A generative statistical approach to automatic 3D building roof reconstruction from laser scanning data

Huang, Hai; Brenner, Claus; Sester, Monika

doi:10.1016/j.isprsjprs.2013.02.004

Cited by 137 publications

(90 citation statements)

References 9 publications

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“…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%

“…Among the reported studies, 2D-plans (Henn et al, 2013;Kada and McKinley, 2009), graph matching technique (Oude Elberink and Vosselman, 2009;Verma et al, 2006;Xiong et al, 2014) and convexity analysis (Lin et al, 2013) are used for this purpose. Unlike the aforementioned approaches, Huang et al (2013) extract roof primitives one by one from LiDAR points by using the RJMCMC technique. Although these model-driven approaches could perform well for sparse data, they are limited to roof primitives predefined in the model library.…”

Section: Introductionmentioning

confidence: 99%

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

Yan

Jiang

Shan

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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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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

Yan

Jiang

Shan

2016

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

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“…A Bayesian decision finds the optimal configuration of 3D-blockes using a Monte Carlo sampler. Huang et al, (2013) developed top-down combined with bottom-up approaches to reconstruct 3D building models from LiDAR points cloud. Based on a pre-defined primitive library, a generative statistical modelling is conducted to reconstruct roof models.…”

Section: Introductionmentioning

confidence: 99%

Statistical Building Roof Reconstruction From Worldview-2 Stereo Imagery

Partovi

Huang

Krauss

et al. 2015

Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci.

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ABSTRACT:3D building reconstruction from point clouds is an active research topic in remote sensing, photogrammetry and computer vision. Most of the prior research has been done on 3D building reconstruction from LiDAR data which means high resolution and dense data. The interest of this work is 3D building reconstruction from Digital Surface Models (DSM) of stereo image matching of space borne satellite data which cover larger areas than LiDAR datasets in one data acquisition step and can be used also for remote regions. The challenging problem is the noise of this data because of low resolution and matching errors. In this paper, a top-down and bottom-up method is developed to find building roof models which exhibit the optimum fit to the point clouds of the DSM. In the bottom up step of this hybrid method, the building mask and roof components such as ridge lines are extracted. In addition, in order to reduce the computational complexity and search space, roofs are classified to pitched and flat roofs as well. Ridge lines are utilized to estimate the roof primitives from a building library such as width, length, positions and orientation. Thereafter, a topdown approach based on Markov Chain Monte Carlo and simulated annealing is applied to optimize roof parameters in an iterative manner by stochastic sampling and minimizing the average of Euclidean distance between point cloud and model surface as fitness function. Experiments are performed on two areas of Munich city which include three roof types (hipped, gable and flat roofs). The results show the efficiency of this method in even for this type of noisy datasets.

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“…Model-driven approaches choose configurable building blocks from a library of pre-defined templates, determine their roof shape parameters to best fit the given data (see e.g. (Henn et al, 2013)), and possibly combine them with other blocks to generate more complex shapes (Huang et al, 2013). Pure data-driven approaches, on the other hand, aggregate the measured points to form higher order primitives (usually planar regions) and combine them to form surface models without any shape restrictions.…”

Section: Introductionmentioning

confidence: 99%

Feature-Driven 3D Building Modeling using Planar Halfspaces

Kada

Wichmann

2013

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

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ABSTRACT:With the exception of pure data-driven methods, reconstruction approaches for 3D building models from aerial point clouds often incorporate some level of model information to construct regularized and well-formed roof structures. In the proposed feature-driven approach, low-level roof features like ridge lines, gable and (half-) hip ends, intersecting ridge lines, dormers etc. are detected in a hierarchical rule-based feature recognition process based on a sub-surface segmentation of the point cloud. With each feature type, a number of planar half-spaces are associated that define a certain part of the resulting 3D building model as a mathematical inequality equation. The Boolean intersection of half-spaces define convex building components that can be united to generate more complex building shapes, where the features enforce that the half-spaces are nicely aligned.

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A generative statistical approach to automatic 3D building roof reconstruction from laser scanning data

Cited by 137 publications

References 9 publications

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

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

Statistical Building Roof Reconstruction From Worldview-2 Stereo Imagery

Feature-Driven 3D Building Modeling using Planar Halfspaces

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