PECo: A Point-Edge Collaborative Framework for Global-Aware Urban Building Contouring From Unstructured Point Clouds

The building contours, as one of the most important features for representing geometry, are widely used in various applications including urban modeling and reconstruction. Automatic extraction of high-fidelity compact contours from unstructured point clouds is rather challenging, and the existing methods are limited in generating global-aware and artifact-free building contours. Here, we approach contour extraction as a Bayesian inference problem. Two ideas are proposed to obtain building contour points and edges directly from unstructured point clouds. First, we construct a point-edge collaborative (PECo) Bayesian framework to couple the information of contour points and contour edges. The developed model fully takes local contour features, global edge structures, and global geometric priors into account. Second, given the Bayesian framework for building contouring, we leverage an expectation maximization (EM) algorithm to iteratively infer the contour edges in a maximum posteriori manner. The alternate EM iterations between point and edge domains progressively refine the local pointwise information to a global representation of contour edges. As a result, a synergistic effect between the point features and edge structures for global-aware building contouring is attained. Our approach outperforms the state-of-the-art methods in terms of geometric accuracy and structural compactness in modeling buildings with various complexities. Furthermore, the compactness of the contouring results can be more flexible and easily controlled.