Bayesian Grammar Learning for Inverse Procedural Modeling

Within the fields of urban reconstruction and city modeling, shape grammars have emerged as a powerful tool for both synthesizing novel designs and reconstructing buildings. Traditionally, a human expert was required to write grammars for specific building styles, which limited the scope of method applicability. We present an approach to automatically learn two-dimensional attributed stochastic context-free grammars (2D-ASCFGs) from a set of labeled building facades. To this end, we use Bayesian Model Merging, a technique originally developed in the field of natural language processing, which we extend to the domain of two-dimensional languages. Given a set of labeled positive examples, we induce a grammar which can be sampled to create novel instances of the same building style. In addition, we demonstrate that our learned grammar can be used for parsing existing facade imagery. Experiments conducted on the dataset of Haussmannian buildings in Paris show that our parsing with learned grammars not only outperforms bottom-up classifiers but is also on par with approaches that use a manually designed style grammar.