Some contributions to geometric modeling of urban environments

This habilitation thesis proposes a series of contributions in the field of geometric modeling from physical measurements. These contributions present concepts and algorithms for object extraction, surface reconstruction, and scene modeling for urban environments. The methodology behind these contributions relies on stochastic geometry and graphical models. These probabilistic models are suited to analyzing the diversity and the complexity of urban objects and to exploring large and highly non-convex solution spaces at city-scales. The physical measurements, data structures, and concepts involved in our work lie between computer vision, geometry processing, and photogrammetry. A general summary also provides a vision of the many remaining challenges in the field.