scholarly journals UNLOCKING POINT CLOUD POTENTIAL: FUSING MLS POINT CLOUDS WITH SEMANTIC 3D BUILDING MODELS WHILE CONSIDERING UNCERTAINTY

2021 ◽  
Vol VIII-4/W2-2021 ◽  
pp. 45-52
Author(s):  
O. Wysocki ◽  
Y. Xu ◽  
U. Stilla
Keyword(s):  
Confidence Interval ◽  
Bayesian Network ◽  
Point Cloud ◽  
Laser Scanning ◽  
Point Clouds ◽  
Dynamic Adjustment ◽  
3D City Models ◽  
Building Models ◽  
City Models

Abstract. Throughout the years, semantic 3D city models have been created to depict 3D spatial phenomenon. Recently, an increasing number of mobile laser scanning (MLS) units yield terrestrial point clouds at an unprecedented level. Both dataset types often depict the same 3D spatial phenomenon differently, thus their fusion should increase the quality of the captured 3D spatial phenomenon. Yet, each dataset has modality-dependent uncertainties that hinder their immediate fusion. Therefore, we present a method for fusing MLS point clouds with semantic 3D building models while considering uncertainty issues. Specifically, we show MLS point clouds coregistration with semantic 3D building models based on expert confidence in evaluated metadata quantified by confidence interval (CI). This step leads to the dynamic adjustment of the CI, which is used to delineate matching bounds for both datasets. Both coregistration and matching steps serve as priors for a Bayesian network (BayNet) that performs application-dependent identity estimation. The BayNet propagates uncertainties and beliefs throughout the process to estimate end probabilities for confirmed, unmodeled, and other city objects. We conducted promising preliminary experiments on urban MLS and CityGML datasets. Our strategy sets up a framework for the fusion of MLS point clouds and semantic 3D building models. This framework aids the challenging parallel usage of such datasets in applications such as façade refinement or change detection. To further support this process, we open-sourced our implementation.

scholarly journals ENHANCEMENT OF GENERIC BUILDING MODELS BY RECOGNITION AND ENFORCEMENT OF GEOMETRIC CONSTRAINTS

2016 ◽  
Vol III-3 ◽  
pp. 333-338 ◽  
Author(s):  
J. Meidow ◽  
H. Hammer ◽  
M. Pohl ◽  
D. Bulatov
Keyword(s):  
Laser Scanning ◽  
Initial Point ◽  
Real Data ◽  
Boundary Representation ◽  
Geometric Constraints ◽  
Data Set ◽  
3D City Models ◽  
Building Models ◽  
Recognition And Enforcement ◽  
City Models

Many buildings in 3D city models can be represented by generic models, e.g. boundary representations or polyhedrons, without expressing building-specific knowledge explicitly. Without additional constraints, the bounding faces of these building reconstructions do not feature expected structures such as orthogonality or parallelism. The recognition and enforcement of man-made structures within model instances is one way to enhance 3D city models. Since the reconstructions are derived from uncertain and imprecise data, crisp relations such as orthogonality or parallelism are rarely satisfied exactly. Furthermore, the uncertainty of geometric entities is usually not specified in 3D city models. Therefore, we propose a point sampling which simulates the initial point cloud acquisition by airborne laser scanning and provides estimates for the uncertainties. We present a complete workflow for recognition and enforcement of man-made structures in a given boundary representation. The recognition is performed by hypothesis testing and the enforcement of the detected constraints by a global adjustment of all bounding faces. Since the adjustment changes not only the geometry but also the topology of faces, we obtain improved building models which feature regular structures and a potentially reduced complexity. The feasibility and the usability of the approach are demonstrated with a real data set.

scholarly journals ANALYSIS OF 3D BUILDING MODELS ACCURACY BASED ON THE AIRBORNE LASER SCANNING POINT CLOUDS

2018 ◽  
Vol XLII-2 ◽  
pp. 797-804 ◽  
Author(s):  
W. Ostrowski ◽  
M. Pilarska ◽  
J. Charyton ◽  
K. Bakuła
Keyword(s):  
Point Cloud ◽  
Laser Scanning ◽  
Large Scale ◽  
Point Clouds ◽  
3D Models ◽  
Airborne Laser Scanning ◽  
Aerial Images ◽  
Statistical Parameters ◽  
Airborne Laser ◽  
Building Models

