scholarly journals Investigation on Roof Segmentation for 3D Building Reconstruction from Aerial LIDAR Point Clouds

2019 ◽  
Vol 9 (21) ◽  
pp. 4674 ◽  
Author(s):  
Raffaele Albano
Keyword(s):  
Region Growing ◽  
Point Clouds ◽  
Operating Conditions ◽  
Information Modeling ◽  
World Heritage Site ◽  
Computational Time ◽  
Building Modeling ◽  
Segmentation Approach ◽  
Region Growing Segmentation ◽  
Building Reconstruction

Three-dimensional (3D) reconstruction techniques are increasingly used to obtain 3D representations of buildings due to the broad range of applications for 3D city models related to sustainability, efficiency and resilience (i.e., energy demand estimation, estimation of the propagation of noise in an urban environment, routing and accessibility, flood or seismic damage assessment). With advancements in airborne laser scanning (ALS), 3D modeling of urban topography has increased its potential to automatize extraction of the characteristics of individual buildings. In 3D building modeling from light detection and ranging (LIDAR) point clouds, one major challenging issue is how to efficiently and accurately segment building regions and extract rooftop features. This study aims to present an investigation and critical comparison of two different fully automatic roof segmentation approaches for 3D building reconstruction. In particular, the paper presents and compares a cluster-based roof segmentation approach that uses (a) a fuzzy c-means clustering method refined through a density clustering and connectivity analysis, and (b) a region growing segmentation approach combined with random sample consensus (RANSAC) method. In addition, a robust 2.5D dual contouring method is utilized to deliver watertight 3D building modeling from the results of each proposed segmentation approach. The benchmark LIDAR point clouds and related reference data (generated by stereo plotting) of 58 buildings over downtown Toronto (Canada), made available to the scientific community by the International Society for Photogrammetry and Remote Sensing (ISPRS), have been used to evaluate the quality of the two proposed segmentation approaches by analysing the geometrical accuracy of the roof polygons. Moreover, the results of both approaches have been evaluated under different operating conditions against the real measurements (based on archive documentation and celerimetric surveys realized by a total station system) of a complex building located in the historical center of Matera (UNESCO world heritage site in southern Italy) that has been manually reconstructed in 3D via traditional Building Information Modeling (BIM) technique. The results demonstrate that both methods reach good performance metrics in terms of geometry accuracy. However, approach (b), based on region growing segmentation, exhibited slightly better performance but required greater computational time than the clustering-based approach.

scholarly journals A Research and Application of the Processing Methods of Railway Measurement Based on Self-Mobile Scanning Measurement System

2020 ◽  
Author(s):  
Jiancai Zhang ◽  
Hang Mu ◽  
Feng Han ◽  
Shumin Han
Keyword(s):  
Measurement System ◽  
High Speed ◽  
Automatic Segmentation ◽  
Laser Scanner ◽  
Region Growing ◽  
Image Sensor ◽  
Operating Conditions ◽  
Secondary Development ◽  
Railway Line ◽  
Region Growing Segmentation

With the gradual improvement of China’s railway net, the opening of international railways as well as the continuous growth of railway operating mileage, the workload of remeasuring railways is increasing. The traditional methods of remeasuring railways can not meet current high-speed and high-density operating conditions anymore in terms of safety, efficiency and quality, so a safer and more efficient measurement method is urgently needed.This thesis integrated various sensors on a self-mobile instrument, such as 3D laser scanner, digital image sensor and GNSS_IMU, designing a set of intelligent and integrated self-mobile scanning measurement system. This thesis proposed region growing segmentation based on the reflection intensity of point cloud. Through the secondary development of CAD, the menu for automatic processing of self-mobile scanning measurement system is designed to realize rail automatic segmentation, extraction of rail top points, fitting of plane parameters of railway line, calculation of curve elements and mileage management.The results show that self-mobile scanning measurement system overcomes the shortcomings of traditional railway measurement to some extent, and realizes intelligent measurement of railways.

scholarly journals Clustering of Wall Geometry from Unstructured Point Clouds Using Conditional Random Fields

