3D Point Cloud Classification Using Graph Convolutional Network with Multi-Scale Pooling

3D point clouds (LiDAR) are an integral application in robotics, creating 2D or 3D maps that help autonomous vehicles navigate or avoid and recognize obstacles as they drive. In this work, we propose Graph Convolutional Network with Multi-Scale Pooling to classify three-dimensional objects. Previous research very rarely undertakes the transformation of point clouds into graphs and the use of increasingly popular graph networks. Researchers focus on transforming 3D points into voxels and using classical CNN or 3D CNN convolutional networks or methods that directly use the entire cloud, as is evident in the PointNet model. Therefore, we focused on transforming our data into irregular structures (graphs) and classifying them. Our proposed method increases the possibilities for point cloud interpretation by taking into account the relationships between points in space. In order to achieve even better accuracy results, we use the integration of two pooling techniques in the final stage of the model. Our GCN with Multi-Scale Pooling architecture has been trained and tested on the ModeiNetl0 dataset, achieving high-quality metrics: Accuracy, Precision, Recall, and F1-Score compared to other classical SOTA models in the classification domain. Additionally, we carried out experiments based on ShapeNet Core dataset, for which our network achieved almost 100% accuracy. In addition, we showed that our proposed model is built from a much smaller number of parameters compared to other modern methods.