Classification of 3D data

The use of 3D data (point clouds, polygonal models, etc.) is nowadays widely diffused for various applications and in different fields, from the documentation of cultural heritage to autonomous driving, from urban planning to semantic 3D modeling. Nevertheless, to provide useful 3D data, it is necessary to associate some semantic information that can help operators to better understand and interpret the data. Our research activity aims at carrying out an optimal, repeatable and reliable segmentation and classification procedure to manage various types of 3D survey data and associate them with heterogeneous information and attributes to characterize and describe the surveyed scene. The developed methods are based on supervised / data-driven machine learning methods as well as unsupervised deep learning methods and are tested on various scenarios, from heritage objects to architectures or urban environments, providing for 3D classified data.

Cultural Heritage - Related publications:

Urban environments - Related publications:

Funding: internal, AI4CH