Special Issue "Advanced Research Based on Multi-Dimensional Point Cloud Analysis"

A special issue of ISPRS International Journal of Geo-Information (ISSN 2220-9964).

Deadline for manuscript submissions: closed (31 October 2021).

Special Issue Editors

Dr. Greet Deruyter
E-Mail Website
Guest Editor
Department of Civil engineering, Ghent University, Sint-Pietersnieuwstraat 41 B2, B-9000 Gent, Belgium
Interests: 3D data acquisition; laser scanning; photogrammetry; GIS; risk assessment; flooding; modelling; land surveying
Dr. Ansgar Brunn
E-Mail Website
Guest Editor
Photogrammetry and Remote Sensing, University of Applied Sciences Wuerzburg-Schweinfurt (FHWS), Roentgenring 8, D-97070 Wuerzburg, Germany
Interests: photogrammetry; laser scanning; 3D point clouds; remote sensing; spatial analysis; online education

Special Issue Information

Because of the evolution of data acquisition techniques together with the availability of more powerful computers and software, the use of so-called point clouds for advanced research on the geometry and spectral characteristics of objects and the object space becomes more and more generally accepted. As point clouds are generated by different data acquisition methods, such as terrestrial laser scanning, mobile mapping, multibeam, photogrammetry, etc., the application domain is also diverse.

This Special Issue aims to collect and share innovative research that reflects the diversity of research based on point clouds, starting from the multi-dimensional point clouds for analyses, interpretation, improvement and more. The research domains can be, but are not limited to
  • bathymetry,
  • erosion,
  • static and mobile laser scanning,
  • mobile mapping,
  • photogrammetry,
  • creation of DTM and DSM,
  • classification based on spectral or radiometric characteristics,
  • deformation measurements, and
  • other applications of point clouds.

Prof. Dr. Ing. Greet Deruyter
Prof. Dr. Ing. Ansgar Brunn
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. ISPRS International Journal of Geo-Information is an international peer-reviewed open access monthly 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 1400 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 clouds
  • Bathymetry
  • Laser scanning
  • Photogrammetry
  • Mobile mapping
  • Modelling
  • Spatial analysis
  • Deformation assessment
  • Industry 4.0
  • Autonomous Driving
  • SLAM
  • Agriculture

Published Papers (8 papers)

