Analysis of Building Textures for Reconstructing Partially Occluded Facades

As part of an architectural modeling project, this paper investigates the problem of understanding and manipulating images of buildings. Our primary motivation is to automatically detect and seamlessly remove unwanted foreground elements from urban scenes. Without explicit handling, these objects will appear pasted as artifacts on the model. Recovering the building facade in a video sequence is relatively simple because parallax induces foreground/background depth layers, but here we consider static images only. We develop a series of methods that enable foreground removal from images of buildings or brick walls. The key insight is to use a priori knowledge about grid patterns on building facades that can be modeled as Near Regular Textures (NRT). We describe a Markov Random Field (MRF) model for such textures and introduce a Markov Chain Monte Carlo (MCMC) optimization procedure for discovering them. This simple spatial rule is then used as a starting point for inference of missing windows, facade segmentation, outlier identification, and foreground removal.