Plane segmentation is a basic task in the automatic reconstruction of indoor and urban environments from unorganized point clouds acquired by laser scanners. As one of the most common plane-segmentation methods, standard Random Sample Consensus (RANSAC) is often used to continually detect planes one after another. However, it suffers from the spurious-plane problem when noise and outliers exist due to the uncertainty of randomly sampling the minimum subset with 3 points. An improved RANSAC method based on Normal Distribution Transformation (NDT) cells is proposed in this study to avoid spurious planes for 3D point-cloud plane segmentation. A planar NDT cell is selected as a minimal sample in each iteration to ensure the correctness of sampling on the same plane surface. The 3D NDT represents the point cloud with a set of NDT cells and models the observed points with a normal distribution within each cell. The geometric appearances of NDT cells are used to classify the NDT cells into planar and non-planar cells. The proposed method is verified on three indoor scenes. The experimental results show that the correctness exceeds 88.5% and the completeness exceeds 85.0%, which indicates that the proposed method identifies more reliable and accurate planes than standard RANSAC. It also executes faster. These results validate the suitability of the method.

The level of detail (LoD) concept in CityGML (City Geography Markup Language), which indicates how closely the model mirrors its real-world counterpart, has been accepted and applied widely in various applications, including the 3D modeling of buildings. However, with an increasing number of human activities occurring in the indoor environment, the standardized LoD definition appears to be insufficient because of its narrow classifications for interior features, which can be presented only in LoD4. In view of this drawback, an extended indoor LoD (ILoD) specification is proposed, particularly for indoor spaces, allowing the existing LoD to become a more precise outdoor LoD (OLoD) by exploiting the advantages of two other international standards: Industry foundation classes (IFC) and IndoorGML. In this paper, the interior space is divided into distinct systems of three semantic aspects (structure, connectivity, and volume); the approach can be considered the guiding ideology to define the detailed indoor levels following a concrete theoretical realization based on extending the UML diagram of CityGML’s building model. Moreover, a continuous and seamless full LoD (FLoD) set obtained by combining various OLoDs and ILoDs is subsequently listed to realize the full specification for 3D building models. Furthermore, to demonstrate the proposed specification and prove the applicability of the building model at different LoDs, a practical experiment is conducted.

Recent developments in laser scanning systems have inspired substantial interest in indoor modeling. Semantically rich indoor models are required in many fields. Despite the rapid development of 3D indoor reconstruction methods for building interiors from point clouds, the indoor reconstruction of multi-room environments with curved walls is still not resolved. This study proposed a novel straight and curved line tracking method followed by a straight line test. Robust parameters are used, and a novel straight line regularization method is achieved using constrained least squares. The method constructs a cell complex with both straight lines and curved lines, and the indoor reconstruction is transformed into a labeling problem that is solved based on a novel Markov Random Field formulation. The optimal labeling is found by minimizing an energy function by applying a minimum graph cut approach. Detailed experiments were conducted, and the results indicate that the proposed method is well suited for 3D indoor modeling in multi-room indoor environments with curved walls.

Ancient Chinese-style architecture has received increased attention during the last century as a segment of cultural heritage and is of great significance, specifically in regard to the process of digitizing and modeling these buildings to preserve and protect this heritage. Because the roof form reflects the age of the structure, the structural character and the historical culture of the ancient building, constructing a refined model for the roof is a primary aspect of the 3D modeling procedure. To avoid cumbersome traditional modeling approaches that use geometry units, such as points, lines and triangles, a flexible semantic method is proposed in this study to improve modeling efficiency and reduce the professional requirements. In this method, a two-level semantic decomposition of the roof is presented according to the characteristics of ancient Chinese-style architecture. The structural level reveals the basic components that determine its structural shape, and the decorative level refers to the attached components that influence the exterior appearance. The assembly validity of the decomposed elements and the combined diversity of the integrated entities are ensured by topological constraints and derived transformations of the semantic components. This proposed method was implemented by utilizing CityGML (City Geography Markup Language) via the ADE (Application Domain Extension) mechanism and was tested by modeling the principal buildings included in the Palace Museum.

Zhang, H.; Tang. L., 2019. A Nash equilibrium game model for seafood safety regulation. In: Gong, D.; Zhu, H., and Liu, R. (eds.), Selected Topics in Coastal Research: Engineering, Industry, Economy, and Sustainable Development. Journal of Coastal Research, Special Issue No. 94, pp. 818–822. Coconut Creek (Florida), ISSN 0749-0208.To prevent the safety incidents from becoming serious, it is vital to find the root causes of the accidents. This paper expatiates the trading patterns of online-sold seafood and the resulting seafood safety situations. Then a brief overview of the existing related regulatory measures and current problems in online-sold food safety supervision is presented. On this basis, three subjects, including seafood producers, third-party platforms and consumers, are used to set the Nash equilibrium, analyzing the factors that can influence each related interest subject's strategy choice. The results show that the third-party platform is the most essential part in the safety supervision chain, and too much emphasis on regulating producers will only curb food safety incidents in the short term. Some suggestions are given to improve the safety supervision of online-sold seafood.