The contours, one of the most significant human perceptual features, have a significant impact on point cloud processing. In urban scenes, contour extraction is quite challenging due to the enormous number of unstructured and irregular points (typically greater than 107points). In this paper, we propose a Large-scale 3D point cloud Contour Extraction Network (LCE-NET) to generate contours consistent with human perception of outdoor scenes. To our knowledge, it is the first time that an end-to-end learning-based framework has been proposed for contour extraction on point cloud at this scale. The proposed LCE-Net is essentially a two-phase system. In the first phase, potential vertexes are detected from the input point cloud by the vertex detection module, then in the second phase, a designed overcomplete line proposal set is generated, and invalid line segments are further suppressed by the line proposal discrimination module. The two phases are jointly trained by a uniform loss function to promote the information interchange, thus leading to extraction results with satisfied accurate and false alarm ratings. Since there is hardly any available dataset with labeled contours for the large-scale outdoor scene, we open sourced SemanticLine, the first dataset for large-scale point clouds with labeled contour information, based on re-annotation of previous mapping level point cloud dataset semantic3D. Experimental results demonstrate that LCE-NET can effectively extract parametric contour lines from large-scale point clouds of urban scenes. Additionally, it outperforms the state-of-the-art approaches. The code will be open source on GitHub soon.

Point cloud segmentation plays an important role in AI applications such as autonomous driving, AR, and VR. However, previous point cloud segmentation neural networks rarely pay attention to the topological correctness of the segmentation results. In this paper, focusing on the perspective of topology awareness. First, to optimize the distribution of segmented predictions from the perspective of topology, we introduce the persistent homology theory in topology into a 3D point cloud deep learning framework. Second, we propose a topology-aware 3D point cloud segmentation module, TopoSeg. Specifically, we design a topological loss function embedded in TopoSeg module, which imposes topological constraints on the segmentation of 3D point clouds. Experiments show that our proposed TopoSeg module can be easily embedded into the point cloud segmentation network and improve the segmentation performance. In addition, based on the constructed topology loss function, we propose a topology-aware point cloud edge extraction algorithm, which is demonstrated that has strong robustness.