As a basic and important research, point cloud segmentation plays an important role in many fields. However, the traditional point cloud segmentation algorithm still suffers from two problems. The first problem is that voxel-based segmentation algorithms cannot guarantee accuracy in regions of inconsistent density. The other problem is the inefficiency of point-based clustering algorithms. Hence, in order to solve the first two problems, a new supervoxel-based segmentation algorithm is proposed. To address the first problem, a multi-resolution supervoxel algorithm is proposed to obtain the basic unit for clustering, which includes a new low-density region detection algorithm and a resegmentation process. However, over-detection during the construction of supervoxels leads to the presence of small fragments around large supervoxels. Therefore, for the second problem, a novel BPSO (belief propagation supervoxel optimization) algorithm is proposed to optimize the supervoxel. Moreover, an improved multi-resolution supervoxel- and graph-based segmentation (MGS) algorithm is presented for supervoxel clustering and a segmentation optimization algorithm is adopted to allocate the unallocated points. Experiments are conducted on different datasets, and segmentation results are evaluated quantitatively. Compared with traditional methods and advanced methods, the results show that this method can segment urban point clouds accurately and effectively.

Automatic large-scale building extraction from the LiDAR point clouds and remote sensing images is a growing focus in the fields of the sensor applications and remote sensing. However, this building extraction task remains highly challenging due to the complexity of building sizes, shapes, and surrounding environments. In addition, the discreteness, sparsity, and irregular distribution of point clouds, lighting, and shadows, as well as occlusions of the images, also seriously affect the accuracy of building extraction. To address the above issues, we propose a new unsupervised building extraction algorithm PBEA (Point and Pixel Building Extraction Algorithm) based on a new dual P-snake model (Dual Point and Pixel Snake Model). The proposed dual P-snake model is an enhanced active boundary model, which uses both point clouds and images simultaneously to obtain the inner and outer boundaries. The proposed dual P-snake model enables interaction and convergence between the inner and outer boundaries to improve the performance of building boundary detection, especially in complex scenes. Using the dual P-snake model and polygonization, this proposed PBEA can accurately extract large-scale buildings. We evaluated our PBEA and dual P-snake model on the ISPRS Vaihingen dataset and the Toronto dataset. The experimental results show that our PBEA achieves an area-based quality evaluation metric of 90.0% on the Vaihingen dataset and achieves the area-based quality evaluation metric of 92.4% on the Toronto dataset. Compared with other methods, our method demonstrates satisfactory performance.

A novel reconstruction method for aerial LiDAR point clouds building models is proposed in this paper to obtain valid roof building models. There are two problems in the reconstruction. Firstly, in the process of primitive segmentation, due to the uneven density of the point cloud, there is the problem of over-segmentation or under-segmentation of the plane, resulting in the inability to extract concise and suitable building planes, which in turn affects the reconstruction topology. Secondly, 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, the initial primitives are first obtained using an improved multi-resolution supervoxel based region growing segmentation algorithm. Then, a new progressive primitive fusion algorithm BPPM (belief propagation primitive merge) is proposed to optimize the fragmented primitives. For the second problem, the CKL (Corner KLine) regularization algorithm is first proposed to obtain the building footprints, and 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.