Special Issue "3D Modelling from Point Cloud: 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 March 2021).

Special Issue Editors

Dr. Boris Kargoll
E-Mail Website
Guest Editor
Institute of Geoinformation and Surveying, Department of Architecture, Facility Management and Geoinformation, Anhalt University of Applied Science, Seminarplatz 2a, 06846 Dessau-Rosslau, Germany
Interests: geodetic analysis techniques; adjustment theory; mathematical statistics; parameter estimation; hypothesis testing; geostatistics; engineering mathematics
Dr. Hamza Alkhatib
E-Mail Website
Guest Editor
Geodetic Institute, Faculty of Civil Engineering and Geodesy, Leibniz University Hannover, Nienburger Str. 1, 30167 Hannover, Germany
Interests: geodetic data analysis; filtering in state space; Monte Carlo methods; Bayesian data analysis; deformation analysis; engineering geodesy; laser scanning

Special Issue Information

Point clouds can be obtained by different kinds of laser-, radar-, as well as camera-based techniques, and can serve as a data basis, possibly alongside complementary data, to infer geometrical models of the surveyed objects. This Special Issue focusses on algorithms and methods related to 3D models, defined as mathematical representations of surfaces of objects in three-dimensional Euclidean space. Although the methodology and software for the processing of remotely sensed point clouds has matured considerably throughout the last decade, numerous challenges remain, related, for example, to:

  • Difficult measurement environments;
  • The fusion of heterogeneous data;
  • Large-scale 3D point clouds;
  • Accommodation of outliers;
  • Spatio-temporal correlations;
  • High-accuracy modeling; and
  • Modeling of new or complex kinds of phenomena/objects

We therefore welcome novel algorithms and methods

  • That take special data characteristics such as outliers, data gaps, stochastic properties, correlations, systematic errors, heterogeneity, or multiplicity of the data sources into account.
  • Which utilize approaches from disciplines such as geoinformatics, geoinformation systems, photogrammetry, remote sensing, computer vision, geodesy, applied mathematics, statistics, and artificial intelligence.
  • For surface reconstruction, pattern recognition, image classification and segmentation, crowd sourcing, feature extraction, SAR interferometry, etc.
  • Solve a real-world problem in a scientific application such as urban GIS, 3D city models, cultural heritage documentation, landslide modelling, investigation of land subsidence phenomena, biomass estimation, determination of spatiotemporal patterns in the Earth sciences, building information modelling, classification, change detection, deformation analysis, georeferencing and localization approaches (e.g., simultaneous localization and mapping, SLAM) from point clouds by means of, for example, filtering in state space algorithms.

Dr. Boris Kargoll
Dr. Hamza Alkhatib
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 2500 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
  • 3D modelling
  • algorithm
  • laser scanning
  • radar interferometry
  • georeferencing
  • filtering in state space
  • robust parameter estimation

Published Papers (8 papers)

