GRSNet: An Ultra-Lightweight Neural Network for 3D Point Cloud Classification and Segmentation

The processing of point cloud data has become a significant area of research in the modern field of perception. Classification and segmentation are critical tasks in autonomous driving, environmental perception, and digital twins. Algorithms that directly extract features from raw point cloud data have simple architectures, but they are constrained by computational demands and limited efficiency. This makes effective deployment on resource-limited devices challenging. This article introduces GRSNet, an ultra-lightweight algorithm. The principal innovation is a new sampling method named golden ratio sampling (GRS), which generates sampling point indices directly using the golden ratio to subsequently locate the corresponding sampling points. This method efficiently extracts representative points from point cloud data and integrates them into deep networks. Leveraging GRS, this study combines the concepts from GhostNet and self-attention mechanisms to develop a feature extraction module dubbed the SA_Ghost Block, forming the core of GRSNet. Comparative experiments with leading algorithms on established point cloud open-source datasets demonstrate that GRSNet achieves superior performance, maintaining only 0.7 M parameters.