共查询到20条相似文献，搜索用时 31 毫秒
1.
Youness Dehbi Fabian Hadiji Gerhard Gröger Kristian Kersting Lutz Plümer 《Transactions in GIS》2017,21(1):134-150
The automatic interpretation of 3D point clouds for building reconstruction is a challenging task. The interpretation process requires highly structured models representing semantics. Formal grammars can describe structures as well as the parameters of buildings and their parts. We propose a novel approach for the automatic learning of weighted attributed context‐free grammar rules for 3D building reconstruction, supporting the laborious manual design of rules. We separate structure from parameter learning. Specific Support Vector Machines (SVMs) are used to generate a weighted context‐free grammar and predict structured outputs such as parse trees. The grammar is extended by parameters and constraints, which are learned based on a statistical relational learning method using Markov Logic Networks (MLNs). MLNs enforce the topological and geometric constraints. MLNs address uncertainty explicitly and provide probabilistic inference. They are able to deal with partial observations caused by occlusions. Uncertain projective geometry is used to deal with the uncertainty of the observations. Learning is based on a large building database covering different building styles and façade structures. In particular, a treebank that has been derived from the database is employed for structure learning. 相似文献
2.
Xuke Hu Hongchao Fan Alexey Noskov Zhiyong Wang Alexander Zipf Fuqiang Gu Jianga Shang 《Transactions in GIS》2021,25(1):71-111
Semantically rich maps are the foundation of indoor location‐based services. Many map providers such as OpenStreetMap and automatic mapping solutions focus on the representation and detection of geometric information (e.g., shape of room) and a few semantics (e.g., stairs and furniture) but neglect room usage. To mitigate the issue, this work proposes a general room tagging method for public buildings, which can benefit both existing map providers and automatic mapping solutions by inferring the missing room usage based on indoor geometric maps. Two kinds of statistical learning‐based room tagging methods are adopted: traditional machine learning (e.g., random forests) and deep learning, specifically relational graph convolutional networks (R‐GCNs), based on the geometric properties (e.g., area), topological relationships (e.g., adjacency and inclusion), and spatial distribution characteristics of rooms. In the machine learning‐based approach, a bidirectional beam search strategy is proposed to deal with the issue that the tag of a room depends on the tag of its neighbors in an undirected room sequence. In the R‐GCN‐based approach, useful properties of neighboring nodes (rooms) in the graph are automatically gathered to classify the nodes. Research buildings are taken as examples to evaluate the proposed approaches based on 130 floor plans with 3,330 rooms by using fivefold cross‐validation. The experiments conducted show that the random forest‐based approach achieves a higher tagging accuracy (0.85) than R‐GCN (0.79). 相似文献
3.
Segmentation of mobile laser point clouds of urban scenes into objects is an important step for post-processing (e.g., interpretation) of point clouds. Point clouds of urban scenes contain numerous objects with significant size variability, complex and incomplete structures, and holes or variable point densities, raising great challenges for the segmentation of mobile laser point clouds. This paper addresses these challenges by proposing a shape-based segmentation method. The proposed method first calculates the optimal neighborhood size of each point to derive the geometric features associated with it, and then classifies the point clouds according to geometric features using support vector machines (SVMs). Second, a set of rules are defined to segment the classified point clouds, and a similarity criterion for segments is proposed to overcome over-segmentation. Finally, the segmentation output is merged based on topological connectivity into a meaningful geometrical abstraction. The proposed method has been tested on point clouds of two urban scenes obtained by different mobile laser scanners. The results show that the proposed method segments large-scale mobile laser point clouds with good accuracy and computationally effective time cost, and that it segments pole-like objects particularly well. 相似文献
4.
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
5.
Florian W. Fichtner Abdoulaye A. Diakité Sisi Zlatanova Robert Voûte 《Transactions in GIS》2018,22(1):233-248
3D indoor navigation in multi‐story buildings and under changing environments is still difficult to perform. 3D models of buildings are commonly not available or outdated. 3D point clouds turned out to be a very practical way to capture 3D interior spaces and provide a notion of an empty space. Therefore, pathfinding in point clouds is rapidly emerging. However, processing of raw point clouds can be very expensive, as these are semantically poor and unstructured data. In this article we present an innovative octree‐based approach for processing of 3D indoor point clouds for the purpose of multi‐story pathfinding. We semantically identify the construction elements, which are of importance for the indoor navigation of humans (i.e., floors, walls, stairs, and obstacles), and use these to delineate the available navigable space. To illustrate the usability of this approach, we applied it to real‐world data sets and computed paths considering user constraints. The structuring of the point cloud into an octree approximation improves the point cloud processing and provides a structure for the empty space of the point cloud. It is also helpful to compute paths sufficiently accurate in their consideration of the spatial complexity. The entire process is automatic and able to deal with a large number of multi‐story indoor environments. 相似文献
6.
