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 quality assessment of precast concrete elements with geometry irregularities using terrestrial laser scanning
TL;DR: In this article, an automated quality assessment technique which estimates the dimensions of precast concrete elements with geometry irregularities using terrestrial laser scanners (TLS) is presented. But this technique is not suitable for real-world applications.
Journal ArticleDOI
Hbim for conservation and management of built heritage: towards a library of vaults and wooden bean floors
TL;DR: In this paper, the authors illustrate the utility to switch from a 3D content model to a Historic Building Information Modelling (HBIM) in order to support conservation and management of built heritage.
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Automatic thermographic and RGB texture of as-built BIM for energy rehabilitation purposes
TL;DR: This paper proposes a methodology for the automatic generation of textured as-built models, starting with data acquisition and continuing with geometric and thermographic data processing.
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Parametric as-built model generation of complex shapes from point clouds
TL;DR: A novel semi-automated method for the generation of 3D parametric as-built models from point clouds intended as a multi-step process where NURBS curves and surfaces are used to reconstruct complex and irregular objects, without excessive simplification of the information encapsulated into huge point clouds.
Journal ArticleDOI
Point Cloud Semantic Segmentation Using a Deep Learning Framework for Cultural Heritage
Roberto Pierdicca,Marina Paolanti,Francesca Matrone,Massimo Martini,Christian Morbidoni,Eva Savina Malinverni,Emanuele Frontoni,Andrea Maria Lingua +7 more
TL;DR: A DL framework for Point Cloud segmentation is proposed, which employs an improved DGCNN (Dynamic Graph Convolutional Neural Network) by adding meaningful features such as normal and colour to make the dataset the least possible uniform and homogeneous.
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.