Special Issue "3D Indoor Mapping and BIM Reconstruction"

A special issue of Remote Sensing (ISSN 2072-4292). This special issue belongs to the section "Urban Remote Sensing".

Deadline for manuscript submissions: 31 August 2021.

Special Issue Editors

Dr. Maarten Bassier
E-Mail Website
Guest Editor
Geomatics Research Group, Department of Civil Engineering, KU Leuven, 9000 Gent, Belgium
Interests: Building Information Modeling (BIM); 3D reconstruction; point cloud; Laser scanning; photogrammetry; Machine Learning; Construction Monitoring
Dr. Florent Poux
E-Mail Website
Guest Editor
Geomatics Unit, University of Liège, Allée du six Août, 19-4000 Liège, Belgium
Interests: point clouds; laser scanning; 3D modeling
Special Issues and Collections in MDPI journals
Dr. Shayan Nikoohemat
E-Mail Website
Guest Editor
Department of Earth Observation Science, University of Twente, 7522 NB Enschede, The Netherlands
Interests: spatial analysis; mapping; geoinformation
Special Issues and Collections in MDPI journals
Dr. Maarten Vergauwen
E-Mail Website
Guest Editor
Geomatics Research Group, Department of Civil Engineering, KU Leuven, 3000 Leuven, Belgium
Interests: photogrammetry; computer vision; machine learning

Special Issue Information

Dear Colleagues,

Digital reconstruction of buildings and infrastructure is being extensively researched. The resulting enriched BIM and GIS models of built assets are becoming increasingly important for facility management, project planning, and refurbishment. However, the current state of the art is not yet able to produce the required geometries in a reliable unsupervised manner.

Typically, point cloud data or photogrammetric inputs are used to reconstruct the digital assets in the built environment. This requires the unsupervised interpretation of the scenery and the automated parameter extraction for the widely varying domain-specific objects, i.e., heritage, structure, MEP, and architecture finishes. In the last few years, there has been intense research activity towards the automated modeling of BIM/GIS. However, there is still important work to be done involving (i) the production and processing of highly accurate point clouds, (ii) scene interpretation including semantic segmentation, and (iii) parameter extraction for the final BIM/GIS models.

This Special Issue will collect new technologies and methodologies that target the above objectives. We welcome submissions that cover but are not limited to the following:

  • Geometric evaluation of mapping systems;
  • Indoor data structures and models;
  • Scan-vs-BIM and building change detection;
  • Automated data analysis of 3D data (segmentation, classification, etc.);
  • Indoor reconstruction;
  • Scan-to-BIM standards (e.g., IFC);
  • Scan-to-GIS standards (e.g., CityGML);
  • Multi-dimensional model representations (4D, 5D, etc.);

Dr. Maarten Bassier
Dr. Florent Poux
Dr. Shayan Nikoohemat
Dr. Maarten Vergauwen
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All papers will be peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Remote Sensing is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2400 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • Point cloud processing
  • 3D reconstruction
  • Classification
  • Meshing
  • Spatial analysis
  • BIM
  • GIS

Published Papers (1 paper)

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Research

Article
RegARD: Symmetry-Based Coarse Registration of Smartphone’s Colorful Point Clouds with CAD Drawings for Low-Cost Digital Twin Buildings
Remote Sens. 2021, 13(10), 1882; https://0-doi-org.brum.beds.ac.uk/10.3390/rs13101882 - 11 May 2021
Viewed by 421
Abstract
Coarse registration of 3D point clouds plays an indispensable role for parametric, semantically rich, and realistic digital twin buildings (DTBs) in the practice of GIScience, manufacturing, robotics, architecture, engineering, and construction. However, the existing methods have prominently been challenged by (i) the high [...] Read more.
Coarse registration of 3D point clouds plays an indispensable role for parametric, semantically rich, and realistic digital twin buildings (DTBs) in the practice of GIScience, manufacturing, robotics, architecture, engineering, and construction. However, the existing methods have prominently been challenged by (i) the high cost of data collection for numerous existing buildings and (ii) the computational complexity from self-similar layout patterns. This paper studies the registration of two low-cost data sets, i.e., colorful 3D point clouds captured by smartphones and 2D CAD drawings, for resolving the first challenge. We propose a novel method named ‘Registration based on Architectural Reflection Detection’ (RegARD) for transforming the self-symmetries in the second challenge from a barrier of coarse registration to a facilitator. First, RegARD detects the innate architectural reflection symmetries to constrain the rotations and reduce degrees of freedom. Then, a nonlinear optimization formulation together with advanced optimization algorithms can overcome the second challenge. As a result, high-quality coarse registration and subsequent low-cost DTBs can be created with semantic components and realistic appearances. Experiments showed that the proposed method outperformed existing methods considerably in both effectiveness and efficiency, i.e., 49.88% less error and 73.13% less time, on average. The RegARD presented in this paper first contributes to coarse registration theories and exploitation of symmetries and textures in 3D point clouds and 2D CAD drawings. For practitioners in the industries, RegARD offers a new automatic solution to utilize ubiquitous smartphone sensors for massive low-cost DTBs. Full article
(This article belongs to the Special Issue 3D Indoor Mapping and BIM Reconstruction)
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