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Research

Article
Dynamic Intervisibility Analysis of 3D Point Clouds
ISPRS Int. J. Geo-Inf. 2021, 10(11), 782; https://doi.org/10.3390/ijgi10110782 - 17 Nov 2021
Viewed by 307
Abstract
With the popularity of ground and airborne three-dimensional laser scanning hardware and the development of advanced technologies for computer vision in geometrical measurement, intelligent processing of point clouds has become a hot issue in artificial intelligence. The intervisibility analysis in 3D space can [...] Read more.
With the popularity of ground and airborne three-dimensional laser scanning hardware and the development of advanced technologies for computer vision in geometrical measurement, intelligent processing of point clouds has become a hot issue in artificial intelligence. The intervisibility analysis in 3D space can use viewpoint, view distance, and elevation values and consider terrain occlusion to derive the intervisibility between two points. In this study, we first use the 3D point cloud of reflected signals from the intelligent autonomous driving vehicle’s 3D scanner to estimate the field-of-view of multi-dimensional data alignment. Then, the forced metrics of mechanical Riemann geometry are used to construct the Manifold Auxiliary Surface (MAS). With the help of the spectral analysis of the finite element topology structure constructed by the MAS, an innovative dynamic intervisibility calculation is finally realized under the geometric calculation conditions of the Mix-Planes Calculation Structure (MPCS). Different from advanced methods of global and interpolation pathway-based point clouds computing, we have removed the 99.54% high-noise background and reduced the computational complexity by 98.65%. Our computation time can reach an average processing time of 0.1044 s for one frame with a 25 fps acquisition rate of the original vision sensor. The remarkable experimental results and significant evaluations from multiple runs demonstrate that the proposed dynamic intervisibility analysis has high accuracy, strong robustness, and high efficiency. This technology can assist in terrain analysis, military guidance, and dynamic driving path planning, Simultaneous Localization And Mapping (SLAM), communication base station siting, etc., is of great significance in both theoretical technology and market applications. Full article
(This article belongs to the Special Issue Advanced Research Based on Multi-Dimensional Point Cloud Analysis)
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Article
Object Semantic Segmentation in Point Clouds—Comparison of a Deep Learning and a Knowledge-Based Method
ISPRS Int. J. Geo-Inf. 2021, 10(4), 256; https://doi.org/10.3390/ijgi10040256 - 10 Apr 2021
Cited by 3 | Viewed by 1256
Abstract
Through the power of new sensing technologies, we are increasingly digitizing the real world. However, instruments produce unstructured data, mainly in the form of point clouds for 3D data and images for 2D data. Nevertheless, many applications (such as navigation, survey, infrastructure analysis) [...] Read more.
Through the power of new sensing technologies, we are increasingly digitizing the real world. However, instruments produce unstructured data, mainly in the form of point clouds for 3D data and images for 2D data. Nevertheless, many applications (such as navigation, survey, infrastructure analysis) need structured data containing objects and their geometry. Various computer vision approaches have thus been developed to structure the data and identify objects therein. They can be separated into model-driven, data-driven, and knowledge-based approaches. Model-driven approaches mainly use the information on the objects contained in the data and are thus limited to objects and context. Among data-driven approaches, we increasingly find deep learning strategies because of their autonomy in detecting objects. They identify reliable patterns in the data and connect these to the object of interest. Deep learning approaches have to learn these patterns in a training stage. Knowledge-based approaches use characteristic knowledge from different domains allowing the detection and classification of objects. The knowledge must be formalized and substitutes the training for deep learning. Semantic web technologies allow the management of such human knowledge. Deep learning and knowledge-based approaches have already shown good results for semantic segmentation in various examples. The common goal but the different strategies of the two approaches engaged our interest in doing a comparison to get an idea of their strengths and weaknesses. To fill this knowledge gap, we applied two implementations of such approaches to a mobile mapping point cloud. The detected object categories are car, bush, tree, ground, streetlight and building. The deep learning approach uses a convolutional neural network, whereas the knowledge-based approach uses standard semantic web technologies such as SPARQL and OWL2to guide the data processing and the subsequent classification as well. The LiDAR point cloud used was acquired by a mobile mapping system in an urban environment and presents various complex scenes, allowing us to show the advantages and disadvantages of these two types of approaches. The deep learning and knowledge-based approaches produce a semantic segmentation with an average F1 score of 0.66 and 0.78, respectively. Further details are given by analyzing individual object categories allowing us to characterize specific properties of both types of approaches. Full article
(This article belongs to the Special Issue Advanced Research Based on Multi-Dimensional Point Cloud Analysis)
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Article
An Experimental Study of a New Keypoint Matching Algorithm for Automatic Point Cloud Registration
ISPRS Int. J. Geo-Inf. 2021, 10(4), 204; https://doi.org/10.3390/ijgi10040204 - 31 Mar 2021
Cited by 1 | Viewed by 998
Abstract
Light detection and ranging (LiDAR) data systems mounted on a moving or stationary platform provide 3D point cloud data for various purposes. In applications where the interested area or object needs to be measured twice or more with a shift, precise registration of [...] Read more.
