  Efficient analysis of point clouds holds paramount significance in real-world
3D applications. Currently, prevailing point-based models adhere to the
PointNet++ methodology, which involves embedding and abstracting point features
within a sequence of spatially overlapping local point sets, resulting in
noticeable computational redundancy. Drawing inspiration from the streamlined
paradigm of pixel embedding followed by regional pooling in Convolutional
Neural Networks (CNNs), we introduce a novel, uncomplicated yet potent
architecture known as PointGL, crafted to facilitate efficient point cloud
analysis. PointGL employs a hierarchical process of feature acquisition through
two recursive steps. First, the Global Point Embedding leverages
straightforward residual Multilayer Perceptrons (MLPs) to effectuate feature
embedding for each individual point. Second, the novel Local Graph Pooling
technique characterizes point-to-point relationships and abstracts regional
representations through succinct local graphs. The harmonious fusion of
one-time point embedding and parameter-free graph pooling contributes to
PointGL's defining attributes of minimized model complexity and heightened
efficiency. Our PointGL attains state-of-the-art accuracy on the ScanObjectNN
dataset while exhibiting a runtime that is more than 5 times faster and
utilizing only approximately 4% of the FLOPs and 30% of the parameters compared
to the recent PointMLP model. The code for PointGL is available at
https://github.com/Roywangj/PointGL.
