Journal ArticleDOI
Automatic reconstruction of as-built building information models from laser-scanned point clouds: A review of related techniques
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TLDR
This article surveys techniques developed in civil engineering and computer science that can be utilized to automate the process of creating as-built BIMs and outlines the main methods used by these algorithms for representing knowledge about shape, identity, and relationships.About:
This article is published in Automation in Construction.The article was published on 2010-11-01. It has received 789 citations till now. The article focuses on the topics: Information model & Computer Aided Design.read more
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Automated 3D volumetric reconstruction of multiple-room building interiors for as-built BIM
TL;DR: This study inputs just the registered three-dimensional (3D) point-cloud data and segmented the point cloud into separate rooms for more effective performance of the later modeling phases for each room, enabling detection and modeling of the hollow areas as windows or doors.
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Rapid data quality oriented laser scan planning for dynamic construction environments
TL;DR: Validation results of multiple case studies show that the proposed laser scan planning method can produce laser-scanning data with higher quality than data collected by experienced professionals, and without increasing the data collection time.
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From point cloud data to building information modelling: an automatic parametric workflow for heritage
TL;DR: This work advances on the parametric modelling from remote sensing point cloud data, which is carried out under the Rhino+Grasshopper-ArchiCAD combination, and enables the automatic conversion of TLS and SFM point cloudData into textured 3D meshes and thus BIM objects to be included in the HBIM project.
Journal ArticleDOI
Planning for terrestrial laser scanning in construction: A review
TL;DR: This work identifies gaps in knowledge that require further research such as the need to establish a fully automated scan plan which provides the optimum coverage in construction domain specifically for indoor construction and provides a framework for others to compare new P4S methods against the existing state of the art in the field.
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Classification of sensor independent point cloud data of building objects using random forests
TL;DR: A generic approach to automatically identify structural elements for the purposes of Scan-to-BIM by taking a set of planar primitives that are pre-segmented from the point cloud.
References
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Journal ArticleDOI
Random sample consensus: a paradigm for model fitting with applications to image analysis and automated cartography
TL;DR: New results are derived on the minimum number of landmarks needed to obtain a solution, and algorithms are presented for computing these minimum-landmark solutions in closed form that provide the basis for an automatic system that can solve the Location Determination Problem under difficult viewing.
Journal ArticleDOI
A taxonomy and evaluation of dense two-frame stereo correspondence algorithms
TL;DR: This paper has designed a stand-alone, flexible C++ implementation that enables the evaluation of individual components and that can easily be extended to include new algorithms.
Journal ArticleDOI
Recognition-by-Components: A Theory of Human Image Understanding.
TL;DR: Recognition-by-components (RBC) provides a principled account of the heretofore undecided relation between the classic principles of perceptual organization and pattern recognition.
Journal ArticleDOI
The FERET evaluation methodology for face-recognition algorithms
TL;DR: Two of the most critical requirements in support of producing reliable face-recognition systems are a large database of facial images and a testing procedure to evaluate systems.
Proceedings ArticleDOI
A volumetric method for building complex models from range images
Brian Curless,Marc Levoy +1 more
TL;DR: This paper presents a volumetric method for integrating range images that is able to integrate a large number of range images yielding seamless, high-detail models of up to 2.6 million triangles.