scholarly journals Strategies for 3D Modelling of Buildings from Airborne Laser Scanner and Photogrammetric Data Based on Free-Form and Model-Driven Methods: The Case Study of the Old Town Centre of Bordeaux (France)

2021 ◽  
Vol 11 (22) ◽  
pp. 10993
Author(s):  
Domenica Costantino ◽  
Gabriele Vozza ◽  
Vincenzo Saverio Alfio ◽  
Massimiliano Pepe
Keyword(s):  
Point Cloud ◽  
Laser Scanner ◽  
Point Clouds ◽  
3D Models ◽  
Airborne Lidar ◽  
Free Form ◽  
Surface Model ◽  
Parametric Modelling ◽  
Model Driven ◽  
Town Centre

This paper presents a data-driven free-form modelling method dedicated to the parametric modelling of buildings with complex shapes located in particularly valuable Old Town Centres, using Airborne LiDAR Scanning (ALS) data and aerial imagery. The method aims to reconstruct and preserve the input point cloud based on the relative density of the data. The method is based on geometric operations, iterative transformations between point clouds, meshes, and shape identification. The method was applied on a few buildings located in the Old Town Centre of Bordeaux (France). The 3D model produced shows a mean distance to the point cloud of 0.058 m and a standard deviation of 0.664 m. In addition, the incidence of building footprint segmentation techniques in automatic and interactive model-driven modelling was investigated and, in order to identify the best approach, six different segmentation methods were tested. The segmentation was performed based on the footprints derived from Digital Surface Model (DSM), point cloud, nadir images, and OpenStreetMap (OSM). The comparison between the models shows that the segmentation that produces the most accurate and precise model is the interactive segmentation based on nadir images. This research also shows that in modelling complex structures, the model-driven method can achieve high levels of accuracy by including an interactive editing phase in building 3D models.

scholarly journals COMPARISON ASSESSMENT OF DIGITAL 3D MODELS OBTAINED BY DRONE-BASED LIDAR AND DRONE IMAGERY

2021 ◽  
Vol XLVI-4/W5-2021 ◽  
pp. 113-118
Author(s):  
M. Bouziani ◽  
M. Amraoui ◽  
S. Kellouch
Keyword(s):  
Laser Scanner ◽  
Digital Terrain Model ◽  
Point Clouds ◽  
3D Models ◽  
Pulse Frequency ◽  
Airborne Lidar ◽  
Measurement Unit ◽  
Surface Model ◽  
Digital Products

Abstract. The purpose of this study is to assess the potential of drone airborne LiDAR technology in Morocco in comparison with drone photogrammetry. The cost and complexity of the equipment which includes a laser scanner, an inertial measurement unit, a positioning system and a platform are among the causes limiting its use. Furthermore, this study was motivated by the following reasons: (1) Limited number of studies in Morocco on drone-based LiDAR technology applications, (2) Lack of study on the parameters that influence the quality of drone-based LiDAR surveys as well as on the evaluation of the accuracy of derived products. In this study, the evaluation of LiDAR technology was carried out by an analysis of the geometric accuracy of the 3D products generated: Digital Terrain Model (DTM), Digital Surface Model (DSM) and Digital Canopy Model (DCM). We conduct a comparison with the products generated by drone photogrammetry and GNSS surveys. Several tests were carried out to analyse the parameters that influence the mission results namely height, overlap, drone speed and laser pulse frequency. After data collection, the processing phase was carried out. It includes: the cleaning, the consolidation then the classification of point clouds and the generation of the various digital models. This project also made it possible to propose and validate a workflow for the processing, the classification of point clouds and the generation of 3D digital products derived from the processing of LiDAR data acquired by drone.

scholarly journals POTENTIAL OF FULL WAVEFORM AIRBORNE LASER SCANNING DATA FOR URBAN AREA CLASSIFICATION – TRANSFER OF CLASSIFICATION APPROACHES BETWEEN MISSIONS

