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Special Issue "3D Modelling from Point Clouds: Algorithms and Methods"

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

Deadline for manuscript submissions: closed (31 July 2018).

Special Issue Editors

Prof. Dr. Pierre Grussenmeyer
E-Mail Website
Guest Editor
Department of Surveying and Civil Engineering, National Institute of Applied Sciences of Strasbourg, 67084 Strasbourg, France
Interests: close-range photogrammetry; architectural photogrammetry & laser scanning; mobile mapping systems and photogrammetric computer systems; integration and accuracy of data in 3D city and building models
Special Issues and Collections in MDPI journals
Prof. Klaus Hanke
E-Mail Website
Guest Editor
Surveying and Geoinformation Unit, University of Innsbruck Technikerstrasse 13, A-6020 Innsbruck, Austria

Special Issue Information

Dear Colleagues,

Image-based and range-based techniques have evolved considerably during the last decade, and are used in numerous fields of applications. The resulting point clouds are interesting for visualization but the processing of 3D models is necessary to structure the data and develop a mathematical representation of the objects. There are several tools currently available providing automatic or semi-automatic methods for 3D modelling from point clouds, but, often, only limited information about the algorithms are given to the users. Authors are invited to submit papers focused on the theory of the algorithms and new methods of 3D modelling based on various sources of point clouds. Papers highlighting the evaluation of the quality of the input data and of the resulting 3D models, as well as the assessment of the algorithms and the performance analysis of the methods are strongly encouraged.

This Remote Sensing Special Issue is meant to support the above-mentioned scope by collecting and publishing research and review papers on related topics. Extended and improved papers from related conferences are also welcome. Papers should be structured in two part sections:

  1. 3D modeling: methods and procedures
  2. Applications

Papers based on commercial software only are not expected for this issue.

We are inviting submission including, but not limited to:

  • Aerial and terrestrial mapping
  • Smart city Modelling
  • Mobile Mapping
  • Indoor and outdoor Mapping
  • Building Information Modelling
  • Underwater Mapping
  • Cultural Heritage
  • Environmental recording
  • Processing of large datasets
  • Merging of various sources of point clouds
Prof. Pierre Grussenmeyer
Prof. Klaus Hanke
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

  • photogrammetry
  • laser scanning
  • sonar
  • point clouds
  • 3D modelling
  • reconstruction
  • algorithms
  • methods
  • accuracy
  • assessment

Published Papers (8 papers)

