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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.

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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.

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.

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Background The pathogenesis and etiology of antrochoanal polyps (ACPs) remains obscure. This study aimed to characterize the inflammatory profiles and investigate the effect of atopy on the pathogenesis of pediatric ACPs. Methods Thirty-three ACP patients and ten control subjects were enrolled from January to December 2017. The severity of individual nasal symptoms was scored on a visual analogue scale (VAS). The serum total immunoglobulin E (IgE) and cytokines level was measured by multiplexed luminex assay. Results There was no significant difference in VAS scores and counts of inflammatory cells between atopic and nonatopic ACP. No difference in IFNγ, IL-4, IL-5, IL-13, IL-17A and IL-25 was found between control and whole ACP, nonatopic and atopic ACP. Significantly increased levels of IL-6 and IL-10 were found in ACP compared with control. For neutrophil chemotactic factor, significant increases of IL-8 and GRO were observed in ACP, but for eosinophil chemotactic factor, no difference was found in RANTES and GM-CSF. IL-6 level was positively correlated with IL-8, MCP1, and GRO level, and IL-10 level was positively correlated with IL-4 and IL-13 in ACP subjects. Conclusion Nasal obstruction was the most common symptom in ACPs in children. Allergic condition may have a poor role in the pathogenesis of ACPs. IL-6 plays a crucial role in the pathogenesis of neutrophilic inflammation in patients with ACPs and may provide a new treatment strategy for ACPs in children. Treg cell associated cytokine IL-10 was involved in the inflammatory pathophysiological process of ACPs and played a certain regulatory role.