2015 ◽  
Vol XL-7/W3 ◽  
pp. 1317-1323 ◽  
Author(s):  
G. Tran ◽  
D. Nguyen ◽  
M. Milenkovic ◽  
N. Pfeifer
Keyword(s):  
Point Cloud ◽  
Laser Scanning ◽  
Laser Scanner ◽  
Point Clouds ◽  
Surface Model ◽  
Knowledge Based ◽  
Full Waveform ◽  
Airborne Laser ◽  
Point Density ◽  
Area Classification

Full-waveform (FWF) LiDAR (Light Detection and Ranging) systems have their advantage in recording the entire backscattered signal of each emitted laser pulse compared to conventional airborne discrete-return laser scanner systems. The FWF systems can provide point clouds which contain extra attributes like amplitude and echo width, etc. In this study, a FWF data collected in 2010 for Eisenstadt, a city in the eastern part of Austria was used to classify four main classes: buildings, trees, waterbody and ground by employing a decision tree. Point density, echo ratio, echo width, normalised digital surface model and point cloud roughness are the main inputs for classification. The accuracy of the final results, correctness and completeness measures, were assessed by comparison of the classified output to a knowledge-based labelling of the points. Completeness and correctness between 90% and 97% was reached, depending on the class. While such results and methods were presented before, we are investigating additionally the transferability of the classification method (features, thresholds …) to another urban FWF lidar point cloud. Our conclusions are that from the features used, only echo width requires new thresholds. A data-driven adaptation of thresholds is suggested.

scholarly journals VAULT MODELING WITH NEURAL NETWORKS

2019 ◽  
Vol XLII-2/W9 ◽  
pp. 81-86
Author(s):  
L. Barazzetti ◽  
M. Previtali
Keyword(s):  
Neural Networks ◽  
Point Cloud ◽  
Laser Scanning ◽  
Point Clouds ◽  
3D Models ◽  
Information Modeling ◽  
Free Form ◽  
Mathematical Representation ◽  
Mathematical Functions ◽  
Digital Reconstruction

<p><strong>Abstract.</strong> Nowadays, the digital reconstruction of vaults is carried out using photogrammetric and laser scanning techniques able to capture the visible surface with dense point clouds. Then, different modeling strategies allow the generation of 3D models in various formats, such as meshes that interpolates the acquired point cloud, NURBS-based reconstructions based on manual, semi-automated, or automated procedures, and parametric objects for Building Information Modeling. This paper proposes a novel method that reconstructs the visible surface of a vault using neural networks. It is based on the assumption that vaults are not irregular free-form objects, but they can be reconstructed by mathematical functions calculated from the acquired point clouds. The proposed approach uses the point cloud to train a neural network that approximates vault surface. The achieved solution is not only able to consider the basic geometry of the vault, but also its irregularities that cannot be neglected in the case of accurate and detailed modeling projects of historical vaults. Considerations on the approximation capabilities of neural networks are illustrated and discussed along with the advantages of creating a mathematical representation encapsulated into a function.</p>

scholarly journals Parametric Point Cloud Slicing for Facade Retrofitting

2021 ◽  
Author(s):  
Oscar Gámez Bohórquez ◽  
William Derigent ◽  
Hind Bril El Haouzi
Keyword(s):  
Measurement Errors ◽  
Point Cloud ◽  
Three Dimensional ◽  
Point Clouds ◽  
3D Models ◽  
Building Envelope ◽  
Parametric Modelling ◽  
Building Stock ◽  
File Formats ◽  
Modelling Environment

Current commitments by European governments seek to improve energy consumption as a means to reduce carbon emissions from building stock by 2050. Within such context, retrieving reliable three-dimensional contours from point clouds becomes an important step in developing facade retrofitting solutions since facade retrofitting projects often make use of as-built 3D models to help reduce inaccuracies by narrowing interpretation and measurement errors. This work aims to provide a method that uses topology-based parametric modelling for reconstructing building envelopes from point clouds. Through a semi-automated process that gives permanent visual feedback, the user adjusts parameters to custom standards of acceptability. A solution under the form of a Grasshopper definition delivers building envelope 3D contours in various file formats as a means for increasing interoperability. The main contributions of this work consist of a parametric reconstruction workflow capable of solving building topology for retrieving 3D contours, a strategy to bypass point cloud occlusion, and a strategy for converting those contours into an IFC model directly from the parametric modelling environment.

