Ricerc@Sapienza

Analysis of 3D data is a field that is increasing its importance in the computer science community due to the great diffusion and wide availability of devices that enable everyone to acquire and generate 3D data autonomously. In the last decade, the amount of available 3D data grows impressively and nowadays, many tools for the analysis, generation and manipulation of this kind of data come to light. Simultaneously, the computer science community proposed several frameworks and tools based on deep learning and artificial intelligence achieving high performances in challenging tasks. With this proposal, we aim to merge these topics to generate new instruments for targeting applications on 3D data. The kind of applications that we will target in this project goes from the shape correspondences to the generation and the analysis of collections of surfaces. The availability of 3D data is, at the same time, a great motivation to develop new tools and a necessary condition to define our instruments on data-driven approaches. To be more concrete, the principal objective of this project is to study and analyze different ways to learn a latent encoding for 3D data that enable us to solve for independent tasks exploiting a unique representation or to define additional constraints for the learning to address a specific application. The recent advances in the context of latent space definition, disentanglement and 3D shape generation will be the base of our work. We plan to develop new techniques that could give rise to outstanding results in challenging scenarios enabling new applications.