Creating 3D building models in large scale is becoming more popular and finds many applications. Nowadays, a wide term “3D building models” can be applied to several types of products: well-known CityGML solid models (available on few Levels of Detail), which are mainly generated from Airborne Laser Scanning (ALS) data, as well as 3D mesh models that can be created from both nadir and oblique aerial images. City authorities and national mapping agencies are interested in obtaining the 3D building models. Apart from the completeness of the models, the accuracy aspect is also important. Final accuracy of a building model depends on various factors (accuracy of the source data, complexity of the roof shapes, etc.). In this paper the methodology of inspection of dataset containing 3D models is presented. The proposed approach check all building in dataset with comparison to ALS point clouds testing both: accuracy and level of details. Using analysis of statistical parameters for normal heights for reference point cloud and tested planes and segmentation of point cloud provides the tool that can indicate which building and which roof plane in do not fulfill requirement of model accuracy and detail correctness. Proposed method was tested on two datasets: solid and mesh model.

scholarly journals INVESTIGATIONS INTO THE QUALITY OF IMAGE-BASED POINT CLOUDS FROM UAV IMAGERY

2019 ◽  
Vol XLII-2/W13 ◽  
pp. 539-545 ◽  
Author(s):  
H.-J. Przybilla ◽  
M. Lindstaedt ◽  
T. Kersten
Keyword(s):  
Point Cloud ◽  
Laser Scanning ◽  
Image Data ◽  
Point Clouds ◽  
Data Sets ◽  
Test Field ◽  
Data Format ◽  
Software Packages ◽  
Dense Point

<p><strong>Abstract.</strong> The quality of image-based point clouds generated from images of UAV aerial flights is subject to various influencing factors. In addition to the performance of the sensor used (a digital camera), the image data format (e.g. TIF or JPG) is another important quality parameter. At the UAV test field at the former Zollern colliery (Dortmund, Germany), set up by Bochum University of Applied Sciences, a medium-format camera from Phase One (IXU 1000) was used to capture UAV image data in RAW format. This investigation aims at evaluating the influence of the image data format on point clouds generated by a Dense Image Matching process. Furthermore, the effects of different data filters, which are part of the evaluation programs, were considered. The processing was carried out with two software packages from Agisoft and Pix4D on the basis of both generated TIF or JPG data sets. The point clouds generated are the basis for the investigation presented in this contribution. Point cloud comparisons with reference data from terrestrial laser scanning were performed on selected test areas representing object-typical surfaces (with varying surface structures). In addition to these area-based comparisons, selected linear objects (profiles) were evaluated between the different data sets. Furthermore, height point deviations from the dense point clouds were determined using check points. Differences in the results generated through the two software packages used could be detected. The reasons for these differences are filtering settings used for the generation of dense point clouds. It can also be assumed that there are differences in the algorithms for point cloud generation which are implemented in the two software packages. The slightly compressed JPG image data used for the point cloud generation did not show any significant changes in the quality of the examined point clouds compared to the uncompressed TIF data sets.</p>

scholarly journals System analysis of the quality of meshes in HBIM

2018 ◽  
Vol 170 ◽  
pp. 03033 ◽  
Author(s):  
Elizaveta Fateeva ◽  
Vladimir Badenko ◽  
Alexandr Fedotov ◽  
Ivan Kochetkov
Keyword(s):  
Point Cloud ◽  
Laser Scanning ◽  
System Analysis ◽  
Point Clouds ◽  
Rational Solutions ◽  
Historical Building ◽  
Architectural Styles ◽  
Architectural Features ◽  
Building Information

Historical Building Information Modelling (HBIM) is nowadays used as a means to collect, store and preserve information about historical buildings and structures. The information is often collected via laser scanning. The resulting point cloud is manipulated and transformed into a polygon mesh, which is a type of model very easy to work with. This paper looks at the problems associated with creating mesh out of point clouds depending on various characteristics in context of façade reconstruction. The study is based on a point cloud recorded via terrestrial laser scanning in downtown Bremen, Germany that contains buildings completed in a number of different architectural styles, allowing to extract multiple architectural features. Analysis of meshes' quality depending on point cloud density was carried out. Conclusions were drawn as to what the rational solutions for effective surface extraction can be for each individual building in question. Recommendations on preprocessing of point clouds were given.

scholarly journals COMPARISON OF ZEB1 AND LEICA C10 INDOOR LASER SCANNING POINT CLOUDS

