Fundamentals to Clustering 3D Point Cloud Data

Automation in point cloud data processing is central for building efficient decision-making systems and to cut labour costs. The identification of objects of interest in these massive datasets constitutes the base of many applications. While new supervised deep learning architectures show promising results, the amount of available labelled 3D data is often insufficient for a good generalization. This is where unsupervised approaches and data clustering shine.