Grid-GCN for Fast and Scalable Point Cloud Learning

Understand

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.