Automatic Generation of Semantically Rich As-Built Building Information Models Using 2D Images: A Derivative-Free Optimization Approach

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@Article{Fan_Xue:CACIE,
  author =       "Fan Xue and Weisheng Lu and Ke Chen",
  title =        "Automatic Generation of Semantically Rich As-Built
                 Building Information Models Using {2D} Images: A
                 Derivative-Free Optimization Approach",
  journal =      "Computer-Aided Civil and Infrastructure Engineering",
  keywords =     "genetic algorithms, genetic programming",
  ISSN =         "1467-8667",
  URL =          "https://onlinelibrary.wiley.com/doi/abs/10.1111/mice.12378",
  DOI =          "doi:10.1111/mice.12378",
  size =         "16 pages",
  abstract =     "Over the past decade a considerable number of studies
                 have focused on generating semantically rich as-built
                 building information models (BIMs). However, the
                 prevailing methods rely on laborious manual
                 segmentation or automatic but error-prone segmentation.
                 In addition, the methods failed to make good use of
                 existing semantics sources. This article presents a
                 novel segmentation-free derivative-free optimization
                 (DFO) approach that translates the generation of
                 as-built BIMs from 2D images into an optimization
                 problem of fitting BIM components regarding
                 architectural and topological constraints. The
                 semantics of the BIMs are subsequently enriched by
                 linking the fitted components with existing semantics
                 sources. The approach was prototyped in two experiments
                 using an outdoor and an indoor case, respectively. The
                 results showed that in the outdoor case 12 out of 13
                 BIM components were correctly generated within 1.5
                 hours, and in the indoor case all target BIM components
                 were correctly generated with a root-mean-square
                 deviation (RMSD) of 3.9cm in about 2.5 hours. The main
                 computational novelties of this study are: (1) to
                 translate the automatic as-built BIM generation from 2D
                 images as an optimization problem; (2) to develop an
                 effective and segmentation-free approach that is
                 fundamentally different from prevailing methods; and
                 (3) to exploit online open BIM component information
                 for semantic enrichment, which, to a certain extent,
                 alleviates the dilemma between information inadequacy
                 and information overload in BIM development.",
  notes =        "Entered for 2018 HUMIES",
}

Genetic Programming entries for Fan Xue Wilson W S Lu Ke Chen

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