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.

翻译：点云的高效分析在现实三维应用中具有至关重要的意义。当前主流基于点的模型遵循PointNet++范式，即在空间重叠的局部点集序列中进行点特征嵌入与抽象，导致显著的计算冗余。受卷积神经网络中像素嵌入后接区域池化这一简洁范式的启发，我们提出一种新颖、简洁且高效的架构——PointGL，专为高效点云分析而设计。PointGL通过两个递归步骤实现层次化特征获取：首先，全局点嵌入利用简单的残差多层感知器对各点进行特征嵌入；其次，新型局部图池化技术通过简洁的局部图刻画点间关系并抽象区域表征。一次性点嵌入与无参数图池化的和谐融合，使PointGL具备模型复杂度最低和效率最高的核心特性。在ScanObjectNN数据集上，PointGL在实现最先进精度的同时，运行速度相比近期模型PointMLP提升5倍以上，计算量仅为其约4%，参数量仅为其约30%。PointGL代码已开源至https://github.com/Roywangj/PointGL。