We propose a novel three-layered approach for semantic segmentation of building facades. In the rst layer, starting from an oversegmentation of a facade, we employ the recently introduced machine learning technique Recursive Neural Networks (RNN) to obtain a probabilistic interpretation of each segment. In the middle layer, initial labeling is augmented with the information coming from specialized facade component detectors. The information is merged using a Markov Random Field dened over the image. In the highest layer, we introduce weak architectural knowledge, which enforces the nal reconstruction to be architecturally plausible and consistent. Rigorous tests performed on two existing datasets of building facades demonstrate that we signicantly outperform the current-state of the art, even when using outputs from lower layers of the pipeline. In the end, we show how the output of the highest layer can be used to create a procedural reconstruction.

There is an increasing interest in semantically annotated 3D models, e.g. of cities. The typical approaches start with the semantic labelling of all the images used for the 3D model. Such labelling tends to be very time consuming though. The inherent redundancy among the overlapping images calls for more efficient solutions. This paper proposes an alternative approach that exploits the geometry of a 3D mesh model obtained from multi-view reconstruction. Instead of clustering similar views, we predict the best view before the actual labelling. For this we find the single image part that bests supports the correct semantic labelling of each face of the underlying 3D mesh. Moreover, our singleimage approach may surprise because it tends to increase the accuracy of the model labelling when compared to approaches that fuse the labels from multiple images. As a matter of fact, we even go a step further, and only explicitly label a subset of faces (e.g. 10%), to subsequently fill in the labels of the remaining faces. This leads to a further reduction of computation time, again combined with a gain in accuracy. Compared to a process that starts from the semantic labelling of the images, our method to semantically label 3D models yields accelerations of about 2 orders of magnitude. We tested our multi-view semantic labelling on a variety of street scenes.

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ABSTRACT:Procedural modeling has proven to be a very valuable tool in the field of architecture. In the last few years, research has soared to automatically create procedural models from images. However, current algorithms for this process of inverse procedural modeling rely on the assumption that the building style is known. So far, the determination of the building style has remained a manual task. In this paper, we propose an algorithm which automates this process through classification of architectural styles from facade images. Our classifier first identifies the images containing buildings, then separates individual facades within an image and determines the building style. This information could then be used to initialize the building reconstruction process. We have trained our classifier to distinguish between several distinct architectural styles, namely Flemish Renaissance, Haussmannian and Neoclassical. Finally, we demonstrate our approach on various street-side images.