The representation quantifies the geometric shape and topology of a building is a necessary procedure for many urban planning applications. A sharp line framework is a high-level structural cue providing a compact building representation. However, accurate and efficient structural line extraction remains a challenging task given the variety and complexity of buildings. This study proposes a general 3-D structural line extraction method from point clouds. The building points are extracted and further divided into various single-building units. In the proposed 3-D structural line extraction method, individual building point cloud is the input. First, the corners are detected by an associative learning module. Next, the curve connection is implemented by a link prediction block based on the graph neural network (GNN) embedded with corner information. After that, the obtained curves are subsequently converted into a topological graph. Finally, the corner points are optimized to achieve precise fitting of the structural lines. The experiments and comparisons on two airborne laser scanning (ALS) point cloud datasets demonstrate the effectiveness of the proposed method and the ability to retrieve ideal structural line results for building point clouds. Furthermore, without reprocessing, the proposed method yielded better results for various dataset types (outdoor building, indoor scene, and furniture point clouds) than the prevalent published methods (i.e., EC-Net, PIE-Net, and PC2WF), verifying its strength and efficacy. To further verify the accuracy of the obtained structural lines, we also introduce a line-based model reconstruction method that employ these lines for building reconstruction.

Incomplete or outdated inventories of railway infrastructures may disrupt the railway sector’s administration and maintenance of transportation infrastructure, thus posing potential threats to the safety of traffic networks. Previous studies have adopted point clouds to accelerate inventory and inspection automation procedures. However, owing to the complexity of the railway scenes, previous studies reveal an imbalance between semantic richness, segmentation accuracy, and processing efficiency. This study aims to advance our understanding by providing a deep-learning framework for railway point cloud semantic segmentation. The proposed framework, named RailSeg, encompasses point cloud downsampling, integrated local-global feature extraction, spatial context aggregation, and semantic regularization. The proposed method, validated using point clouds collected in suburban and rural scenes, generates a point-level railway furniture inventory of 11 categories and achieves competitive performance in overall accuracy and mean intersection over union. In addition, RailSeg achieves better results than the baseline for additional types of point clouds (i.e., plateau railway mobile laser scanning (MLS) point clouds, street MLS point clouds, and urban-scale photogrammetric point clouds), demonstrating the superior generalization capabilities of RailSeg. This study may contribute to the development of 3D semantic segmentation, digital railway, and intelligent transportation.

Interpretation of airborne laser scanning (ALS) point clouds plays a notable role in geoinformation production. As a critical step for interpretation, accurate semantic segmentation can considerably broaden various applications of ALS data. However, most existing methods cannot provide precise annotations and high robustness due to occlusions, varied point densities, and complex and incomplete object structures. Therefore, we developed a semantic segmentation framework focusing on ALS point clouds. The framework comprises contextual feature extraction from a local neighborhood, scene-aware global information representation, and a ground-aware attention module. To verify its effectiveness, comprehensive experiments were conducted on three airborne light detection and ranging (LiDAR) datasets: DublinCity, Dayton Annotated LiDAR Earth Scan (DALES), and DFC2019 datasets. The experimental results demonstrate that the proposed method achieves better segmentation performance than that some advanced methods. For the DublinCity dataset, our model’s overall accuracy (OA) can be improved to 67.5% with an average F1(Avg$F_{1}$) of 37.6%. For the DALES dataset, our method achieved an OA of 96.5% and a mean intersection over union (mIoU) of 77.6%. Our method also achieves a more accurate result on the DFC2019 dataset than that obtained using other models with an OA of 94.8% and an AvgF1of 81.4%.

This paper presents a neural network for robust normal estimation on point clouds, named AdaFit, that can deal with point clouds with noise and density variations. Existing works use a network to learn point-wise weights for weighted least squares surface fitting to estimate the normals, which has difficulty in finding accurate normals in complex regions or containing noisy points. By analyzing the step of weighted least squares surface fitting, we find that it is hard to determine the polynomial order of the fitting surface and the fitting surface is sensitive to outliers. To address these problems, we propose a simple yet effective solution that adds an additional offset prediction to improve the quality of normal estimation. Furthermore, in order to take advantage of points from different neighborhood sizes, a novel Cascaded Scale Aggregation layer is proposed to help the network predict more accurate point-wise offsets and weights. Extensive experiments demonstrate that AdaFit achieves state-of-the-art performance on both the synthetic PCPNet dataset and the real-word SceneNN dataset. The code is publicly available at https://github.com/Runsong123/AdaFit.