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3D Indoor Mapping and BIM Reconstruction

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

Deadline for manuscript submissions: closed (30 November 2022) | Viewed by 18715

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Special Issue Editors

Dr. Maarten Bassier

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Guest Editor

Geomatics Research Group, Faculty of Engineering Technology, KU Leuven, Gent, Belgium
Interests: building information modeling (BIM); 3D reconstruction; point cloud; laser scanning; photogrammetry; machine learning; construction monitoring
Special Issues, Collections and Topics in MDPI journals

Dr. Florent Poux

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Guest Editor

Geomatics Unit, University of Liège, Allée du Six Août, 19-4000 Liège, Belgium
Interests: point clouds; laser scanning; 3D modeling
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Dr. Shayan Nikoohemat

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Guest Editor

Department of Earth Observation Science, University of Twente, 7522 NB Enschede, The Netherlands
Interests: spatial analysis; mapping; geoinformation
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Prof. Dr. Maarten Vergauwen

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Guest Editor

Geomatics Research Group, Department of Civil Engineering, KU Leuven, Gebroeders De Smetstraat 1, 9000 Ghent, Belgium
Interests: photogrammetry; computer vision; machine learning; 3D modeling
Special Issues, Collections and Topics in MDPI journals

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 submissions that pass pre-check are 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 2700 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 (7 papers)

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Editorial

Jump to: Research

3 pages, 172 KiB

Open AccessEditorial

3D Indoor Mapping and BIM Reconstruction Editorial

by Maarten Bassier, Florent Poux and Shayan Nikoohemat

Remote Sens. 2023, 15(7), 1913; https://doi.org/10.3390/rs15071913 - 02 Apr 2023

Viewed by 1449

Abstract

This Special Issue gathers papers reporting research on various aspects of the use of low-cost photogrammetric and lidar sensors for indoor building reconstruction. It includes contributions presenting improvements in the alignment of mobile mapping systems with and without a prior 3D BIM model, the interpretation of both imagery and lidar data of indoor scenery and finally the reconstruction and enrichment of existing 3D point clouds and meshes with BIM information. Concretely, the publications showcase methods and experiments for the Reconstruction of Indoor Navigation Elements for Point Cloud of Buildings with Occlusions and Openings by Wall Segment Restoration from Indoor Context Labeling, Two-Step Alignment of Mixed Reality Devices to Existing Building Data, Pose Normalization of Indoor Mapping Datasets Partially Compliant with the Manhattan World Assumption, A Robust Rigid Registration Framework of 3D Indoor Scene Point Clouds Based on RGB-D Information, 3D Point Cloud Semantic Augmentation for Instance Segmentation of 360° Panoramas by Deep Learning Techniques and the Symmetry-Based Coarse Registration of Smartphone’s Colorful Point Clouds with CAD Drawings (RegARD) for Low-Cost Digital Twin Buildings. Full article

(This article belongs to the Special Issue 3D Indoor Mapping and BIM Reconstruction)

Research

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24 pages, 22450 KiB

Open AccessArticle

Reconstruction of Indoor Navigation Elements for Point Cloud of Buildings with Occlusions and Openings by Wall Segment Restoration from Indoor Context Labeling

by Guangzu Liu, Shuangfeng Wei, Shaobo Zhong, Shuai Huang and Ruofei Zhong

Remote Sens. 2022, 14(17), 4275; https://doi.org/10.3390/rs14174275 - 30 Aug 2022

Cited by 6 | Viewed by 1846

Abstract

Indoor 3D reconstruction and navigation element extraction with point cloud data has become a research focus in recent years, which has important application in community refinement management, emergency rescue and evacuation, etc. Aiming at the problem that the complete wall surfaces cannot be obtained in the indoor space affected by the occluded objects and the existing methods of navigation element extraction are over-segmented or under-segmented, we propose a method to automatically reconstruct indoor navigation elements from unstructured 3D point cloud of buildings with occlusions and openings. First, the outline and occupancy information provided by the horizontal projection of the point cloud was used to guide the wall segment restoration. Second, we simulate the scanning process of a laser scanner for segmentation. Third, we use projection statistical graphs and given rules to identify missing wall surfaces and “hidden doors”. The method is tested on several building datasets with complex structures. The results show that the method can detect and reconstruct indoor navigation elements without viewpoint information. The means of deviation in the reconstructed models is between 0–5 cm, and the completeness and correction are greater than 80%. However, the proposed method also has some limitations for the extraction of “thick doors” with a large number of occluded, non-planar components. Full article