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Research

Article
Estimating Control Points for B-Spline Surfaces Using Fully Populated Synthetic Variance–Covariance Matrices for TLS Point Clouds
Remote Sens. 2021, 13(16), 3124; https://0-doi-org.brum.beds.ac.uk/10.3390/rs13163124 - 06 Aug 2021
Viewed by 580
Abstract
A flexible approach for geometric modelling of point clouds obtained from Terrestrial Laser Scanning (TLS) is by means of B-splines. These functions have gained some popularity in the engineering geodesy as they provide a suitable basis for a spatially continuous and parametric deformation [...] Read more.
A flexible approach for geometric modelling of point clouds obtained from Terrestrial Laser Scanning (TLS) is by means of B-splines. These functions have gained some popularity in the engineering geodesy as they provide a suitable basis for a spatially continuous and parametric deformation analysis. In the predominant studies on geometric modelling of point clouds by B-splines, uncorrelated and equally weighted measurements are assumed. Trying to overcome this, the elementary errors theory is applied for establishing fully populated covariance matrices of TLS observations that consider correlations in the observed point clouds. In this article, a systematic approach for establishing realistic synthetic variance–covariance matrices (SVCMs) is presented and afterward used to model TLS point clouds by B-splines. Additionally, three criteria are selected to analyze the impact of different SVCMs on the functional and stochastic components of the estimation results. Plausible levels for variances and covariances are obtained using a test specimen of several dm—dimension. It is used to identify the most dominant elementary errors under laboratory conditions. Starting values for the variance level are obtained from a TLS calibration. The impact of SVCMs with different structures and different numeric values are comparatively investigated. Main findings of the paper are that for the analyzed object size and distances, the structure of the covariance matrix does not significantly affect the location of the estimated surface control points, but their precision in terms of the corresponding standard deviations. Regarding the latter, properly setting the main diagonal terms of the SVCM is of superordinate importance compared to setting the off-diagonal ones. The investigation of some individual errors revealed that the influence of their standard deviation on the precision of the estimated parameters is primarily dependent on the scanning distance. When the distance stays the same, one-sided influences on the precision of the estimated control points can be observed with an increase in the standard deviations. Full article
(This article belongs to the Special Issue 3D Modelling from Point Cloud: Algorithms and Methods)
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Article
Regional Ground Movement Detection by Analysis and Modeling PSI Observations
Remote Sens. 2021, 13(12), 2246; https://0-doi-org.brum.beds.ac.uk/10.3390/rs13122246 - 08 Jun 2021
Viewed by 711
Abstract
Any changes to the Earth’s surface should be monitored in order to maintain and update the spatial reference system. To establish a global model of ground movements for a large area, it is important to have consistent and reliable measurements. However, in dealing [...] Read more.
Any changes to the Earth’s surface should be monitored in order to maintain and update the spatial reference system. To establish a global model of ground movements for a large area, it is important to have consistent and reliable measurements. However, in dealing with mass data, outliers may occur and robust analysis of data is indispensable. In particular, this paper will analyse Synthetic Aperture Radar (SAR) data for detecting the regional ground movements (RGM) in the area of Hanover, Germany. The relevant data sets have been provided by the Federal Institute for Geo-sciences and Natural Resources (BGR) for the period of 2014 to 2018. In this paper, we propose a data adoptive outlier detection algorithm to preprocess the observations. The algorithm is tested with different reference data sets and as a binary classifier performs with 0.99 accuracy and obtains a 0.95 F1-score in detecting the outliers. The RGMs that are observed as height velocities are mathematically modeled as a surface based on a hierarchical B-splines (HB-splines) method. For the approximated surface, a 95% confidence interval is estimated based on a bootstrapping approach. In the end, the user is enabled to predict RGM at any point and is provided with a measure of quality for the prediction. Full article
(This article belongs to the Special Issue 3D Modelling from Point Cloud: Algorithms and Methods)
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Article
3D Mesh Pre-Processing Method Based on Feature Point Classification and Anisotropic Vertex Denoising Considering Scene Structure Characteristics
Remote Sens. 2021, 13(11), 2145; https://0-doi-org.brum.beds.ac.uk/10.3390/rs13112145 - 29 May 2021
Viewed by 864
Abstract
3D mesh denoising plays an important role in 3D model pre-processing and repair. A fundamental challenge in the mesh denoising process is to accurately extract features from the noise and to preserve and restore the scene structure features of the model. In this [...] Read more.