scholarly journals COMPARISON OF UAV IMAGERY-DERIVED POINT CLOUD TO TERRESTRIAL LASER SCANNER POINT CLOUD

2019 ◽  
Vol IV-2/W5 ◽  
pp. 149-155 ◽  
Author(s):  
S. Peterson ◽  
J. Lopez ◽  
R. Munjy
Keyword(s):  
Point Cloud ◽  
Laser Scanning ◽  
Focal Length ◽  
Laser Scanner ◽  
Digital Camera ◽  
Point Clouds ◽  
Surface Model ◽  
Control Points ◽  
Ground Control Points ◽  
Aerial Vehicle

<p><strong>Abstract.</strong> A small unmanned aerial vehicle (UAV) with survey-grade GNSS positioning is used to produce a point cloud for topographic mapping and 3D reconstruction. The objective of this study is to assess the accuracy of a UAV imagery-derived point cloud by comparing a point cloud generated by terrestrial laser scanning (TLS). Imagery was collected over a 320&amp;thinsp;m by 320&amp;thinsp;m area with undulating terrain, containing 80 ground control points. A SenseFly eBee Plus fixed-wing platform with PPK positioning with a 10.6&amp;thinsp;mm focal length and a 20&amp;thinsp;MP digital camera was used to fly the area. Pix4Dmapper, a computer vision based commercial software, was used to process a photogrammetric block, constrained by 5 GCPs while obtaining cm-level RMSE based on the remaining 75 checkpoints. Based on results of automatic aerial triangulation, a point cloud and digital surface model (DSM) (2.5&amp;thinsp;cm/pixel) are generated and their accuracy assessed. A bias less than 1 pixel was observed in elevations from the UAV DSM at the checkpoints. 31 registered TLS scans made up a point cloud of the same area with an observed horizontal root mean square error (RMSE) of 0.006m, and negligible vertical RMSE. Comparisons were made between fitted planes of extracted roof features of 2 buildings and centreline profile comparison of a road in both UAV and TLS point clouds. Comparisons showed an average +8&amp;thinsp;cm bias with UAV point cloud computing too high in two features. No bias was observed in the roof features of the southernmost building.</p>

scholarly journals IMAGE-BASED AIRBORNE LiDAR POINT CLOUD ENCODING FOR 3D BUILDING MODEL RETRIEVAL

2016 ◽  
Vol XLI-B8 ◽  
pp. 1237-1242 ◽  
Author(s):  
Yi-Chen Chen ◽  
Chao-Hung Lin
Keyword(s):  
Point Cloud ◽  
Retrieval System ◽  
Data Retrieval ◽  
Point Clouds ◽  
3D Models ◽  
Airborne Lidar ◽  
Building Roof ◽  
Depth Images ◽  
Building Models ◽  
Model Retrieval

With the development of Web 2.0 and cyber city modeling, an increasing number of 3D models have been available on web-based model-sharing platforms with many applications such as navigation, urban planning, and virtual reality. Based on the concept of data reuse, a 3D model retrieval system is proposed to retrieve building models similar to a user-specified query. The basic idea behind this system is to reuse these existing 3D building models instead of reconstruction from point clouds. To efficiently retrieve models, the models in databases are compactly encoded by using a shape descriptor generally. However, most of the geometric descriptors in related works are applied to polygonal models. In this study, the input query of the model retrieval system is a point cloud acquired by Light Detection and Ranging (LiDAR) systems because of the efficient scene scanning and spatial information collection. Using Point clouds with sparse, noisy, and incomplete sampling as input queries is more difficult than that by using 3D models. Because that the building roof is more informative than other parts in the airborne LiDAR point cloud, an image-based approach is proposed to encode both point clouds from input queries and 3D models in databases. The main goal of data encoding is that the models in the database and input point clouds can be consistently encoded. Firstly, top-view depth images of buildings are generated to represent the geometry surface of a building roof. Secondly, geometric features are extracted from depth images based on height, edge and plane of building. Finally, descriptors can be extracted by spatial histograms and used in 3D model retrieval system. For data retrieval, the models are retrieved by matching the encoding coefficients of point clouds and building models. In experiments, a database including about 900,000 3D models collected from the Internet is used for evaluation of data retrieval. The results of the proposed method show a clear superiority over related methods.