(This article belongs to the Special Issue 3D Indoor Mapping and BIM Reconstruction)

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27 pages, 24116 KiB

Open AccessArticle

Two-Step Alignment of Mixed Reality Devices to Existing Building Data

by Jelle Vermandere, Maarten Bassier and Maarten Vergauwen

Remote Sens. 2022, 14(11), 2680; https://doi.org/10.3390/rs14112680 - 03 Jun 2022

Cited by 5 | Viewed by 1983

Abstract

With the emergence of XR technologies, the demand for new time- and cost-saving applications in the AEC industry based on these new technologies is rapidly increasing. Their real-time feedback and digital interaction in the field makes these systems very well suited for construction site monitoring, maintenance, project planning, and so on. However, the continuously changing environments of construction sites and facilities requires extraordinary robust and dynamic data acquisition technologies to capture and update the built environment. New XR devices already have the hardware to accomplish these tasks, but the framework to document and geolocate multi-temporal mappings of a changing environment is still very much the subject of ongoing research. The goal of this research is, therefore, to study whether Lidar and photogrammetric technologies can be adapted to process XR sensory data and align multiple time series in the same coordinate system. Given the sometimes drastic changes on sites, we do not only use the sensory data but also any preexisting remote sensing data and as-is or as-designed BIM to aid the registration. In this work, we specifically study the low-resolution geometry and image matching of the Hololens 2 during consecutive stages of a construction. During the experiments, multiple time series of constructions are captured and registered. The experiments show that XR-captured data can be reliably registered to preexisting datasets with an accuracy that matches or exceeds the resolution of the sensory data. These results indicate that this method is an excellent way to align generic XR devices to a wide variety of existing reference data. Full article

(This article belongs to the Special Issue 3D Indoor Mapping and BIM Reconstruction)

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34 pages, 10184 KiB

Open AccessArticle

Pose Normalization of Indoor Mapping Datasets Partially Compliant with the Manhattan World Assumption

by Patrick Hübner, Martin Weinmann, Sven Wursthorn and Stefan Hinz

Remote Sens. 2021, 13(23), 4765; https://doi.org/10.3390/rs13234765 - 24 Nov 2021

Cited by 2 | Viewed by 1893

Abstract

Due to their great potential for a variety of applications, digital building models are well established in all phases of building projects. Older stock buildings however frequently lack digital representations, and creating these manually is a tedious and time-consuming endeavor. For this reason, the automated reconstruction of building models from indoor mapping data has arisen as an active field of research. In this context, many approaches rely on simplifying suppositions about the structure of buildings to be reconstructed such as, e.g., the well-known Manhattan World assumption. This however not only presupposes that a given building structure itself is compliant with this assumption, but also that the respective indoor mapping dataset is aligned with the coordinate axes. Indoor mapping systems, on the other hand, typically initialize the coordinate system arbitrarily by the sensor pose at the beginning of the mapping process. Thus, indoor mapping data need to be transformed from the local coordinate system, resulting from the mapping process, to a local coordinate system where the coordinate axes are aligned with the Manhattan World structure of the building. This necessary preprocessing step for many indoor reconstruction approaches is also frequently known as pose normalization. In this paper, we present a novel pose-normalization method for indoor mapping point clouds and triangle meshes that is robust against large portions of the indoor mapping geometries deviating from an ideal Manhattan World structure. In the case of building structures that contain multiple Manhattan World systems, the dominant Manhattan World structure supported by the largest fraction of geometries was determined and used for alignment. In a first step, a vertical alignment orienting a chosen axis to be orthogonal to horizontal floor and ceiling surfaces was conducted. Subsequently, a rotation around the resulting vertical axis was determined that aligned the dataset horizontally with the axes of the local coordinate system. The performance of the proposed method was evaluated quantitatively on several publicly available indoor mapping datasets of different complexity. The achieved results clearly revealed that our method is able to consistently produce correct poses for the considered datasets for different input rotations with high accuracy. The implementation of our method along with the code for reproducing the evaluation is made available to the public. Full article