2016 ◽  
Vol III-1 ◽  
pp. 143-149
Author(s):  
Beril Sirmacek ◽  
Yueqian Shen ◽  
Roderik Lindenbergh ◽  
Sisi Zlatanova ◽  
Abdoulaye Diakite
Keyword(s):  
Data Acquisition ◽  
Point Cloud ◽  
Laser Scanning ◽  
Laser Scanner ◽  
Point Clouds ◽  
Quality Of Data ◽  
Local Point ◽  
Measurement Planning ◽  
Data Acquisition And Processing

We present a comparison of point cloud generation and quality of data acquired by Zebedee (Zeb1) and Leica C10 devices which are used in the same building interior. Both sensor devices come with different practical and technical advantages. As it could be expected, these advantages come with some drawbacks. Therefore, depending on the requirements of the project, it is important to have a vision about what to expect from different sensors. In this paper, we provide a detailed analysis of the point clouds of the same room interior acquired from Zeb1 and Leica C10 sensors. First, it is visually assessed how different features appear in both the Zeb1 and Leica C10 point clouds. Next, a quantitative analysis is given by comparing local point density, local noise level and stability of local normals. Finally, a simple 3D room plan is extracted from both the Zeb1 and the Leica C10 point clouds and the lengths of constructed line segments connecting corners of the room are compared. The results show that Zeb1 is far superior in ease of data acquisition. No heavy handling, hardly no measurement planning and no point cloud registration is required from the operator. The resulting point cloud has a quality in the order of centimeters, which is fine for generating a 3D interior model of a building. Our results also clearly show that fine details of for example ornaments are invisible in the Zeb1 data. If point clouds with a quality in the order of millimeters are required, still a high-end laser scanner like the Leica C10 is required, in combination with a more sophisticated, time-consuming and elaborative data acquisition and processing approach.

scholarly journals COMPARISON OF ZEB1 AND LEICA C10 INDOOR LASER SCANNING POINT CLOUDS

2016 ◽  
Vol III-1 ◽  
pp. 143-149 ◽  
Author(s):  
Beril Sirmacek ◽  
Yueqian Shen ◽  
Roderik Lindenbergh ◽  
Sisi Zlatanova ◽  
Abdoulaye Diakite
Keyword(s):  
Data Acquisition ◽  
Point Cloud ◽  
Laser Scanning ◽  
Laser Scanner ◽  
Point Clouds ◽  
Quality Of Data ◽  
Local Point ◽  
Measurement Planning ◽  
Data Acquisition And Processing

We present a comparison of point cloud generation and quality of data acquired by Zebedee (Zeb1) and Leica C10 devices which are used in the same building interior. Both sensor devices come with different practical and technical advantages. As it could be expected, these advantages come with some drawbacks. Therefore, depending on the requirements of the project, it is important to have a vision about what to expect from different sensors. In this paper, we provide a detailed analysis of the point clouds of the same room interior acquired from Zeb1 and Leica C10 sensors. First, it is visually assessed how different features appear in both the Zeb1 and Leica C10 point clouds. Next, a quantitative analysis is given by comparing local point density, local noise level and stability of local normals. Finally, a simple 3D room plan is extracted from both the Zeb1 and the Leica C10 point clouds and the lengths of constructed line segments connecting corners of the room are compared. The results show that Zeb1 is far superior in ease of data acquisition. No heavy handling, hardly no measurement planning and no point cloud registration is required from the operator. The resulting point cloud has a quality in the order of centimeters, which is fine for generating a 3D interior model of a building. Our results also clearly show that fine details of for example ornaments are invisible in the Zeb1 data. If point clouds with a quality in the order of millimeters are required, still a high-end laser scanner like the Leica C10 is required, in combination with a more sophisticated, time-consuming and elaborative data acquisition and processing approach.

Structural assessment using terrestrial laser scanning point clouds

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Linh Truong-Hong ◽  
Roderik Lindenbergh ◽  
Thu Anh Nguyen
Keyword(s):  
Point Cloud ◽  
Laser Scanning ◽  
Noisy Data ◽  
Point Clouds ◽  
Reference Surface ◽  
Content Type ◽  
Mixed Pixels ◽  
Data Points ◽  
The Impact