scholarly journals IMPROVED TOPOGRAPHIC MODELS VIA CONCURRENT AIRBORNE LIDAR AND DENSE IMAGE MATCHING

2017 ◽  
Vol IV-2/W4 ◽  
pp. 259-266 ◽  
Author(s):  
G. Mandlburger ◽  
K. Wenzel ◽  
A. Spitzer ◽  
N. Haala ◽  
P. Glira ◽  
...  
Keyword(s):  
Spatial Resolution ◽  
Image Matching ◽  
Point Cloud ◽  
Laser Scanning ◽  
Point Clouds ◽  
Airborne Lidar ◽  
Surface Model ◽  
3D Point Clouds ◽  
Dense Image ◽  
Surface Models

Modern airborne sensors integrate laser scanners and digital cameras for capturing topographic data at high spatial resolution. The capability of penetrating vegetation through small openings in the foliage and the high ranging precision in the cm range have made airborne LiDAR the prime terrain acquisition technique. In the recent years dense image matching evolved rapidly and outperforms laser scanning meanwhile in terms of the achievable spatial resolution of the derived surface models. In our contribution we analyze the inherent properties and review the typical processing chains of both acquisition techniques. In addition, we present potential synergies of jointly processing image and laser data with emphasis on sensor orientation and point cloud fusion for digital surface model derivation. Test data were concurrently acquired with the <i>RIEGL</i> LMS-Q1560 sensor over the city of Melk, Austria, in January 2016 and served as basis for testing innovative processing strategies. We demonstrate that (i) systematic effects in the resulting scanned and matched 3D point clouds can be minimized based on a hybrid orientation procedure, (ii) systematic differences of the individual point clouds are observable at penetrable, vegetated surfaces due to the different measurement principles, and (iii) improved digital surface models can be derived combining the higher density of the matching point cloud and the higher reliability of LiDAR point clouds, especially in the narrow alleys and courtyards of the study site, a medieval city.

scholarly journals 3D Surface Reconstruction of Stone Tools by Using Four-Directional Measurement Machine

2011 ◽  
Vol 10 (1) ◽  
pp. 37-43 ◽  
Author(s):  
Enkhbayar Altantsetseg ◽  
Yuta Muraki ◽  
Fumito Chiba ◽  
Kouichi Konno
Keyword(s):  
Data Acquisition ◽  
Surface Reconstruction ◽  
Point Cloud ◽  
Laser Scanner ◽  
Point Clouds ◽  
3D Models ◽  
Stone Tools ◽  
Hole Filling ◽  
Sharp Edges ◽  
Slicing Method

In this paper, we present a whole procedure for constructing 3D models of stone tools including scanning, data acquisition and surface reconstruction with hole-filling. The process of scanning hundreds or thousands of small objects is time consuming. Our original 3D laser scanner optimizes the scanning process and reduces time significantly by four directional scanning of many small objects simultaneously. To reconstruct surface of stone tools, the scanned point clouds are processed with a new triangulation method that preserves the properties of sharp edges. Our approach is based on a projection based method in which points are distinguished into neighboring layers with a point cloud slicing method to be individually reconstructed. In addition, we introduce a simple hole-filling algorithm for mesh completion of models. The main advantages of our approach are speed and efficiency for reconstruction of many small objects.

Integration of point clouds from UAV photogrammetry and laserscan survey for the assessment of the risk of collapse of the vault of an underground cavity

2020 ◽  
Author(s):  
Davide Martinucci ◽  
Simone Pillon ◽  
Annelore Bezzi ◽  
Giulia Casagrande ◽  
Giorgio Fontolan ◽  
...  
Keyword(s):  
High Resolution ◽  
Point Cloud ◽  
Control Point ◽  
Laser Scanner ◽  
Point Clouds ◽  
Integrated Approach ◽  
Surface Model ◽  
North East ◽  
Critical Issues ◽  
Gps Rtk