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Research

Article
3D Point Cloud Semantic Modelling: Integrated Framework for Indoor Spaces and Furniture
Remote Sens. 2018, 10(9), 1412; https://0-doi-org.brum.beds.ac.uk/10.3390/rs10091412 - 05 Sep 2018
Cited by 21 | Viewed by 4657
Abstract
3D models derived from point clouds are useful in various shapes to optimize the trade-off between precision and geometric complexity. They are defined at different granularity levels according to each indoor situation. In this article, we present an integrated 3D semantic reconstruction framework [...] Read more.
3D models derived from point clouds are useful in various shapes to optimize the trade-off between precision and geometric complexity. They are defined at different granularity levels according to each indoor situation. In this article, we present an integrated 3D semantic reconstruction framework that leverages segmented point cloud data and domain ontologies. Our approach follows a part-to-whole conception which models a point cloud in parametric elements usable per instance and aggregated to obtain a global 3D model. We first extract analytic features, object relationships and contextual information to permit better object characterization. Then, we propose a multi-representation modelling mechanism augmented by automatic recognition and fitting from the 3D library ModelNet10 to provide the best candidates for several 3D scans of furniture. Finally, we combine every element to obtain a consistent indoor hybrid 3D model. The method allows a wide range of applications from interior navigation to virtual stores. Full article
(This article belongs to the Special Issue 3D Modelling from Point Clouds: Algorithms and Methods)
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Article
Evaluating a Static Multibeam Sonar Scanner for 3D Surveys in Confined Underwater Environments
Remote Sens. 2018, 10(9), 1395; https://0-doi-org.brum.beds.ac.uk/10.3390/rs10091395 - 01 Sep 2018
Cited by 5 | Viewed by 2010
Abstract
Mechanical Sonar Scanning (MSS) is a recent technology that allows sonar to be used for static measurements in the same way as Terrestrial Laser Scanners (TLS), which makes it an attractive tool for underwater infrastructure surveys. Nevertheless, the metrological capabilities of this type [...] Read more.
Mechanical Sonar Scanning (MSS) is a recent technology that allows sonar to be used for static measurements in the same way as Terrestrial Laser Scanners (TLS), which makes it an attractive tool for underwater infrastructure surveys. Nevertheless, the metrological capabilities of this type of device have been little explored in the literature, particularly in narrow and shallow environments. In this paper, we report on the experimental assessment of a recent MSS, the BlueView BV5000, in a lock. The 3D sonar scans performed with the system suspended from the surface are registered using an innovative algorithm that exploits external measurements from a total station and the symmetry of the structure. We review the different errors that impair sonar data, and compare the resulting point cloud to a TLS model that was acquired the day before, while the lock was completely emptied for maintenance. After correcting a tilt angle calibration error, the maximum difference is less than 10 cm, and the standard deviation is about 3 cm. Visual inspection shows that coarse defects of the masonry, such as stone lacks or cavities, can be detected in the MSS point cloud, while details smaller than 4 cm, e.g., damaged joints, are harder to notice. Full article
(This article belongs to the Special Issue 3D Modelling from Point Clouds: Algorithms and Methods)
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Article
Reconstruction of Three-Dimensional (3D) Indoor Interiors with Multiple Stories via Comprehensive Segmentation
Remote Sens. 2018, 10(8), 1281; https://0-doi-org.brum.beds.ac.uk/10.3390/rs10081281 - 14 Aug 2018
Cited by 19 | Viewed by 2782
Abstract
The fast and stable reconstruction of building interiors from scanned point clouds has recently attracted considerable research interest. However, reconstructing long corridors and connected areas across multiple floors has emerged as a substantial challenge. This paper presents a comprehensive segmentation method for reconstructing [...] Read more.
The fast and stable reconstruction of building interiors from scanned point clouds has recently attracted considerable research interest. However, reconstructing long corridors and connected areas across multiple floors has emerged as a substantial challenge. This paper presents a comprehensive segmentation method for reconstructing a three-dimensional (3D) indoor structure with multiple stories. With this method, the over-segmentation that usually occurs in the reconstruction of long corridors in a complex indoor environment is overcome by morphologically eroding the floor space to segment rooms and by overlapping the segmented room-space with partitioned cells via extracted wall lines. Such segmentation ensures both the integrity of the room-space partitions and the geometric regularity of the rooms. For spaces across floors in a multistory building, a peak-nadir-peak strategy in the distribution of points along the z-axis is proposed in order to extract connected areas across multiple floors. A series of experimental tests while using seven real-world 3D scans and eight synthetic models of indoor environments show the effectiveness and feasibility of the proposed method. Full article
(This article belongs to the Special Issue 3D Modelling from Point Clouds: Algorithms and Methods)
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Article
Automating Parameter Learning for Classifying Terrestrial LiDAR Point Cloud Using 2D Land Cover Maps
