Due to the sparsity and irregularity of the point cloud data, methods that
directly consume points have become popular. Among all point-based models,
graph convolutional networks (GCN) lead to notable performance by fully
preserving the data granularity and exploiting point interrelation. However,
point-based networks spend a significant amount of time on data structuring
(e.g., Farthest Point Sampling (FPS) and neighbor points querying), which limit
the speed and scalability. In this paper, we present a method, named Grid-GCN,
for fast and scalable point cloud learning. Grid-GCN uses a novel data
structuring strategy, Coverage-Aware Grid Query (CAGQ). By leveraging the
efficiency of grid space, CAGQ improves spatial coverage while reducing the
theoretical time complexity. Compared with popular sampling methods such as
Farthest Point Sampling (FPS) and Ball Query, CAGQ achieves up to 50X speed-up.
With a Grid Context Aggregation (GCA) module, Grid-GCN achieves
state-of-the-art performance on major point cloud classification and
segmentation benchmarks with significantly faster runtime than previous
studies. Remarkably, Grid-GCN achieves the inference speed of 50fps on ScanNet
using 81920 points per scene as input.

本文提出了名为 Grid-GCN 的方法，用于快速和可扩展的点云学习。Grid-GCN 使用一种新颖的数据结构策略，Coverage-Aware Grid Query（CAGQ），通过利用网格空间的效率，提高了空间覆盖范围，同时减少了理论时间复杂度，具有很高的速度和可扩展性。Grid-GCN 在各类点云分类和分割基准测试中都表现优异，并且比以前的研究运行速度更快。值得注意的是，在使用 81920 个点作为输入场景进行 ScanNet 推理时，Grid-GCN 实现了 50fps 的推理速度。