This article presents an efficient approach for accurate registration of a building facade model “dressed” with dense semantic information. Localization sensors such as the GPS as well as vision-based methods are able to provide a camera pose in an efficient and stable way, but at the expense of low accuracy. We propose here to rely on semantic maps to improve the accuracy of a rough camera pose. Simultaneously we aim to iteratively improve the quality of the semantic map through the registration. Registration and semantic segmentation are jointly refined in an Expectation–Maximization framework. We especially introduce a Bayesian model that uses prior semantic segmentation as well as geometric structure of the facade reference modeled by Generalized Gaussian Mixtures. We show the advantages of our method in terms of robustness to clutter and change of illumination on urban images from various databases.

本文提出了一种有效的方法，用于精确注册带有密集语义信息的“装饰”的建筑立面模型。诸如GPS之类的定位传感器以及基于视觉的方法能够以高效，稳定的方式提供相机姿态，但要以降低精度为代价。我们在这里建议依靠语义图来提高相机粗略姿势的准确性。同时，我们旨在通过注册来迭代地提高语义图的质量。在期望最大化框架中，联合完善了注册和语义分割。我们特别介绍了一种贝叶斯模型，该模型使用先验语义分割以及由广义高斯混合模型建模的立面参考的几何结构。