A graph edit dictionary for correcting errors in roof topology graphs reconstructed from point clouds

Xiong, Boli; Elberink, Sander Oude; Vosselman, G.

doi:10.1016/j.isprsjprs.2014.01.007

Cited by 92 publications

(90 citation statements)

References 30 publications

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“…Rau [45] Images 0.8 0.6 Bulatov et al [8] Images 1.1 0.4 Oude Elbrink and Vosselman [46] LiDAR 0.8 0.1 Xiong et al [47] LiDAR 0.7 0.1 Perera et al [48] LiDAR 0.7 0.1 Dorninger and Pfeifer [16] LiDAR 0.8 0.1 Sohn et al [49] LiDAR 0.6 0.2 Xiao et al [50] LiDAR 0.8 0.1 Awrangjeb et al [14] LiDAR 0.8 0.1 Zarea et al [43] LiDAR 0.9 0.4 Our approach LiDAR + Image 0.6 0.1…”

Section: Researchers and Referencesmentioning

confidence: 99%

3D Building Roof Modeling by Optimizing Primitive’s Parameters Using Constraints from LiDAR Data and Aerial Imagery

et al. 2014

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Abstract:In this paper, a primitive-based 3D building roof modeling method, by integrating LiDAR data and aerial imagery, is proposed. The novelty of the proposed modeling method is to represent building roofs by geometric primitives and to construct a cost function by using constraints from both LiDAR data and aerial imagery simultaneously, so that the accuracy potential of the different sensors can be tightly integrated for the building model generation by an integrated primitive's parameter optimization procedure. To verify the proposed modeling method, both simulated data and real data with simple buildings provided by ISPRS (International Society for Photogrammetry and Remote Sensing), were used in this study. The experimental results were evaluated by the ISPRS, which demonstrate the proposed modeling method can integrate LiDAR data and aerial imagery to generate 3D building models with high accuracy in both the horizontal and vertical directions. The experimental results also show that by adding a component, such as a dormer, to the primitive, a variant of the simple primitive is constructed, and the proposed method can generate a building model with some details.

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Section: Researchers and Referencesmentioning

confidence: 99%

3D Building Roof Modeling by Optimizing Primitive’s Parameters Using Constraints from LiDAR Data and Aerial Imagery

et al. 2014

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“…(Airborne) LiDAR (Light Detection and Ranging) technology can directly collect dense, accurate 3D point clouds over building roofs, from which 3D building models may be automatically reconstructed. In spite of many efforts made in the past two decades (Haala and Kada, 2010), building roof reconstruction remains to be an open issue, largely due to the insufficiency of the data and the complexity of the actual building roofs (Xiong et al, 2014).…”

Section: Introductionmentioning

confidence: 99%

“…Among these studies, model-driven methods assume a building is an assembly of roof primitives (e.g., gable roof and hipped roof), which and whose topology are predefined in a model library (Tarsha-Kurdi et al, 2007). To extract roof primitives from LiDAR point clouds, techniques such as invariant moments (Maas and Vosselman, 1999), graph matching (Oude Elberink and Vosselman, 2009;Verma et al, 2006;Xiong et al, 2014), Support Vector Machine (SVM) (Henn et al, 2013;Satari et al, 2012), RANdom SAmple Consensus (RANSAC) (Henn et al, 2013) and Reversible Jump Markov Chain Monte Carlo (RJMCMC) (Huang et al, 2013) are used. However, these approaches tend to fail when reconstructing complex roof shapes.…”

Section: Introductionmentioning

confidence: 99%

A Global Solution to Topological Reconstruction of Building Roof Models From Airborne Lidar Point Clouds

Yan¹,

Jiang²,

Shen³

2016

ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci.

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ABSTRACT:This paper presents a global solution to building roof topological reconstruction from LiDAR point clouds. Starting with segmented roof planes from building LiDAR points, a BSP (binary space partitioning) algorithm is used to partition the bounding box of the building into volumetric cells, whose geometric features and their topology are simultaneously determined. To resolve the inside/outside labelling problem of cells, a global energy function considering surface visibility and spatial regularization between adjacent cells is constructed and minimized via graph cuts. As a result, the cells are labelled as either inside or outside, where the planar surfaces between the inside and outside form the reconstructed building model. Two LiDAR data sets of Yangjiang (China) and Wuhan University (China) are used in the study. Experimental results show that the completeness of reconstructed roof planes is 87.5%. Comparing with existing data-driven approaches, the proposed approach is global. Roof faces and edges as well as their topology can be determined at one time via minimization of an energy function. Besides, this approach is robust to partial absence of roof planes and tends to reconstruct roof models with visibility-consistent surfaces.

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“…Martin and Andreas [21,22] used planar half-space combination to form a semantic part of building model as a mathematical inequality equation. Xiong et al use graph to represent the topology of roof planes and sub graph to represent roof primitives [23,24]. Based on these model presentations, constrains derived from prior knowledge such as symmetries, co-planarity, parallelism, and orthogonality are used for model refinement [22,23].…”

mentioning

confidence: 99%

“…Based on these model presentations, constrains derived from prior knowledge such as symmetries, co-planarity, parallelism, and orthogonality are used for model refinement [22,23]. Xiong et al [24] apply a graph edit dictionary to correct the errors of roof topology effectively. Although it is effective to improve the final result, knowledge constrains are still challenging to detect automatically and reliability.…”

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confidence: 99%

A Model-Driven Approach for 3D Modeling of Pylon from Airborne LiDAR Data

Chen

2015

Remote Sensing

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Abstract:Reconstructing three-dimensional model of the pylon from LiDAR (Light Detection And Ranging) point clouds automatically is one of the key techniques for facilities management GIS system of high-voltage nationwide transmission smart grid. This paper presents a model-driven three-dimensional pylon modeling (MD3DM) method using airborne LiDAR data. We start with constructing a parametric model of pylon, based on its actual structure and the characteristics of point clouds data. In this model, a pylon is divided into three parts: pylon legs, pylon body and pylon head. The modeling approach mainly consists of four steps. Firstly, point clouds of individual pylon are detected and segmented from massive high-voltage transmission corridor point clouds automatically. Secondly, an individual pylon is divided into three relatively simple parts in order to reconstruct different parts with different strategies. Its position and direction are extracted by contour analysis of the pylon body in this stage. Thirdly, the geometric features of the pylon head are extracted, from which the head type is derived with a SVM (Support Vector Machine) classifier. After that, the head is constructed by seeking corresponding model OPEN ACCESSRemote Sens. 2015, 7 11502 from pre-build model library. Finally, the body is modeled by fitting the point cloud to planes. Experiment results on several point clouds data sets from China Southern high-voltage nationwide transmission grid from Yunnan Province to Guangdong Province show that the proposed approach can achieve the goal of automatic three-dimensional modeling of the pylon effectively.

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A graph edit dictionary for correcting errors in roof topology graphs reconstructed from point clouds

Cited by 92 publications

References 30 publications

3D Building Roof Modeling by Optimizing Primitive’s Parameters Using Constraints from LiDAR Data and Aerial Imagery

3D Building Roof Modeling by Optimizing Primitive’s Parameters Using Constraints from LiDAR Data and Aerial Imagery

A Global Solution to Topological Reconstruction of Building Roof Models From Airborne Lidar Point Clouds

A Model-Driven Approach for 3D Modeling of Pylon from Airborne LiDAR Data

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