3D mesh denoising plays an important role in 3D model pre-processing and repair. A fundamental challenge in the mesh denoising process is to accurately extract features from the noise and to preserve and restore the scene structure features of the model. In this paper, we propose a novel feature-preserving mesh denoising method, which was based on robust guidance normal estimation, accurate feature point extraction and an anisotropic vertex denoising strategy. The methodology of the proposed approach is as follows: (1) The dual weight function that takes into account the angle characteristics is used to estimate the guidance normals of the surface, which improved the reliability of the joint bilateral filtering algorithm and avoids losing the corner structures; (2) The filtered facet normal is used to classify the feature points based on the normal voting tensor (NVT) method, which raised the accuracy and integrity of feature classification for the noisy model; (3) The anisotropic vertex update strategy is used in triangular mesh denoising: updating the non-feature points with isotropic neighborhood normals, which effectively suppressed the sharp edges from being smoothed; updating the feature points based on local geometric constraints, which preserved and restored the features while avoided sharp pseudo features. The detailed quantitative and qualitative analyses conducted on synthetic and real data show that our method can remove the noise of various mesh models and retain or restore the edge and corner features of the model without generating pseudo features. Full article
(This article belongs to the Special Issue 3D Modelling from Point Cloud: Algorithms and Methods)
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Article
Monitoring of the Production Process of Graded Concrete Component Using Terrestrial Laser Scanning
Remote Sens. 2021, 13(9), 1622; https://0-doi-org.brum.beds.ac.uk/10.3390/rs13091622 - 21 Apr 2021
Cited by 1 | Viewed by 686
Abstract
Accepting the ecological necessity of a drastic reduction of resource consumption and greenhouse gas emissions in the building industry, the Institute for Lightweight Structures and Conceptual Design (ILEK) at the University of Stuttgart is developing graded concrete components with integrated concrete hollow spheres. [...] Read more.
Accepting the ecological necessity of a drastic reduction of resource consumption and greenhouse gas emissions in the building industry, the Institute for Lightweight Structures and Conceptual Design (ILEK) at the University of Stuttgart is developing graded concrete components with integrated concrete hollow spheres. These components weigh a fraction of usual conventional components while exhibiting the same performance. Throughout the production process of a component, the positions of the hollow spheres and the level of the fresh concrete have to be monitored with high accuracy and in close to real-time, so that the quality and structural performance of the component can be guaranteed. In this contribution, effective solutions of multiple sphere detection and concrete surface modeling based on the technology of terrestrial laser scanning (TLS) during the casting process are proposed and realized by the Institute of Engineering Geodesy (IIGS). A complete monitoring concept is presented to acquire the point cloud data fast and with high-quality. The data processing method for multiple sphere segmentation based on the efficient combination of region growing and random sample consensus (RANSAC) exhibits great performance on computational efficiency and robustness. The feasibility and reliability of the proposed methods are verified and evaluated by an experiment monitoring the production of an exemplary graded concrete component. Some suggestions to improve the monitoring performance and relevant future work are given as well. Full article
(This article belongs to the Special Issue 3D Modelling from Point Cloud: Algorithms and Methods)
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Article
A Modeling Approach for Predicting the Resolution Capability in Terrestrial Laser Scanning
Remote Sens. 2021, 13(4), 615; https://0-doi-org.brum.beds.ac.uk/10.3390/rs13040615 - 09 Feb 2021
Cited by 4 | Viewed by 1133
Abstract
The minimum size of objects or geometrical features that can be distinguished within a laser scanning point cloud is called the resolution capability (RC). Herein, we develop a simple analytical expression for predicting the RC in angular direction for phase-based laser scanners. We [...] Read more.
The minimum size of objects or geometrical features that can be distinguished within a laser scanning point cloud is called the resolution capability (RC). Herein, we develop a simple analytical expression for predicting the RC in angular direction for phase-based laser scanners. We start from a numerical approximation of the mixed-pixel bias which occurs when the laser beam simultaneously hits surfaces at grossly different distances. In correspondence with previous literature, we view the RC as the minimum angular distance between points on the foreground and points on the background which are not (severely) affected by a mixed-pixel bias. We use an elliptical Gaussian beam for quantifying the effect. We show that the surface reflectivities and the distance step between foreground and background have generally little impact. Subsequently, we derive an approximation of the RC and extend it to include the selected scanning resolution, that is, angular increment. We verify our model by comparison to the resolution capabilities empirically determined by others. Our model requires parameters that can be taken from the data sheet of the scanner or approximated using a simple experiment. We describe this experiment herein and provide the required software on GitHub. Our approach is thus easily accessible, enables the prediction of the resolution capability with little effort and supports assessing the suitability of a specific scanner or of specific scanning parameters for a given application. Full article
(This article belongs to the Special Issue 3D Modelling from Point Cloud: Algorithms and Methods)
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Article
ISAR Image Matching and Three-Dimensional Scattering Imaging Based on Extracted Dominant Scatterers
Remote Sens. 2020, 12(17), 2699; https://0-doi-org.brum.beds.ac.uk/10.3390/rs12172699 - 20 Aug 2020
Cited by 1 | Viewed by 1147
Abstract
