No abstract

Urban facades regularly contain interesting variations due to allowed deformations of repeated elements (e.g., windows in different open or close positions) posing challenges to state-of-the-art facade analysis algorithms. We propose a semi-automatic framework to recover both repetition patterns of the elements and their individual deformation parameters to produce a factored facade representation. Such a representation enables a range of applications including interactive facade images, improved multi-view stereo reconstruction, facade-level change detection, and novel image editing possibilities.

Urban data ranging from images and laser scans to traffic flows are regularly analyzed and modeled leading to better scene understanding. Commonly used computational approaches focus on geometric descriptors, both for images and for laser scans. In contrast, in urban planning, a large body of work has qualitatively evaluated street networks to understand their effects on the functionality of cities, both for pedestrians and for cars. In this work, we analyze street networks, both their topology (i.e., connectivity) and their geometry (i.e., layout), in an attempt to understand which factors play dominant roles in determining the characteristic of cities. We propose a set of street network descriptors to capture the essence of city layouts and use them, in a supervised setting, to classify and categorize various cities across the world. We evaluate our method on a range of cities, of various styles, and demonstrate that while standard image-level descriptors perform poorly, the proposed network-level descriptors can distinguish between different cities reliably and with high accuracy.