PurposeTerrestrial laser scanning (TLS) point clouds have been widely used in deformation measurement for structures. However, reliability and accuracy of resulting deformation estimation strongly depends on quality of each step of a workflow, which are not fully addressed. This study aims to give insight error of these steps, and results of the study would be guidelines for a practical community to either develop a new workflow or refine an existing one of deformation estimation based on TLS point clouds. Thus, the main contributions of the paper are investigating point cloud registration error affecting resulting deformation estimation, identifying an appropriate segmentation method used to extract data points of a deformed surface, investigating a methodology to determine an un-deformed or a reference surface for estimating deformation, and proposing a methodology to minimize the impact of outlier, noisy data and/or mixed pixels on deformation estimation.Design/methodology/approachIn practice, the quality of data point clouds and of surface extraction strongly impacts on resulting deformation estimation based on laser scanning point clouds, which can cause an incorrect decision on the state of the structure if uncertainty is available. In an effort to have more comprehensive insight into those impacts, this study addresses four issues: data errors due to data registration from multiple scanning stations (Issue 1), methods used to extract point clouds of structure surfaces (Issue 2), selection of the reference surface Sref to measure deformation (Issue 3), and available outlier and/or mixed pixels (Issue 4). This investigation demonstrates through estimating deformation of the bridge abutment, building and an oil storage tank.FindingsThe study shows that both random sample consensus (RANSAC) and region growing–based methods [a cell-based/voxel-based region growing (CRG/VRG)] can be extracted data points of surfaces, but RANSAC is only applicable for a primary primitive surface (e.g. a plane in this study) subjected to a small deformation (case study 2 and 3) and cannot eliminate mixed pixels. On another hand, CRG and VRG impose a suitable method applied for deformed, free-form surfaces. In addition, in practice, a reference surface of a structure is mostly not available. The use of a fitting plane based on a point cloud of a current surface would cause unrealistic and inaccurate deformation because outlier data points and data points of damaged areas affect an accuracy of the fitting plane. This study would recommend the use of a reference surface determined based on a design concept/specification. A smoothing method with a spatial interval can be effectively minimize, negative impact of outlier, noisy data and/or mixed pixels on deformation estimation.Research limitations/implicationsDue to difficulty in logistics, an independent measurement cannot be established to assess the deformation accuracy based on TLS data point cloud in the case studies of this research. However, common laser scanners using the time-of-flight or phase-shift principle provide point clouds with accuracy in the order of 1–6 mm, while the point clouds of triangulation scanners have sub-millimetre accuracy.Practical implicationsThis study aims to give insight error of these steps, and the results of the study would be guidelines for a practical community to either develop a new workflow or refine an existing one of deformation estimation based on TLS point clouds.Social implicationsThe results of this study would provide guidelines for a practical community to either develop a new workflow or refine an existing one of deformation estimation based on TLS point clouds. A low-cost method can be applied for deformation analysis of the structure.Originality/valueAlthough a large amount of the studies used laser scanning to measure structure deformation in the last two decades, the methods mainly applied were to measure change between two states (or epochs) of the structure surface and focused on quantifying deformation-based TLS point clouds. Those studies proved that a laser scanner could be an alternative unit to acquire spatial information for deformation monitoring. However, there are still challenges in establishing an appropriate procedure to collect a high quality of point clouds and develop methods to interpret the point clouds to obtain reliable and accurate deformation, when uncertainty, including data quality and reference information, is available. Therefore, this study demonstrates the impact of data quality in a term of point cloud registration error, selected methods for extracting point clouds of surfaces, identifying reference information, and available outlier, noisy data and/or mixed pixels on deformation estimation.

scholarly journals Spatial ETL for 3D Building Modelling Based on Unmanned Aerial Vehicle Data in Semi-Urban Areas

2020 ◽  
Vol 12 (12) ◽  
pp. 1972 ◽  
Author(s):  
Urška Drešček ◽  
Mojca Kosmatin Fras ◽  
Jernej Tekavec ◽  
Anka Lisec
Keyword(s):  
Unmanned Aerial Vehicle ◽  
Spatial Data ◽  
Urban Areas ◽  
Point Cloud ◽  
Process Model ◽  
Laser Scanning ◽  
3D City Models ◽  
Aerial Vehicle ◽  
Spatial Etl ◽  
City Models

