SmartBoxes for interactive urban reconstruction

Publication informationComputer Aided Design, 53 : 46-61 Publisher ElsevierItem record/more information http://hdl.handle.net/10197/7451 Publisher's statementThis is the author's version of a work that was accepted for publication in Computer Aided Design. Changes resulting from the publishing process, such as peer review, editing, corrections, structural formatting, and other quality control mechanisms may not be reflected in this document. Changes may have been made to this work since it was submitted for publication. A definitive version was subsequently published in Computer Aided Design (VOL 53, ISSUE 2014, (2014 ABSTRACT:This paper introduces a new, octree-based algorithm to assist in the automated conversion of laser scanning point cloud data into solid models appropriate for computational analysis. The focus of the work is for typical, urban, vernacular structures to assist in better damage prediction prior to tunnelling. The proposed FacadeVoxel algorithm automatically detects boundaries of building façades and their openings. Next, it checks and automatically fills unintentional occlusions. The proposed method produced robust and efficient reconstructions of building models from various data densities. When compared to measured drawings, the reconstructed building models were good agreement, with only 1% relative errors in overall dimensions and 3% errors in openings. In addition, the proposed algorithm was significantly faster than other automatic approaches without compromising accuracy.

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“…Additionally, there are some interactive tools to support users in the fast reconstruction of building models (e.g. [19,20]). …”

Section: Related Workmentioning

confidence: 99%

Octree-based, automatic building façade generation from LiDAR data

Truong-Hong

Laefer

2014

Computer-Aided Design

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“…This technology opened up many interesting research problems concerned with processing such data (Xiao et al 2015;Li et al 2016;Vanegas, Aliaga, and Benes 2012;Lin et al 2013;Arikan et al 2013;Nan et al 2010;Zhou and Neumann 2010). Despite these recent efforts, the fully automatic generation of building mass models from noisy, incomplete point clouds still remains an open problem.…”

Section: Introductionmentioning

confidence: 99%

Fitting boxes to Manhattan scenes using linear integer programming

Nan

Liu

2016

International Journal of Digital Earth

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We propose an approach for automatic generation of building models by assembling a set of boxes using a Manhattan-world assumption. The method first aligns the point cloud with a per-building local coordinate system, and then fits axis-aligned planes to the point cloud through an iterative regularization process. The refined planes partition the space of the data into a series of compact cubic cells (candidate boxes) spanning the entire 3D space of the input data. We then choose to approximate the target building by the assembly of a subset of these candidate boxes using a binary linear programming formulation. The objective function is designed to maximize the point cloud coverage and the compactness of the final model. Finally, all selected boxes are merged into a lightweight polygonal mesh model, which is suitable for interactive visualization of large scale urban scenes. Experimental results and a comparison with state-of-the-art methods demonstrate the effectiveness of the proposed framework.

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“…Since streetlights, traffic signals, and utility poles are managed by different organizations, their classification is very important for asset management and maintenance. Identification of object classes is also required for shape reconstruction, because knowledge-based methods are typical for reconstructing 3D shapes from incomplete point-clouds (Nan et al, 2010;Nan et al, 2012;Kim et al, 2012;Lin et al, 2013). Some researchers studied shape classification for pole-like objects (Yokoyama et al, 2011;Cabo et al, 2014;Yang et al, 2015;Kamal et al, 2013;Pu et al, 2011;Li and Oude Elberink, 2013).…”

Section: Introductionmentioning

confidence: 99%

Detection and Classification of Pole-Like Objects From Mobile Mapping Data

Fukano

Masuda

2015

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

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ABSTRACT:Laser scanners on a vehicle-based mobile mapping system can capture 3D point-clouds of roads and roadside objects. Since roadside objects have to be maintained periodically, their 3D models are useful for planning maintenance tasks. In our previous work, we proposed a method for detecting cylindrical poles and planar plates in a point-cloud. However, it is often required to further classify pole-like objects into utility poles, streetlights, traffic signals and signs, which are managed by different organizations. In addition, our previous method may fail to extract low pole-like objects, which are often observed in urban residential areas. In this paper, we propose new methods for extracting and classifying pole-like objects. In our method, we robustly extract a wide variety of poles by converting point-clouds into wireframe models and calculating cross-sections between wireframe models and horizontal cutting planes. For classifying pole-like objects, we subdivide a pole-like object into five subsets by extracting poles and planes, and calculate feature values of each subset. Then we apply a supervised machine learning method using feature variables of subsets. In our experiments, our method could achieve excellent results for detection and classification of pole-like objects.

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SmartBoxes for interactive urban reconstruction

Cited by 70 publications

References 24 publications

Octree-based, automatic building façade generation from LiDAR data

Octree-based, automatic building façade generation from LiDAR data

Fitting boxes to Manhattan scenes using linear integer programming

Detection and Classification of Pole-Like Objects From Mobile Mapping Data

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