Plane segmentation for a building roof combining deep learning and the RANSAC method from a 3D point cloud

Abstract

The segmentation of a point cloud on the roof plane is of great significance to the reconstruction of building models. However, the traditional segmentation methods segment the aerial point cloud of the roof, which cannot fully express the geometric structure of the roof, whereas the deep learning-based methods have problems such as too much manual annotation and training time. In this work, a plane segmentation method for a building roof based on the PointNet network combined with the random sample consensus (RANSAC) algorithm is proposed to directly segment the whole point cloud of the building, but it is not limited to the point cloud of the roof. With the proposed framework, the roof part is extracted from the building by an improved PointNet network, and then the roof semantic point cloud is segmented by the RANSAC algorithm to complete the roof extraction. Based on the experimental results gained from multiple building point clouds, it is shown that the proposed method achieves the segmentation of a roof on most multi-plane roof building point clouds and that it has strong practical value.