Generative approaches enable creating numerous scenarios coherent with the reality, as long as the representations are good approximations of the real world. In this talk, I will discuss extracting such generative representations from 2D and 3D data for mapping, modeling, and reconstruction of spatial data and urban models; combining computer vision, machine learning, and computational geometry for shape understanding. I will introduce geometry processing algorithms to exploit similarities, grammar discovery approaches to extract procedural rules, and machine learning methods to understand geospatial information. The talk will conclude by proposed applications, new directions in 3D deep learning for generative models, and experimental results on various types of geometric data.
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