Light detection and ranging (LiDAR) data systems mounted on a moving or stationary platform provide 3D point cloud data for various purposes. In applications where the interested area or object needs to be measured twice or more with a shift, precise registration of the obtained point clouds is crucial for generating a healthy model with the combination of the overlapped point clouds. Automatic registration of the point clouds in the common coordinate system using the iterative closest point (ICP) algorithm or its variants is one of the frequently applied methods in the literature, and a number of studies focus on improving the registration process algorithms for achieving better results. This study proposed and tested a different approach for automatic keypoint detecting and matching in coarse registration of the point clouds before fine registration using the ICP algorithm. In the suggested algorithm, the keypoints were matched considering their geometrical relations expressed by means of the angles and distances among them. Hence, contributing the quality improvement of the 3D model obtained through the fine registration process, which is carried out using the ICP method, was our aim. The performance of the new algorithm was assessed using the root mean square error (RMSE) of the 3D transformation in the rough alignment stage as well as a-prior and a-posterior RMSE values of the ICP algorithm. The new algorithm was also compared with the point feature histogram (PFH) descriptor and matching algorithm, accompanying two commonly used detectors. In result of the comparisons, the superiorities and disadvantages of the suggested algorithm were discussed. The measurements for the datasets employed in the experiments were carried out using scanned data of a 6 cm × 6 cm × 10 cm Aristotle sculpture in the laboratory environment, and a building facade in the outdoor as well as using the publically available Stanford bunny sculpture data. In each case study, the proposed algorithm provided satisfying performance with superior accuracy and less iteration number in the ICP process compared to the other coarse registration methods. From the point clouds where coarse registration has been made with the proposed method, the fine registration accuracies in terms of RMSE values with ICP iterations are calculated as ~0.29 cm for Aristotle and Stanford bunny sculptures, ~2.0 cm for the building facade, respectively. Full article
(This article belongs to the Special Issue Advanced Research Based on Multi-Dimensional Point Cloud Analysis)
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Article
3D Change Detection Using Adaptive Thresholds Based on Local Point Cloud Density
ISPRS Int. J. Geo-Inf. 2021, 10(3), 127; https://doi.org/10.3390/ijgi10030127 - 02 Mar 2021
Cited by 1 | Viewed by 685
Abstract
In recent years, because of highly developed LiDAR (Light Detection and Ranging) technologies, there has been increasing demand for 3D change detection in urban monitoring, urban model updating, and disaster assessment. In order to improve the effectiveness of 3D change detection based on [...] Read more.
In recent years, because of highly developed LiDAR (Light Detection and Ranging) technologies, there has been increasing demand for 3D change detection in urban monitoring, urban model updating, and disaster assessment. In order to improve the effectiveness of 3D change detection based on point clouds, an approach for 3D change detection using point-based comparison is presented in this paper. To avoid density variation in point clouds, adaptive thresholds are calculated through the k-neighboring average distance and the local point cloud density. A series of experiments for quantitative evaluation is performed. In the experiments, the influencing factors including threshold, registration error, and neighboring number of 3D change detection are discussed and analyzed. The results of the experiments demonstrate that the approach using adaptive thresholds based on local point cloud density are effective and suitable. Full article
(This article belongs to the Special Issue Advanced Research Based on Multi-Dimensional Point Cloud Analysis)
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Article
Hierarchical Instance Recognition of Individual Roadside Trees in Environmentally Complex Urban Areas from UAV Laser Scanning Point Clouds
ISPRS Int. J. Geo-Inf. 2020, 9(10), 595; https://doi.org/10.3390/ijgi9100595 - 10 Oct 2020
Cited by 2 | Viewed by 1019
Abstract
Individual tree segmentation is essential for many applications in city management and urban ecology. Light Detection and Ranging (LiDAR) system acquires accurate point clouds in a fast and environmentally-friendly manner, which enables single tree detection. However, the large number of object categories and [...] Read more.
Individual tree segmentation is essential for many applications in city management and urban ecology. Light Detection and Ranging (LiDAR) system acquires accurate point clouds in a fast and environmentally-friendly manner, which enables single tree detection. However, the large number of object categories and occlusion from nearby objects in complex environment pose great challenges in urban tree inventory, resulting in omission or commission errors. Therefore, this paper addresses these challenges and increases the accuracy of individual tree segmentation by proposing an automated method for instance recognition urban roadside trees. The proposed algorithm was implemented of unmanned aerial vehicles laser scanning (UAV-LS) data. First, an improved filtering algorithm was developed to identify ground and non-ground points. Second, we extracted tree-like objects via labeling on non-ground points using a deep learning model with a few smaller modifications. Unlike only concentrating on the global features in previous method, the proposed method revises a pointwise semantic learning network to capture both the global and local information at multiple scales, significantly avoiding the information loss in local neighborhoods and reducing useless convolutional computations. Afterwards, the semantic representation is fed into a graph-structured optimization model, which obtains globally optimal classification results by constructing a weighted indirect graph and solving the optimization problem with graph-cuts. The segmented tree points were extracted and consolidated through a series of operations, and they were finally recognized by combining graph embedding learning with a structure-aware loss function and a supervoxel-based normalized cut segmentation method. Experimental results on two public datasets demonstrated that our framework achieved better performance in terms of classification accuracy and recognition ratio of tree. Full article