(This article belongs to the Special Issue 3D Indoor Mapping and BIM Reconstruction)

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20 pages, 12619 KiB

Open AccessArticle

A Robust Rigid Registration Framework of 3D Indoor Scene Point Clouds Based on RGB-D Information

by Saishang Zhong, Mingqiang Guo, Ruina Lv, Jianguo Chen, Zhong Xie and Zheng Liu

Remote Sens. 2021, 13(23), 4755; https://doi.org/10.3390/rs13234755 - 24 Nov 2021

Cited by 4 | Viewed by 2003

Abstract

Rigid registration of 3D indoor scenes is a fundamental yet vital task in various fields that include remote sensing (e.g., 3D reconstruction of indoor scenes), photogrammetry measurement, geometry modeling, etc. Nevertheless, state-of-the-art registration approaches still have defects when dealing with low-quality indoor scene point clouds derived from consumer-grade RGB-D sensors. The major challenge is accurately extracting correspondences between a pair of low-quality point clouds when they contain considerable noise, outliers, or weak texture features. To solve the problem, we present a point cloud registration framework in view of RGB-D information. First, we propose a point normal filter for effectively removing noise and simultaneously maintaining sharp geometric features and smooth transition regions. Second, we design a correspondence extraction scheme based on a novel descriptor encoding textural and geometry information, which can robustly establish dense correspondences between a pair of low-quality point clouds. Finally, we propose a point-to-plane registration technology via a nonconvex regularizer, which can further diminish the influence of those false correspondences and produce an exact rigid transformation between a pair of point clouds. Compared to existing state-of-the-art techniques, intensive experimental results demonstrate that our registration framework is excellent visually and numerically, especially for dealing with low-quality indoor scenes. Full article

(This article belongs to the Special Issue 3D Indoor Mapping and BIM Reconstruction)

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32 pages, 10916 KiB

Open AccessArticle

3D Point Cloud Semantic Augmentation: Instance Segmentation of 360° Panoramas by Deep Learning Techniques

by Ghizlane Karara, Rafika Hajji and Florent Poux

Remote Sens. 2021, 13(18), 3647; https://doi.org/10.3390/rs13183647 - 13 Sep 2021

Cited by 5 | Viewed by 3867

Abstract

Semantic augmentation of 3D point clouds is a challenging problem with numerous real-world applications. While deep learning has revolutionised image segmentation and classification, its impact on point cloud is an active research field. In this paper, we propose an instance segmentation and augmentation of 3D point clouds using deep learning architectures. We show the potential of an indirect approach using 2D images and a Mask R-CNN (Region-Based Convolution Neural Network). Our method consists of four core steps. We first project the point cloud onto panoramic 2D images using three types of projections: spherical, cylindrical, and cubic. Next, we homogenise the resulting images to correct the artefacts and the empty pixels to be comparable to images available in common training libraries. These images are then used as input to the Mask R-CNN neural network, designed for 2D instance segmentation. Finally, the obtained predictions are reprojected to the point cloud to obtain the segmentation results. We link the results to a context-aware neural network to augment the semantics. Several tests were performed on different datasets to test the adequacy of the method and its potential for generalisation. The developed algorithm uses only the attributes X, Y, Z, and a projection centre (virtual camera) position as inputs. Full article

(This article belongs to the Special Issue 3D Indoor Mapping and BIM Reconstruction)

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22 pages, 9402 KiB

Open AccessArticle

RegARD: Symmetry-Based Coarse Registration of Smartphone’s Colorful Point Clouds with CAD Drawings for Low-Cost Digital Twin Buildings

by Yijie Wu, Jianga Shang and Fan Xue

Remote Sens. 2021, 13(10), 1882; https://doi.org/10.3390/rs13101882 - 11 May 2021

Cited by 31 | Viewed by 2924

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 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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