Identification and Modelling of Translational and Axial Symmetries and their Hierarchical Structures in Building Footprints by Formal Grammars 
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下载免费PDF全文 Buildings and other man‐made objects, for many reasons such as economical or aesthetic, are often characterized by their symmetry. The latter predominates in the design of building footprints and building parts such as façades. Thus the identification and modeling of this valuable information facilitates the reconstruction of these buildings and their parts. This article presents a novel approach for the automatic identification and modelling of symmetries and their hierarchical structures in building footprints, providing an important prior for façade and roof reconstruction. The uncertainty of symmetries is explicitly addressed using supervised machine learning methods, in particular Support Vector Machines (SVMs). Unlike classical statistical methods, for SVMs assumptions on the a priori distribution of the data are not required. Both axial and translational symmetries are detected. The quality of the identified major and minor symmetry axes is assessed by a least squares based adjustment. Context‐free formal grammar rules are used to model the hierarchical and repetitive structure of the underlying footprints. We present an algorithm which derives grammar rules based on the previously acquired symmetry information and using lexical analysis describing regular patterns and palindrome‐like structures. This offers insights into the latent structures of building footprints and therefore describes the associated façade in a relational and compact way. 相似文献
7.
空间数据库是通过建立并执行空间完整性约束来维护目标及其间关系的精确性、正确性、有效性。空间完整性约束包括对几何结构冲突、属性冲突及空间关系冲突约束,其中,空间关系冲突是指目标间的拓扑、方向及度量关系应符合空间完整性约束。在地籍数据库中,空间对象拓扑关系约束是为防止任意对象及其目标间的拓扑关系冲突,避免造成数据库对实体及其之间关系的表达错误,导致自身几何结构和对象间拓扑关系错误等,从而破坏了空间数据库的完整性。 相似文献
8.
高精度的地面LiDAR点云配准是空间目标三维表面拓扑重建的关键,针对待配准LiDAR点云和基准LiDAR点云存在位置、姿态和比例缩放差异的问题,提出了基于直线簇的地面LiDAR点云配准方法。首先,根据直线间相交、平行和异面的拓扑关系,分别对待配准和基准LiDAR点云的直线进行聚簇,构建直线簇;然后,分别将同名直线用Plücker坐标表示,通过待配准LiDAR点云的直线簇在空间中的螺旋缩放运动,使其与基准LiDAR点云的直线簇比例尺一致,且同名Plücker直线重合,构建基于直线簇的共线条件方程,实现了比例因子和相对位姿一体化解算。实验结果表明,直线簇的螺旋缩放增强了配准方程的几何约束性,提高了抗噪声能力,实现了高精度的地面LiDAR点云配准。 相似文献
9.
基于三角网光滑规则的LiDAR点云噪声剔除算法 总被引：1，自引：0，他引：1
通过对传统移动均值法、频率域信号分析等离散点云噪声剔除算法局限性的分析,结合LiDAR点云离散空间分布特性,本文提出一种基于三角网光滑规则的点云噪声剔除算法。该算法先快速生成离散点云的二维Delaunay三角网,并构建任意点的邻接拓扑关系,然后依据设定的光滑规则进行噪声信号点检测,并输出非噪声点信号。针对条带数据进行实验,结论表明本文算法适合离散点状噪声剔除,可较大程度提高点云数据的信噪比。 相似文献
10.
