Point cloud completion is able to estimate the complete point cloud starting from the missing point cloud, which obtains higher quality point cloud data for widely used in remote sensing 3D modeling, medical imaging, robot vision, etc. The challenge of point clouds mainly lies in the disordered and unstructured nature, which makes point cloud completion difficult. Point cloud completion research can be broadly categorized into traditional approaches and deep learning based methods. Recently, intensive research has primarily focused on deep learning based methods, given robustness and efficiency in processing the substantial missing data encountered in complex real world scenes. In addition, deep learning based methods have higher generalization performance. To stimulate future research, this survey presents a comprehensive review of existing traditional and deep learning based 3D point cloud completion methods. This review conducts extensive examinations of each stage of the process, providing a compilation of famous datasets, metrics, and their respective characteristics. Additionally, the impacts of subsequent downstream application tasks with or without completion are discussed, followed by some potential future issues in point cloud completion

Accurate extraction of tunnel lining is crucial for deformation monitoring, damage detection, and tunnel modeling. However, the extraction of tunnel lining is still challenging due to the different tunnel shapes and complicated construction scenes. As for tunnel point clouds, inconsistent local density and non-structural features also pose challenges. Previous research has shown that utilizing the centerline of a tunnel is the preferred method for extracting tunnel lining. However, imprecision often arises due to noise points and point cloud hole. To address these challenges, we propose a novel extraction framework for various tunnel shapes that seldom depends on the centerline. First, the structure detection module is designed to detect the lining structure from the raw point cloud. Following that, multi-scale clustering filtering is applied for fine extraction. The global clustering filtering procedure is then implemented to process the projected point cloud. Finally, the local filtering approach is created for confusion clustering. Experimental results show that the highest Kappa coefficient of the proposed method is 95.5%. Compared with the elliptic cylinder method, the angle threshold method, and the template method, the extraction accuracy of our method is improved by 6.2%, 10.9%, and 9.7%, respectively.

The first law of geography is one of the most important concepts in geographical analysis, revealing the significant role of spatial proximity. At present, some current international relation studies or geographic network analysis studies tend to build corresponding network models according to different themes, but the most basic level of geographic neighborhoods is intentionally or unintentionally neglected in those processes. Based on the adjacency relationship between the terrestrial countries in the world, the model of the terrestrial adjacency network (TAN) is constructed. The model includes almost all land-based countries and is divided into three main regions, respectively, Eurasia, Africa, and America. On the mathematical model of these regions, we analyze the geospatial structure and network evolution of the adjacent networks utilizing statistical methods and network analysis methods. This study helps to map and understand the geographical attributes and characteristics of countries from the perspective of holistic structure, aiming to provide a quantitative reference for subsequent research on international relations and geographic computing. Moreover, despite some limitations, TAN represents a new advance in geographical network analysis that can be further applied by overlaying more attribute data.

Multibeam echo sounding sonar, as an active acoustic detection technology, is the mainstream equipment for current seabed topography surveying. The accuracy of the seabed topographic model is significantly influenced by anomalies in survey. Addressing the limitations in stability and comprehensiveness in anomaly processing of existing detection algorithms, this study proposes a novel anomaly detection algorithm based on prior constraints. This algorithm integrates the distribution characteristics of various types of anomalies and classifies them meticulously. A robust estimation of the detection model is achieved through geostatistical methods to enhance the accuracy of seabed topography representation. Experimental results demonstrate that our method excels in error control. Tests on one simulated dataset and two field-collected datasets showed Type Ⅰ errors between 1.6% and 3%, Type Ⅱ errors between 5.3% and 17%, and total error rates between 1.9% and 3.1%. Compared to existing classic algorithms, our method not only preserves the terrain features but also achieves comprehensive detection of different types of anomalies.