This paper studies inverse synthetic aperture radar (ISAR) image matching and three-dimensional (3D) scattering imaging based on extracted dominant scatterers. In the condition of a long baseline between two radars, it is easy for obvious rotation, scale, distortion, and shift to occur between [...] Read more.
This paper studies inverse synthetic aperture radar (ISAR) image matching and three-dimensional (3D) scattering imaging based on extracted dominant scatterers. In the condition of a long baseline between two radars, it is easy for obvious rotation, scale, distortion, and shift to occur between two-dimensional (2D) radar images. These problems lead to the difficulty of radar-image matching, which cannot be resolved by motion compensation and cross-correlation. What is more, due to the anisotropy, existing image-matching algorithms, such as scale invariant feature transform (SIFT), do not adapt to ISAR images very well. In addition, the angle between the target rotation axis and the radar line of sight (LOS) cannot be neglected. If so, the calibration result will be smaller than the real projection size. Furthermore, this angle cannot be estimated by monostatic radar. Therefore, instead of matching image by image, this paper proposes a novel ISAR imaging matching and 3D imaging based on extracted scatterers to deal with these issues. First, taking advantage of ISAR image sparsity, radar images are converted into scattering point sets. Then, a coarse scatterer matching based on the random sampling consistency algorithm (RANSAC) is performed. The scatterer height and accurate affine transformation parameters are estimated iteratively. Based on matched scatterers, information such as the angle and 3D image can be obtained. Finally, experiments based on the electromagnetic simulation software CADFEKO have been conducted to demonstrate the effectiveness of the proposed algorithm. Full article
(This article belongs to the Special Issue 3D Modelling from Point Cloud: Algorithms and Methods)
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Article
Regularization of Building Roof Boundaries from Airborne LiDAR Data Using an Iterative CD-Spline
Remote Sens. 2020, 12(12), 1904; https://0-doi-org.brum.beds.ac.uk/10.3390/rs12121904 - 12 Jun 2020
Cited by 4 | Viewed by 1200
Abstract
Building boundaries play an essential role in many applications such as urban planning and production of 3D realistic views. In this context, airborne LiDAR data have been explored for the generation of digital building models. Despite the many developed strategies, there is no [...] Read more.
Building boundaries play an essential role in many applications such as urban planning and production of 3D realistic views. In this context, airborne LiDAR data have been explored for the generation of digital building models. Despite the many developed strategies, there is no method capable of encompassing all the complexities in an urban environment. In general, the vast majority of existing regularization methods are based on building boundaries that are made up of straight lines. Therefore, the development of a strategy able to model building boundaries, regardless of their degree of complexity is of high importance. To overcome the limitations of existing strategies, an iterative CD-spline (changeable degree spline) regularization method is proposed. The main contribution is the automated selection of the polynomial function that best models each segment of the building roof boundaries. Conducted experiments with real data verified the ability of the proposed approach in modeling boundaries with different levels of complexities, including buildings composed of complex curved segments and point cloud with different densities, presenting Fscore and PoLiS around 95% and 0.30 m, respectively. Full article
(This article belongs to the Special Issue 3D Modelling from Point Cloud: Algorithms and Methods)
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Article
The Local Median Filtering Method for Correcting the Laser Return Intensity Information from Discrete Airborne Laser Scanning Data
Remote Sens. 2020, 12(10), 1681; https://0-doi-org.brum.beds.ac.uk/10.3390/rs12101681 - 24 May 2020
Viewed by 1252
Abstract
Laser return intensity (LRI) information obtained from airborne laser scanning (ALS) data has been used to classify land cover types and to reveal canopy physiological features. However, the sensor-related and environmental parameters may introduce noise. In this study, we developed a local median [...] Read more.
Laser return intensity (LRI) information obtained from airborne laser scanning (ALS) data has been used to classify land cover types and to reveal canopy physiological features. However, the sensor-related and environmental parameters may introduce noise. In this study, we developed a local median filtering (LMF) method to point-by-point correct the LRI information. For each point, we deduced the reference variation range for its LRI. Then, we replaced the outliers of LRI with their local median values. To evaluate the LMF method, we assessed the discrepancy of LRI information from the same and diverse land cover types. Moreover, we used the corrected LRI to distinguish points from grass, road, and bare land, which were classified as ground type in ALS data. The results show that using the LMF method could increase the similarity of pointwise LRI from the same land cover type and the discrepancy of those from different kinds of targets. Using the LMF-corrected LRI could improve the overall classification accuracy of three land cover types by about 3% (all over 81%, κ ≥ 0.73, p < 0.05), compared to those using the original and range-normalized LRI. The sensor-related metrics brought more noise to the original LRI information than the environmental factors. Using the LMF method could effectively correct LRI information from historical ALS datasets. Full article
(This article belongs to the Special Issue 3D Modelling from Point Cloud: Algorithms and Methods)
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