D 和 SemanticKITTI 上表现优于现有方法。