Sandra Loch-Dehbi Lutz Plümer 《ISPRS Journal of Photogrammetry and Remote Sensing》2011,66(2):177-187
This paper presents a novel approach to automated geometric reasoning for 3D building models. Geometric constraints like orthogonality or parallelity play a prominent role in man-made objects such as buildings. Thus, constraint based modelling, that specifies buildings by their individual components and the constraints between them, is a common approach in 3D city models. Since prototyped building models allow one to incorporate a priori knowledge they support the 3D reconstruction of buildings from point clouds and allow the construction of virtual cities. However, high level building models have a high degree of complexity and consequently are not easily manageable. Interactive tools are needed which facilitate the development of consistent models that, for instance, do not entail internal logical contradictions. Furthermore, there is often an interest in a compact, redundancy-free representation. We propose an approach that uses algebraic methods to prove that a constraint is deducible from a set of premises. While automated reasoning in 2D models is practical, a substantial increase in complexity can be observed in the transition to the three-dimensional space. Apart from that, algebraic theorem provers are restricted to crisp constraints so far. Thus, they are unable to handle quality issues, which are, however, an important aspect of GIS data and models. In this article we present an approach to automatic 3D reasoning which explicitly addresses uncertainty. Hereby, our aim is to support the interactive modelling of 3D city models and the automatic reconstruction of buildings. Geometric constraints are represented by multivariate polynomials whereas algebraic reasoning is based on Wu’s method of pseudodivision and characteristic sets. The reasoning process is further supported by logical inference rules. In order to cope with uncertainty and to address quality issues the reasoner integrates uncertain projective geometry and statistical hypothesis tests. Consequently, it allows one to derive uncertain conclusions from uncertain premises. The quality of such conclusions is quantified in a way which is sound both from a logical and a statistical perspective. 相似文献
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《制图学和地理信息科学》2013,40(4):210-221
In the generalization of a concept, we seek to preserve the essential characteristics and behavior of objects. In map generalization, the appropriate selection and application of procedures (such as merging, exaggeration, and selection) require information at the geometric, attribute, and topological levels. This article highlights the potential of graph theoretic representations in providing the topological information necessary for the efficient and effective application of specific generalization procedures. Besides ease of algebraic manipulation, the principal benefit of a graph theoretic approach is the ability to detect and thus preserve topological characteristics of map objects such as isolation, adjacency, and connectivity. While it is true that topologically based systems have been developed for consistency checking and error detection during editing, this article emphasizes the benefits from a map-generalization perspective. Examples are given with respect to specific generalization procedures and are summarized as a partial set of rules for potential inclusion in a cartographic knowledge-based system. 相似文献
13.
Frank A. van den Heuvel 《ISPRS Journal of Photogrammetry and Remote Sensing》1998,53(6):354-368
Photogrammetry has many advantages as a technique for the acquisition of three-dimensional models for virtual reality. But the traditional photogrammetric process to extract 3D geometry from multiple images is often considered too labour-intensive. In this paper a method is presented with which a polyhedral object model can be efficiently derived from measurements in a single image combined with geometric knowledge on the object. Man-made objects can often be described by a polyhedral model and usually many geometric constraints are valid. These constraints are inferred during image interpretation or may even be extracted automatically. In this paper different types of geometric constraints and their use for object reconstruction are discussed. Applying more constraints than needed for reconstruction will lead to redundancy and thereby to the need for an adjustment. The redundancy is the basis for reliability that is introduced by testing for possible measurement errors. The adjusted observations are used for object reconstruction in a separate step. Of course the model that is obtained from a single image will not be complete, for instance due to occlusion. An arbitrary number of models can be combined using similarity transformations based on the coordinates of common points. The information gathered allows for a bundle adjustment if highest accuracy is strived for. In virtual reality applications this is generally not the case, as quality is mainly determined by visual perception. A visual aspect of major importance is the photo-realistic texture mapped to the faces of the object. This texture is extracted from the same (single) image. In this paper the measurement process, the different types of constraints, their adjustment and the object model reconstruction are treated. A practical application of the proposed method is discussed in which a texture mapped model of a historic building is constructed and the repeatability of the method is assessed. The application shows the feasibility of the method and the potential of photogrammetry as an efficient tool for the production of 3D models for virtual reality applications. 相似文献
14.
Michael Govorov Giedr&#x; Beconyt&#x; Gennady Gienko Viktor Putrenko 《Transactions in GIS》2019,23(5):1048-1077
In this article, multilayer perceptron (MLP) network models with spatial constraints are proposed for regionalization of geostatistical point data based on multivariate homogeneity measures. The study focuses on non‐stationarity and autocorrelation in spatial data. Supervised MLP machine learning algorithms with spatial constraints have been implemented and tested on a point dataset. MLP spatially weighted classification models and an MLP contiguity‐constrained classification model are developed to conduct spatially constrained regionalization. The proposed methods have been tested with an attribute‐rich point dataset of geological surveys in Ukraine. The experiments show that consideration of the spatial effects, such as the use of spatial attributes and their respective whitening, improve the output of regionalization. It is also shown that spatial sorting used to preserve spatial contiguity leads to improved regionalization performance. 相似文献
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This paper presents a generative statistical approach to automatic 3D building roof reconstruction from airborne laser scanning point clouds. In previous works, bottom-up methods, e.g., points clustering, plane detection, and contour extraction, are widely used. Due to the data artefacts caused by tree clutter, reflection from windows, water features, etc., the bottom-up reconstruction in urban areas may suffer from a number of incomplete or irregular roof parts. Manually given geometric constraints are usually needed to ensure plausible results. In this work we propose an automatic process with emphasis on top-down approaches. The input point cloud is firstly pre-segmented into subzones containing a limited number of buildings to reduce the computational complexity for large urban scenes. For the building extraction and reconstruction in the subzones we propose a pure top-down statistical scheme, in which the bottom-up efforts or additional data like building footprints are no more required. Based on a predefined primitive library we conduct a generative modeling to reconstruct roof models that fit the data. Primitives are assembled into an entire roof with given rules of combination and merging. Overlaps of primitives are allowed in the assembly. The selection of roof primitives, as well as the sampling of their parameters, is driven by a variant of Markov Chain Monte Carlo technique with specified jump mechanism. Experiments are performed on data-sets of different building types (from simple houses, high-rise buildings to combined building groups) and resolutions. The results show robustness despite the data artefacts mentioned above and plausibility in reconstruction. 相似文献
17.