2019 ◽  
Vol 11 (13) ◽  
pp. 1586 ◽  
Author(s):  
Maarten Bassier ◽  
Maarten Vergauwen
Keyword(s):  
Conditional Random Fields ◽  
Conditional Random Field ◽  
Region Growing ◽  
Point Clouds ◽  
Information Modeling ◽  
Ongoing Research ◽  
Cloud Data ◽  
Building Information ◽  
Wall Geometry ◽  
Balanced Clustering

The automated reconstruction of Building Information Modeling (BIM) objects from point cloud data is still subject of ongoing research. A vital step in the process is identifying the observations for each wall object. Given a set of segmented and classified point clouds, the labeled segments should be clustered according to their respective objects. The current processes to perform this task are sensitive to noise, occlusions, and the associativity between faces of neighboring objects. The proper retrieval of the observed geometry is especially important for wall geometry as it forms the basis for further BIM reconstruction. In this work, a method is presented to automatically group wall segments derived from point clouds according to the proper walls of a building. More specifically, a Conditional Random Field is employed that evaluates the context of each wall segment in order to determine which wall it belongs to. First, a set of classified planar primitives is obtained through algorithms developed in prior work. Next, both local and contextual features are extracted based on the nearest neighbors and a number of seeds that are heuristically determined. The final wall clusters are then computed by decoding the graph. The method is tested on our own data as well as the 2D-3D-Semantics (2D-3D-S) benchmark data of Stanford. Compared to a conventional region growing method, the proposed method reduces the rate of false positives, resulting in better wall clusters. Overall, the method computes a more balanced clustering of the observations. A key advantage of the proposed method is its capability to deal with wall geometry in complex configurations in multi-storey buildings opposed to the presented methods in current literature.

scholarly journals Leaf Segmentation on Dense Plant Point Clouds with Facet Region Growing

Sensors ◽  
2018 ◽  
Vol 18 (11) ◽  
pp. 3625 ◽  
Author(s):  
Dawei Li ◽  
Yan Cao ◽  
Xue-song Tang ◽  
Siyuan Yan ◽  
Xin Cai
Keyword(s):  
Point Cloud ◽  
Active Material ◽  
Region Growing ◽  
Point Clouds ◽  
Phenotypic Traits ◽  
Individual Leaf ◽  
3D Point Clouds ◽  
Segmentation Approach ◽  
Monstera Deliciosa ◽  
Leaf Segmentation

Leaves account for the largest proportion of all organ areas for most kinds of plants, and are comprise the main part of the photosynthetically active material in a plant. Observation of individual leaves can help to recognize their growth status and measure complex phenotypic traits. Current image-based leaf segmentation methods have problems due to highly restricted species and vulnerability toward canopy occlusion. In this work, we propose an individual leaf segmentation approach for dense plant point clouds using facet over-segmentation and facet region growing. The approach can be divided into three steps: (1) point cloud pre-processing, (2) facet over-segmentation, and (3) facet region growing for individual leaf segmentation. The experimental results show that the proposed method is effective and efficient in segmenting individual leaves from 3D point clouds of greenhouse ornamentals such as Epipremnum aureum, Monstera deliciosa, and Calathea makoyana, and the average precision and recall are both above 90%. The results also reveal the wide applicability of the proposed methodology for point clouds scanned from different kinds of 3D imaging systems, such as stereo vision and Kinect v2. Moreover, our method is potentially applicable in a broad range of applications that aim at segmenting regular surfaces and objects from a point cloud.

scholarly journals Integration of Laser Scanner and Photogrammetry for Heritage BIM Enhancement

2021 ◽  
Vol 10 (5) ◽  
pp. 316
Author(s):  
Yahya Alshawabkeh ◽  
Ahmad Baik ◽  
Yehia Miky
Keyword(s):  
Point Cloud ◽  
Three Dimensional ◽  
Laser Scanner ◽  
Cost Effective ◽  
Point Clouds ◽  
Data Interpretation ◽  
Information Modeling ◽  
World Heritage Site ◽  
Architectural Features ◽  
House Museum

