Roof Reconstruction of Aerial Point Cloud Based on BPPM Plane Segmentation and Energy Optimization

In this article, a novel reconstruction method for aerial LiDAR point cloud building models is proposed to obtain valid roof building models. There are two problems in the reconstruction. First, in the process of primitive segmentation, due to the uneven density of the point cloud, there is the problem of over- or undersegmentation of the plane, resulting in the inability to extract concise and suitable building planes, which, in turn, affects the reconstruction topology. Second, in the model construction process, due to the variety and complexity of building structures, obtaining regular, compact, and topologically correct surface models from sparse and noisy point clouds is still a challenge. To address the first problem, first, the initial primitives are obtained using an improved multiresolution supervoxel-based region growing segmentation algorithm. Then, a new progressive primitive fusion algorithm Belief Propagation Primitive Merge is proposed to optimize the fragmented primitives. For the second problem, first, the Corner KLine regularization algorithm is proposed to obtain the building footprints. Then, the height map is constructed from the point cloud to extract the polyline of the building boundaries and deduce the vertical planes. Finally, a new energy function is proposed to encourage the selection of the recommended combination of model planes to obtain a compact and valid roof reconstruction model. Experiments are performed on different roof point clouds to quantitatively evaluate the proposed method, and qualitative experiments are conducted with comparative experiments to confirm the effectiveness of the algorithm.