Unsupervised object-based clustering in support of supervised point-based 3D point cloud classification

The number of approaches available for semantic segmentation of point clouds has grown exponentially in recent years. The availability of numerous annotated datasets has resulted in the emergence of deep learning approaches with increasingly promising outcomes. Even if successful, the implementation of such algorithms requires operators with a high level of expertise, large quantities of annotated data and high-performance computers. On the contrary, the purpose of this study is to develop a fast, light and user-friendly classification approach valid from urban to indoor or heritage scenarios. To this aim, an unsupervised object-based clustering approach is used to assist and improve a feature-based classification approach based on a standard machine learning predictive model. Results achieved over four different large scenarios demonstrate the possibility to develop a reliable, accurate and flexible approach based on a limited number of features and very few annotated data.