CVA-GNN: Convolutional Vicinity Aggregation Graph Neural Network for Point Cloud Classification

Point cloud classification is highly dependent on how points' features are extracted and aggregated. The graph-based feature extraction strategies are currently used. Not only point coordinates are taken into consideration but also neighbourhood pair-wise geometrical relations. A newly proposed Convolutional Vicinity Aggregation (CVA) module extends reference solutions with mutual geometrical point relations. Simultaneous convolution of points geometrical interrelations allows a network to retrieve salient features under the permutation-invariance constraint. The resulting hierarchical CVA-based architecture outperforms the state-of-the-art point cloud classification methods on the well-established ModelNet40 dataset. Additional analysis of the CVA module hyper-parameters was also provided in order to support its effectiveness.