This paper provides the innovative approach of using a spatial extract, transform, load (ETL) solution for 3D building modelling, based on an unmanned aerial vehicle (UAV) photogrammetric point cloud. The main objective of the paper is to present the holistic workflow for 3D building modelling, emphasising the benefits of using spatial ETL solutions for this purpose. Namely, despite the increasing demands for 3D city models and their geospatial applications, the generation of 3D city models is still challenging in the geospatial domain. Advanced geospatial technologies provide various possibilities for the mass acquisition of geospatial data that is further used for 3D city modelling, but there is a huge difference in the cost and quality of input data. While aerial photogrammetry and airborne laser scanning involve high costs, UAV photogrammetry has brought new opportunities, including for small and medium-sized companies, by providing a more flexible and low-cost source of spatial data for 3D modelling. In our data-driven approach, we use a spatial ETL solution to reconstruct a 3D building model from a dense image matching point cloud which was obtained beforehand from UAV imagery. The results are 3D building models in a semantic vector format consistent with the OGC CityGML standard, Level of Detail 2 (LOD2). The approach has been tested on selected buildings in a simple semi-urban area. We conclude that spatial ETL solutions can be efficiently used for 3D building modelling from UAV data, where the data process model developed allows the developer to easily control and manipulate each processing step.

scholarly journals Evaluating the Quality of TLS Point Cloud Colorization

2020 ◽  
Vol 12 (17) ◽  
pp. 2748
Author(s):  
Arttu Julin ◽  
Matti Kurkela ◽  
Toni Rantanen ◽  
Juho-Pekka Virtanen ◽  
Mikko Maksimainen ◽  
...  
Keyword(s):  
Point Cloud ◽  
Laser Scanning ◽  
Production Efficiency ◽  
Point Clouds ◽  
Measuring Instruments ◽  
Efficient Production ◽  
White Balance ◽  
3D Point Clouds ◽  
Imaging Sensors

Terrestrial laser scanning (TLS) enables the efficient production of high-density colored 3D point clouds of real-world environments. An increasing number of applications from visual and automated interpretation to photorealistic 3D visualizations and experiences rely on accurate and reliable color information. However, insufficient attention has been put into evaluating the colorization quality of the 3D point clouds produced applying TLS. We have developed a method for the evaluation of the point cloud colorization quality of TLS systems with integrated imaging sensors. Our method assesses the capability of several tested systems to reproduce colors and details of a scene by measuring objective image quality metrics from 2D images that were rendered from 3D scanned test charts. The results suggest that the detected problems related to color reproduction (i.e., measured differences in color, white balance, and exposure) could be mitigated in data processing while the issues related to detail reproduction (i.e., measured sharpness and noise) are less in the control of a scanner user. Despite being commendable 3D measuring instruments, improving the colorization tools and workflows, and automated image processing pipelines would potentially increase not only the quality and production efficiency but also the applicability of colored 3D point clouds.

scholarly journals QUALITY EVALUATION OF 3D BUILDING MODELS BASED ON LOW-ALTITUDE IMAGERY AND AIRBORNE LASER SCANNING POINT CLOUDS

2021 ◽  
Vol XLIII-B2-2021 ◽  
pp. 345-352
Author(s):  
G. Gabara ◽  
P. Sawicki
Keyword(s):  
Urban Areas ◽  
Point Cloud ◽  
Laser Scanning ◽  
Wide Spectrum ◽  
Vertical Plane ◽  
Point Clouds ◽  
Airborne Laser Scanning ◽  
Airborne Laser ◽  
Building Models ◽  
Low Altitude

Abstract. The term “3D building models” is used in relation to the CityGML models and building information modelling. Reconstruction and modelling of 3D building objects in urban areas becomes a common trend and finds a wide spectrum of utilitarian applications. The paper presents the quality assessment of two multifaceted 3D building models, which were obtained from two open-access databases: Polish national Geoportal (accuracy in LOD 2 standard) and Trimble SketchUp Warehouse (accuracy in LOD 2 standard with information about architectural details of façades). The Geoportal 3D models were primary created based on the airborne laser scanning data (density 12 pts/sq. m, elevation accuracy to 0.10 m) collected during Informatic System for Country Protection against extraordinary hazards project. The testing was performed using different validation low-altitude photogrammetric datasets: RIEGL LMS-Q680i airborne laser scanning point cloud (min. density 25 pts/sq. m and height accuracy 0.03 m), and image-based Phase One iXU-RS 1000 point cloud (average accuracy in the horizontal and in the vertical plane is respectively to 0.015 m and 0.030 m). The visual comparison, heat maps with the function of the signed distance, and histograms in predefined ranges were used to evaluate the quality and accuracy of 3D building models. The aspect of error sources that occurred during the modelling process was also discussed.

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