Artificial neural networks exploiting point cloud data for fragmented solid objects classification

This paper presents a novel approach for fragmented solid object classification exploiting neural networks based on point clouds. This work is the initial step of a project in collaboration with the Institution of 'Ente Parco Archeologico del Colosseo' in Rome, which aims to reconstruct ancient artifacts from their fragments. We built from scratch a synthetic dataset (DS) of fragments of different 3D objects including aging effects. We used this DS to train deep learning models for the task of classifying internal and external fragments. As model architectures, we adopted PointNet and dynamical graph convolutional neural network, which take as input a point cloud representing the spatial geometry of a fragment, and we optimized model performance by adding additional features sensitive to local geometry characteristics. We tested the approach by performing several experiments to check the robustness and generalization capabilities of the models. Finally, we test the models on a real case using a 3D scan of artifacts preserved in different museums, artificially fragmented, obtaining good performance.