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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Journal ArticleDOI
Digitisation in facilities management: A literature review and future research directions
TL;DR: A number of possibilities for future research into DT in FM are identified, including enhancing the interoperability of data, improving the accuracy of point cloud data for developing as-built models for existing facilities, and generating effective BIM/GIS asset database integration.
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Tracking the Built Status of MEP Works: Assessing the Value of a Scan-vs-BIM System
TL;DR: In this article, a Scan-vs-BIM object recognition framework was proposed for the automatic assessment of as-built status of MEP works in construction projects at a cost that continues to decline.
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State-of-the-art on research and applications of machine learning in the building life cycle
TL;DR: This study systematically surveyed how machine learning has been applied at different stages of building life cycle and can inform future machine learning research to improve occupant comfort, energy efficiency, demand flexibility, and resilience of buildings.
Journal ArticleDOI
Potential of big visual data and building information modeling for construction performance analytics: An exploratory study
Kevin Han,Mani Golparvar-Fard +1 more
TL;DR: This paper investigates current strategies for leveraging emerging big visual data and BIM in construction performance monitoring from these three aspects, characterizes gaps in knowledge via case studies and structures a road map for research in visual sensing and analytics.
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Review: deep learning on 3D point clouds
TL;DR: A survey of the recent state-of-the-art deep learning techniques that mainly focused on point cloud data, introducing the popular 3D point cloud benchmark datasets, and discussing the application of deep learning in popular3D vision tasks including classification, segmentation and detection.
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.