(This article belongs to the Special Issue Advanced Research Based on Multi-Dimensional Point Cloud Analysis)
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Article
Utilizing Airborne LiDAR and UAV Photogrammetry Techniques in Local Geoid Model Determination and Validation
ISPRS Int. J. Geo-Inf. 2020, 9(9), 528; https://doi.org/10.3390/ijgi9090528 - 02 Sep 2020
Cited by 6 | Viewed by 1165
Abstract
This investigation evaluates the performance of digital terrain models (DTMs) generated in different vertical datums by aerial LiDAR and unmanned aerial vehicle (UAV) photogrammetry techniques, for the determination and validation of local geoid models. Many engineering projects require the point heights referring to [...] Read more.
This investigation evaluates the performance of digital terrain models (DTMs) generated in different vertical datums by aerial LiDAR and unmanned aerial vehicle (UAV) photogrammetry techniques, for the determination and validation of local geoid models. Many engineering projects require the point heights referring to a physical surface, i.e., geoid, rather than an ellipsoid. When a high-accuracy local geoid model is available in the study area, the physical heights are practically obtained with the transformation of global navigation satellite system (GNSS) ellipsoidal heights of the points. Besides the commonly used geodetic methods, this study introduces a novel approach for the determination and validation of the local geoid surface models using photogrammetry. The numeric tests were carried out in the Bergama region, in the west of Turkey. Using direct georeferenced airborne LiDAR and indirect georeferenced UAV photogrammetry-derived point clouds, DTMs were generated in ellipsoidal and geoidal vertical datums, respectively. After this, the local geoid models were calculated as differences between the generated DTMs. Generated local geoid models in the grid and pointwise formats were tested and compared with the regional gravimetric geoid model (TG03) and a high-resolution global geoid model (EIGEN6C4), respectively. In conclusion, the applied approach provided sufficient performance for modeling and validating the geoid heights with centimeter-level accuracy. Full article
(This article belongs to the Special Issue Advanced Research Based on Multi-Dimensional Point Cloud Analysis)
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Article
Nighttime Mobile Laser Scanning and 3D Luminance Measurement: Verifying the Outcome of Roadside Tree Pruning with Mobile Measurement of the Road Environment
ISPRS Int. J. Geo-Inf. 2020, 9(7), 455; https://doi.org/10.3390/ijgi9070455 - 19 Jul 2020
Cited by 2 | Viewed by 1456
Abstract
Roadside vegetation can affect the performance of installed road lighting. We demonstrate a workflow in which a car-mounted measurement system is used to assess the light-obstructing effect of roadside vegetation. The mobile mapping system (MMS) includes a panoramic camera system, laser scanner, inertial [...] Read more.
Roadside vegetation can affect the performance of installed road lighting. We demonstrate a workflow in which a car-mounted measurement system is used to assess the light-obstructing effect of roadside vegetation. The mobile mapping system (MMS) includes a panoramic camera system, laser scanner, inertial measurement unit, and satellite positioning system. The workflow and the measurement system were applied to a road section of Munkkiniemenranta, Helsinki, Finland, in 2015 and 2019. The relative luminance distribution on a road surface and the obstructing vegetation were measured before and after roadside vegetation pruning applying a luminance-calibrated mobile mapping system. The difference between the two measurements is presented, and the opportunities provided by the mobile 3D luminance measurement system are discussed. Full article
(This article belongs to the Special Issue Advanced Research Based on Multi-Dimensional Point Cloud Analysis)
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Article
Sharp Feature Detection as a Useful Tool in Smart Manufacturing
ISPRS Int. J. Geo-Inf. 2020, 9(7), 422; https://doi.org/10.3390/ijgi9070422 - 30 Jun 2020
Viewed by 1096
Abstract
Industry 4.0 comprises a wide spectrum of developmental processes within the management of manufacturing and chain production. Presently, there is a huge effort to automate manufacturing and have automatic control of the production. This intention leads to the increased need for high-quality methods [...] Read more.
Industry 4.0 comprises a wide spectrum of developmental processes within the management of manufacturing and chain production. Presently, there is a huge effort to automate manufacturing and have automatic control of the production. This intention leads to the increased need for high-quality methods for digitization and object reconstruction, especially in the area of reverse engineering. Commonly used scanning software based on well-known algorithms can correctly process smooth objects. Nevertheless, they are usually not applicable for complex-shaped models with sharp features. The number of the points on the edges is extremely limited due to the principle of laser scanning and sometimes also low scanning resolution. Therefore, a correct edge reconstruction problem occurs. The same problem appears in many other laser scanning applications, i.e., in the representation of the buildings from airborne laser scans for 3D city models. We focus on a method for preservation and reconstruction of sharp features. We provide a detailed description of all three key steps: point cloud segmentation, edge detection, and correct B-spline edge representation. The feature detection algorithm is based on the conventional region-growing method and we derive the optimal input value of curvature threshold using logarithmic least square regression. Subsequent edge representation stands on the iterative algorithm of B-spline approximation where we compute the weighted asymmetric error using the golden ratio. The series of examples indicates that our method gives better or comparable results to other methods. Full article
(This article belongs to the Special Issue Advanced Research Based on Multi-Dimensional Point Cloud Analysis)
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