Remote Sens. 2018, 10(8), 1192; https://0-doi-org.brum.beds.ac.uk/10.3390/rs10081192 - 30 Jul 2018
Cited by 5 | Viewed by 1610
Abstract
The automating classification of point clouds capturing urban scenes is critical for supporting applications that demand three-dimensional (3D) models. Achieving this goal, however, is met with challenges because of the varying densities of the point clouds and the complexity of the 3D data. [...] Read more.
The automating classification of point clouds capturing urban scenes is critical for supporting applications that demand three-dimensional (3D) models. Achieving this goal, however, is met with challenges because of the varying densities of the point clouds and the complexity of the 3D data. In order to increase the level of automation in the point cloud classification, this study proposes a segment-based parameter learning method that incorporates a two-dimensional (2D) land cover map, in which a strategy of fusing the 2D land cover map and the 3D points is first adopted to create labelled samples, and a formalized procedure is then implemented to automatically learn the following parameters of point cloud classification: the optimal scale of the neighborhood for segmentation, optimal feature set, and the training classifier. It comprises four main steps, namely: (1) point cloud segmentation; (2) sample selection; (3) optimal feature set selection; and (4) point cloud classification. Three datasets containing the point cloud data were used in this study to validate the efficiency of the proposed method. The first two datasets cover two areas of the National University of Singapore (NUS) campus while the third dataset is a widely used benchmark point cloud dataset of Oakland, Pennsylvania. The classification parameters were learned from the first dataset consisting of a terrestrial laser-scanning data and a 2D land cover map, and were subsequently used to classify both of the NUS datasets. The evaluation of the classification results showed overall accuracies of 94.07% and 91.13%, respectively, indicating that the transition of the knowledge learned from one dataset to another was satisfactory. The classification of the Oakland dataset achieved an overall accuracy of 97.08%, which further verified the transferability of the proposed approach. An experiment of the point-based classification was also conducted on the first dataset and the result was compared to that of the segment-based classification. The evaluation revealed that the overall accuracy of the segment-based classification is indeed higher than that of the point-based classification, demonstrating the advantage of the segment-based approaches. Full article
(This article belongs to the Special Issue 3D Modelling from Point Clouds: Algorithms and Methods)
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Article
Towards Reconstructing 3D Buildings from ALS Data Based on Gestalt Laws
Remote Sens. 2018, 10(7), 1127; https://0-doi-org.brum.beds.ac.uk/10.3390/rs10071127 - 17 Jul 2018
Cited by 12 | Viewed by 1947
Abstract
3D building models are an essential data infrastructure for various applications in a smart city system, since they facilitate spatial queries, spatial analysis, and interactive visualization. Due to the highly complex nature of building structures, automatically reconstructing 3D buildings from point clouds remains [...] Read more.
3D building models are an essential data infrastructure for various applications in a smart city system, since they facilitate spatial queries, spatial analysis, and interactive visualization. Due to the highly complex nature of building structures, automatically reconstructing 3D buildings from point clouds remains a challenging task. In this paper, a Roof Attribute Graph (RAG) method is proposed to describe the decomposition and topological relations within a complicated roof structure. Furthermore, top-down decomposition and bottom-up refinement processes are proposed to reconstruct roof parts according to the Gestalt laws, generating a complete structural model with a hierarchical topological tree. Two LiDAR datasets from Guangdong (China) and Vaihingen (Germany) with different point densities were used in our study. Experimental results, including the assessment on Vaihingen standardized by the International Society for Photogrammetry and Remote Sensing (ISPRS), show that the proposed method can be used to model 3D building roofs with high quality results as demonstrated by the completeness and correctness metrics presented in this paper. Full article
(This article belongs to the Special Issue 3D Modelling from Point Clouds: Algorithms and Methods)
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Article
Feature Surface Extraction and Reconstruction from Industrial Components Using Multistep Segmentation and Optimization
Remote Sens. 2018, 10(7), 1073; https://0-doi-org.brum.beds.ac.uk/10.3390/rs10071073 - 05 Jul 2018
Cited by 8 | Viewed by 1887
Abstract
The structure of industrial components is diversified, and extensive efforts have been exerted to improve automation, accuracy, and completeness of feature surfaces extracted from such components. This paper presents a novel method called multistep segmentation and optimization for extracting feature surfaces from industrial [...] Read more.
