High-quality urban reconstruction requires more than multi-view reconstruction and local optimization. The structure of facades depends on the general layout, which has to be optimized globally. Shape grammars are an established method to express hierarchical spatial relationships, and are therefore suited as representing constraints for semantic facade interpretation. Usually inference uses numerical approximations, or hard-coded grammar schemes. Existing methods inspired by classical grammar parsing are not applicable on real-world images due to their prohibitively high complexity. This work provides feasible generic facade reconstruction by combining low-level classifiers with mid-level object detectors to infer an irregular lattice. The irregular lattice preserves the logical structure of the facade while reducing the search space to a manageable size. We introduce a novel method for handling symmetry and repetition within the generic grammar. We show competitive results on two datasets, namely the Paris2010 and the Graz50. The former includes only Hausmannian, while the latter includes Classicism, Biedermeier, Historicism, Art Nouveau and post-modern architectural styles

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With the current state of video games growing in scale, manual content creation may no longer be feasible in the future. Split grammars are a promising technology for large-scale procedural generation of urban structures, which are very common in video games. Buildings with curved parts, however, can currently only be approximated by static pre-modelled assets, and rules apply only to planar surface parts. We present an extension to split grammar systems that allow the creation of curved architecture through integration of free-form deformations at any level in a grammar. Further split rules can then proceed in two different ways. They can either adapt to these deformations so that repetitions can adjust to more or less space, while maintaining length constraints, or they can split the deformed geometry with straight planes to introduce straight structures on deformed geometry

ABSTRACT:The generative surface reconstruction problem can be stated like this: Given a finite collection of 3D shapes, create a small set of functions that can be combined to generate the given shapes procedurally. We propose generative fact labeling (GFL) as an attempt to organize the iterative process of shape analysis and shape synthesis in a systematic way. We present our results for the reconstruction of complex windows of neo-classical buildings in Graz, followed by a critical discussion of the limitations of the approach.