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Shayan Nikoohemat Abdoulaye A. Diakit Ville Lehtola Sisi Zlatanova George Vosselman 《Transactions in GIS》2021,25(1):189-212
Indoor 3D models are digital representations of building interiors reconstructed from scanned data acquired by laser scanners, digital depth (RGBD) cameras, and CAD drawings. Consequently, there is noise in the source data and a notable variety in the methods used to treat the noise and to process these data into reconstructed models. Alas, the correctness of these reconstructions and thus their suitability for a given application are uncertain. There is a lack of a robust base logic that would allow for controlling the consistency of these (automatically) generated models. Fortunately, correctness criteria are well‐defined through existing international standards. Hence, we propose a conceptual framework based on formal grammars to check the semantic, geometric, and topological consistency of a reconstructed 3D model. The proposed method proceeds in three steps to validate the model: (1) correctness checking of individual components; (2) consistency verification of instances’ interactions; and (3) model consistency check for targeted applications. Our method identifies the components in the model that violate the given rules derived from the current standards and expert knowledge. Ultimately, we propose a quantified formulation of our method that may be straightforwardly integrated into industrial‐level model checkers. The approach is independent of level of details and reconstruction method. 相似文献
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Yixiang Tian Markus Gerke George Vosselman Qing Zhu 《ISPRS Journal of Photogrammetry and Remote Sensing》2010,65(4):395-408
The paper presents an automatic method for the reconstruction of building models from video image sequences. These videos may be recorded using a hand-held camera or a camera mounted on a moving car. Such terrestrial video sequences are economic and flexible. Presenting buildings as geometric models–rather than for instance a representation from a simple meshing of 3D points–enables one to perform a wide range of analyses. However, sparse 3D points and 3D edges do not contain topological relations. Therefore, integrating building structure knowledge into the reconstruction steps plays an important role in our method. First, some rules are applied to reasonably group the extracted features. Then, a suitable outline and normal direction are specified for each surface patch. Based on these surface patches, a hybrid model- and data-driven method is used to recover a building model from both the extracted surface patches and hypothesized parts. Using the building structure knowledge leads to a simple and fast reconstruction method, and also enables one to obtain the main structures of buildings. The results show that this method correctly sets up topological relationships between generated surface patches and also obtains reasonable structure models in occluded areas. Therefore, the reconstructed models satisfy requirements for both visualization and analysis. 相似文献
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Knowledge based reconstruction of building models from terrestrial laser scanning data 总被引：1，自引：0，他引：1
This paper presents an automatic method for reconstruction of building façade models from terrestrial laser scanning data. Important façade elements such as walls and roofs are distinguished as features. Knowledge about the features’ sizes, positions, orientations, and topology is then introduced to recognize these features in a segmented laser point cloud. An outline polygon of each feature is generated by least squares fitting, convex hull fitting or concave polygon fitting, according to the size of the feature. Knowledge is used again to hypothesise the occluded parts from the directly extracted feature polygons. Finally, a polyhedron building model is combined from extracted feature polygons and hypothesised parts. The reconstruction method is tested with two data sets containing various building shapes. 相似文献
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An automated method for 3D modelling of complex highway interchanges is presented. Laser data and 2D topographic map data have been combined in an innovative 3D reconstruction procedure. Complex situations as shown in this paper demand knowledge to guide the automatic reconstruction. This knowledge has been used in the fusion procedure to constrain the topological and geometrical properties of the reconstructed 3D model. These additions are needed to take care of the lack of data in occluded areas. Laser data has been segmented and filtered before it is fused with map data. In the surface-growing algorithm combining map and laser points, the laser data is assigned to the corresponding road element. Although results are shown using two specific data sources, the algorithm is designed to be capable of dealing with any polygon-based topographic map and any aerial laser scanner data-set. 相似文献
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ABSTRACT Remote sensing images have long been recognized as useful for the detection of building damages, mainly due to their wide coverage, revisit capabilities and high spatial resolution. The majority of contributions aimed at identifying debris and rubble piles, as the main focus is to assess collapsed and partially collapsed structures. However, these approaches might not be optimal for the image classification of façade damages, where damages might appear in the form of spalling, cracks and collapse of small segments of the façade. A few studies focused their damage detection on the façades using only post-event images. Nonetheless, several studies achieved better performances in damage detection approaches when considering multi-temporal image data. Hence, in this work a multi-temporal façade damage detection is tested. The first objective is to optimally merge pre- and post-event aerial oblique imagery within a supervised classification approach using convolutional neural networks to detect façade damages. The second objective is related to the fact that façades are normally depicted in several views in aerial manned photogrammetric surveys; hence, different procedures combining these multi-view image data are also proposed and embedded in the image classification approach. Six multi-temporal approaches are compared against 3 mono-temporal ones. The results indicate the superiority of multi-temporal approaches (up to ~25% in f1-score) when compared to the mono-temporal ones. The best performing multi-temporal approach takes as input sextuples (3 views per epoch, per façade) within a late fusion approach to perform the image classification of façade damages. However, the detection of small damages, such as smaller cracks or smaller areas of spalling, remains challenging in this approach, mainly due to the low resolution (~0.14 m ground sampling distance) of the dataset used. 相似文献
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