Learning from synthetic models for roof style classification in point clouds

Automatic roof style classification using point clouds is useful and can be used as a prior knowledge in various applications, such as the construction of 3D models of real-world buildings. Previous classification approaches usually employ heuristic rules to recognize roof style and are limited to a few roof styles. In this paper, the recognition of roof style is done by a roof style classifier which is trained based on bag of words features extracted from a point cloud. In the computation of bag of words features, a key challenge is the generation of the codebook. Unsupervised learning is often misguided easily by the data and detects uninteresting patterns within the data. In contrast, we propose to integrate existing knowledge of roof structure and cluster the points of target roof styles into several semantic classes which can then be used as code words in the bag of words model. We use synthetic variants of these code words to train a semantics point classifier. We evaluate our approach on two datasets with different levels of degradations. We compare the results of our approach with two unsupervised learning algorithms: K-Means and Gaussian Mixture Model. We show that our approach achieve higher accuracy in classification of the roof styles and maintains consistent performance among different datasets.