基于统计归纳学习的GIS属性数据挖掘 总被引：7，自引：0，他引：7
将统计分析方法和面向属性的归纳方法结合起来，形成了一种应用面比较广的统计归纳学习方法，可以用于GIS属性数据挖掘。同时提出GIS属性数据挖掘可以分为3个层次，包括从数据生成新的数据，从数据产生模型和从归纳出知识，由原始数据生成数据，可以得出变量之间粗浅的关系，从数据推导出的模型，可以定量描述变量之间的关系，由数据挖掘出的知识，可以揭示客观世界的普遍性规律。 相似文献
18.
遥感影像的计算机解译已广泛地应用于许多领域，但要达到完全自动地解译的目标仍然存在不少问题。本文研究的问题是，在统计分类的基础上，引入空间推理方法，对遥感影像进行专题解译，直至最后自动地输出专题图。据此，文中提出了按专题影像分析需要所形成的由原始影像、分割影像、等质区影像、分类解译影像和功能区影像等构成的表达法层次。在低层次影像分析中，影像按照统计属性被分割；在中间层次和高层次的影像分析中，与地物和专题有关的结构和空间知识被利用。本文说明了四叉树生成、复合形标记、复合形归并和匹配等在沟通不同的表达法中的作用。设计了应用于专题影像解译的差别图。介绍了与差别图和空间分析有关的规则。最后，上述技术已被有效地应用于中国吉林省双阳县的植被调查。 相似文献
19.
基于点集拓扑学的三维拓扑空间关系形式化描述 总被引：24，自引：3，他引：24
本文阐明了研究空间关系理论的必要性，分析了拓扑空间关系描述方法的研究进展及存在问题，以点集拓扑理论为基础，运用维数扩展的方法，提出三维拓扑空间关系完善和形式化的描述框架，在此基础上，对三维空间目标中存在着的拓扑空间关系进行了分类，定义了五种基本的拓扑空间关系，并且给出了三维拓扑空间关系最小集的互斥性与完备性证明。 相似文献
20.
Individual tree crown delineation is of great importance for forest inventory and management. The increasing availability of high-resolution airborne light detection and ranging (LiDAR) data makes it possible to delineate the crown structure of individual trees and deduce their geometric properties with high accuracy. In this study, we developed an automated segmentation method that is able to fully utilize high-resolution LiDAR data for detecting, extracting, and characterizing individual tree crowns with a multitude of geometric and topological properties. The proposed approach captures topological structure of forest and quantifies topological relationships of tree crowns by using a graph theory-based localized contour tree method, and finally segments individual tree crowns by analogy of recognizing hills from a topographic map. This approach consists of five key technical components: (1) derivation of canopy height model from airborne LiDAR data; (2) generation of contours based on the canopy height model; (3) extraction of hierarchical structures of tree crowns using the localized contour tree method; (4) delineation of individual tree crowns by segmenting hierarchical crown structure; and (5) calculation of geometric and topological properties of individual trees. We applied our new method to the Medicine Bow National Forest in the southwest of Laramie, Wyoming and the HJ Andrews Experimental Forest in the central portion of the Cascade Range of Oregon, U.S. The results reveal that the overall accuracy of individual tree crown delineation for the two study areas achieved 94.21% and 75.07%, respectively. Our method holds great potential for segmenting individual tree crowns under various forest conditions. Furthermore, the geometric and topological attributes derived from our method provide comprehensive and essential information for forest management. 相似文献