The structure of industrial components is diversified, and extensive efforts have been exerted to improve automation, accuracy, and completeness of feature surfaces extracted from such components. This paper presents a novel method called multistep segmentation and optimization for extracting feature surfaces from industrial components. The method analyzes the normal vector distribution matrix to segment feature points from a 3D point cloud. The point cloud is then divided into different patches by applying the region growing method on the basis of the distance constraint and according to the initial results. Subsequently, each patch is fitted with an implicit expression equation, and the proposed method is combined with the random sample consensus (RANSAC) algorithm and parameter fitting to extract and optimize the feature surface. The proposed method is experimentally validated on three industrial components. The threshold setting in the algorithm is discussed in terms of algorithm principles and model features. Comparisons with state-of-the-art methods indicate that the proposed method for feature surface extraction is feasible and capable of achieving favorable performance and facilitating automation of industrial components. Full article
(This article belongs to the Special Issue 3D Modelling from Point Clouds: Algorithms and Methods)
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Article
Model Selection for Parametric Surfaces Approximating 3D Point Clouds for Deformation Analysis
Remote Sens. 2018, 10(4), 634; https://0-doi-org.brum.beds.ac.uk/10.3390/rs10040634 - 19 Apr 2018
Cited by 16 | Viewed by 2719
Abstract
Deformation monitoring of structures is a common application and one of the major tasks of engineering surveying. Terrestrial laser scanning (TLS) has become a popular method for detecting deformations due to high precision and spatial resolution in capturing a number of three-dimensional point [...] Read more.
Deformation monitoring of structures is a common application and one of the major tasks of engineering surveying. Terrestrial laser scanning (TLS) has become a popular method for detecting deformations due to high precision and spatial resolution in capturing a number of three-dimensional point clouds. Surface-based methodology plays a prominent role in rigorous deformation analysis. Consequently, it is of great importance to select an appropriate regression model that reflects the geometrical features of each state or epoch. This paper aims at providing the practitioner some guidance in this regard. Different from standard model selection procedures for surface models based on information criteria, we adopted the hypothesis tests from D.R. Cox and Q.H. Vuong to discriminate statistically between parametric models. The methodology was instantiated in two numerical examples by discriminating between widely used polynomial and B-spline surfaces as models of given TLS point clouds. According to the test decisions, the B-spline surface model showed a slight advantage when both surface types had few parameters in the first example, while it performed significantly better for larger numbers of parameters. Within B-spline surface models, the optimal one for the specific segment was fixed by Vuong’s test whose result was quite consistent with the judgment of widely used Bayesian information criterion. The numerical instabilities of B-spline models due to data gap were clearly reflected by the model selection tests, which rejected inadequate B-spline models in another numerical example. Full article
(This article belongs to the Special Issue 3D Modelling from Point Clouds: Algorithms and Methods)
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Article
Unsupervised Clustering Method for Complexity Reduction of Terrestrial Lidar Data in Marshes
Remote Sens. 2018, 10(1), 133; https://0-doi-org.brum.beds.ac.uk/10.3390/rs10010133 - 18 Jan 2018
Cited by 13 | Viewed by 2962
Abstract
Accurate characterization of marsh elevation and landcover evolution is important for coastal management and conservation. This research proposes a novel unsupervised clustering method specifically developed for segmenting dense terrestrial laser scanning (TLS) data in coastal marsh environments. The framework implements unsupervised clustering with [...] Read more.
Accurate characterization of marsh elevation and landcover evolution is important for coastal management and conservation. This research proposes a novel unsupervised clustering method specifically developed for segmenting dense terrestrial laser scanning (TLS) data in coastal marsh environments. The framework implements unsupervised clustering with the well-known K-means algorithm by applying an optimization to determine the “k” clusters. The fundamental idea behind this novel framework is the application of multi-scale voxel representation of 3D space to create a set of features that characterizes the local complexity and geometry of the terrain. A combination of point- and voxel-generated features are utilized to segment 3D point clouds into homogenous groups in order to study surface changes and vegetation cover. Results suggest that the combination of point and voxel features represent the dataset well. The developed method compresses millions of 3D points representing the complex marsh environment into eight distinct clusters representing different landcover: tidal flat, mangrove, low marsh to high marsh, upland, and power lines. A quantitative assessment of the automated delineation of the tidal flat areas shows acceptable results considering the proposed method is unsupervised with no training data. Clustering results based on K-means are also compared to results based on the Self Organizing Map (SOM) clustering algorithm. Results demonstrate that the developed multi-scale voxelization approach and representative feature set are transferrable to other clustering algorithms, thereby providing an unsupervised framework for intelligent scene segmentation of TLS point cloud data in marshes. Full article
(This article belongs to the Special Issue 3D Modelling from Point Clouds: Algorithms and Methods)
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