Structure-driven facade parsing with irregular patterns

Abstract

We propose a novel method for recognizing irregular patterns in facades. An irregular pattern is an incomplete 2D grid, representing the placements of repetitive structural architectural objects (e.g., windows), which is capable of being generalized to a variety of facade structures. To effectively recognize such a pattern, we jointly model objects and object structures in a unified Marked Point Process framework, where the architectural objects are abstracted as sparsely populated geometric entities and the pairwise spatially interactions are modeled as elliptical repulsion fields. To optimize the proposed model, we introduce a structure-driven Monte Carlo Markov Chain (MCMC) sampler, by which the irregular pattern hypotheses are iteratively constructed in a bottom-up manner and verified in a top-down manner. The solution space is explored more efficiently for fast convergence. Extensive experiments have shown the efficiency and accuracy of our method of parsing a large category of facades.