&lt;p&gt;Photogrammetric surveys from UAV and LiDAR surveys are two techniques that allow for the production of very high resolution point clouds. The use of these techniques result in a detailed reconstruction of difficult-to-access environments such as underground cavities. A rigorous georeferencing of the acquired data allows for a comparison of the hypogean development of the cave to the overlying territory. This study presents a case of integration between these two techniques, applied to the risk assessment of the collapse of the vaults in a natural cavity in the Trieste Karst (north east Italy). This site is particularly delicate given that on the slope above the cave there is an abandoned stone quarry. In order to survey the quarry above the cave, a flight was performed with UAV, while the cave was surveyed with Laser Scan from the ground. The flight was made using a UAV DJI Phantom RTK, which carried a 20 Mpixel 1&amp;#8220; sensor camera. 8 ha of terrain was surveyed, capturing about 733 high resolution images and surveying 22 GCPs (Ground Control Point) with a GNSS RTK receiver. It was possible to reduce the number of GCPs, since the drone recorded the shooting positions very accurately with the on-board GPS RTK. Data were analyzed using Agisoft Metashape Professional to produce an orthophoto and a DSM (Digital Surface Model) with a ground resolution of 0.02 m and 0.04 m respectively. The point cloud has a density of 586 points/m&lt;sup&gt;2&lt;/sup&gt;. The LiDaR survey was carried out using an ILRIS 3D ER laser scanner from Optec. The point cloud has a density of approximately 2500 points/m&lt;sup&gt;2&lt;/sup&gt; and 5 stations were needed to cover the underground development of the cavity. The georeferencing of the data was carried out by roto-translation on geo-referenced benchmarks, surveyed with GPS RTK and total station. The point cloud was processed using Terrascan software (Terrasolid). The two point clouds were aligned, geo-referenced and combined using Polyworks software (Innovmetric), in order to check the thicknesses of the material present above the vault of the cave. The integration of epigean and hypogean data made it possible to identify some critical points related to a vault thickness of approximately 1.5 meters, located at the quarry square. This work made it possible to highlight critical issues difficult to detect without the integrated approach of these different survey methodologies.&lt;/p&gt;

scholarly journals IMAGE-BASED AIRBORNE LiDAR POINT CLOUD ENCODING FOR 3D BUILDING MODEL RETRIEVAL

2016 ◽  
Vol XLI-B8 ◽  
pp. 1237-1242 ◽  
Author(s):  
Yi-Chen Chen ◽  
Chao-Hung Lin
Keyword(s):  
Point Cloud ◽  
Retrieval System ◽  
Data Retrieval ◽  
Point Clouds ◽  
3D Models ◽  
Airborne Lidar ◽  
Building Roof ◽  
Depth Images ◽  
Building Models ◽  
Model Retrieval

With the development of Web 2.0 and cyber city modeling, an increasing number of 3D models have been available on web-based model-sharing platforms with many applications such as navigation, urban planning, and virtual reality. Based on the concept of data reuse, a 3D model retrieval system is proposed to retrieve building models similar to a user-specified query. The basic idea behind this system is to reuse these existing 3D building models instead of reconstruction from point clouds. To efficiently retrieve models, the models in databases are compactly encoded by using a shape descriptor generally. However, most of the geometric descriptors in related works are applied to polygonal models. In this study, the input query of the model retrieval system is a point cloud acquired by Light Detection and Ranging (LiDAR) systems because of the efficient scene scanning and spatial information collection. Using Point clouds with sparse, noisy, and incomplete sampling as input queries is more difficult than that by using 3D models. Because that the building roof is more informative than other parts in the airborne LiDAR point cloud, an image-based approach is proposed to encode both point clouds from input queries and 3D models in databases. The main goal of data encoding is that the models in the database and input point clouds can be consistently encoded. Firstly, top-view depth images of buildings are generated to represent the geometry surface of a building roof. Secondly, geometric features are extracted from depth images based on height, edge and plane of building. Finally, descriptors can be extracted by spatial histograms and used in 3D model retrieval system. For data retrieval, the models are retrieved by matching the encoding coefficients of point clouds and building models. In experiments, a database including about 900,000 3D models collected from the Internet is used for evaluation of data retrieval. The results of the proposed method show a clear superiority over related methods.

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