Digital 3D capture and reliable reproduction of architectural features is the first and most difficult step towards defining a heritage BIM. Three-dimensional digital survey technologies, such as TLS and photogrammetry, enable experts to scan buildings with a new level of detail. Challenges in the tracing of parametric objects in a TLS point cloud include the reconstruction of occluded parts, measurement of uncertainties relevant to surface reflectivity, and edge detection and location. In addition to image-based techniques being considered cost effective, highly flexible, and efficient in producing a high-quality 3D textured model, they also provide a better interpretation of surface linear characteristics. This article addresses an architecture survey workflow using photogrammetry and TLS to optimize a point cloud that is sufficient for a reliable HBIM. Fusion-based workflows were proposed during the recording of two heritage sites—the Matbouli House Museum in Historic Jeddah, a UNESCO World Heritage Site; and Asfan Castle. In the Matbouli House Museum building, which is rich with complex architectural features, multi-sensor recording was implemented at different resolutions and levels of detail. The TLS data were used to reconstruct the basic shape of the main structural elements, while the imagery’s superior radiometric data and accessibility were effectively used to enhance the TLS point clouds for improving the geometry, data interpretation, and parametric tracing of irregular objects in the facade. Furthermore, in the workflow that is considered to be the ragged terrain of the Castle of Asfan, here, the TLS point cloud was supplemented with UAV data in the upper building zones where the shadow data originated. Both datasets were registered using an ICP algorithm to scale the photogrammetric data and define their actual position in the construction system. The hybrid scans were imported and processed in the BIM environment. The building components were segmented and classified into regular and irregular surfaces, in order to perform detailed building information modeling of the architectural elements. The proposed workflows demonstrated an appropriate performance in terms of reliable and complete BIM mapping in the complex structures.

scholarly journals Historic Timber Roof Structure Reconstruction through Automated Analysis of Point Clouds

2022 ◽  
Vol 8 (1) ◽  
pp. 10
Author(s):  
Taşkın Özkan ◽  
Norbert Pfeifer ◽  
Gudrun Styhler-Aydın ◽  
Georg Hochreiner ◽  
Ulrike Herbig ◽  
...  
Keyword(s):  
Laser Scanning ◽  
3D Model ◽  
Region Growing ◽  
Point Clouds ◽  
Automated Analysis ◽  
Structural Elements ◽  
Roof Structure ◽  
Main Target ◽  
Side Face ◽  
Region Growing Segmentation

We present a set of methods to improve the automation of the parametric 3D modeling of historic roof structures using terrestrial laser scanning (TLS) point clouds. The final product of the TLS point clouds consist of 3D representation of all objects, which were visible during the scanning, including structural elements, wooden walking ways and rails, roof cover and the ground; thus, a new method was applied to detect and exclude the roof cover points. On the interior roof points, a region-growing segmentation-based beam side face searching approach was extended with an additional method that splits complex segments into linear sub-segments. The presented workflow was conducted on an entire historic roof structure. The main target is to increase the automation of the modeling in the context of completeness. The number of manually counted beams served as reference to define a completeness ratio for results of automatically modeling beams. The analysis shows that this approach could increase the quantitative completeness of the full automatically generated 3D model of the roof structure from 29% to 63%.

scholarly journals A DATA DRIVEN METHOD FOR FLAT ROOF BUILDING RECONSTRUCTION FROM LiDAR POINT CLOUDS

2017 ◽  
Vol XLII-4/W4 ◽  
pp. 167-172 ◽  
Author(s):  
A. Mahphood ◽  
H. Arefi
Keyword(s):  
Region Growing ◽  
Point Clouds ◽  
Airborne Lidar ◽  
Data Driven ◽  
Grid Data ◽  
Building Modeling ◽  
Straight Line ◽  
Edge Points ◽  
Centroid Point ◽  
Flat Roof

3D building modeling is one of the most important applications in photogrammetry and remote sensing. Airborne LiDAR (Light Detection And Ranging) is one of the primary information sources for building modeling. In this paper, a new data-driven method is proposed for 3D building modeling of flat roofs. First, roof segmentation is implemented using region growing method. The distance between roof points and the height difference of the points are utilized in this step. Next, the building edge points are detected using a new method that employs grid data, and then roof lines are regularized using the straight line approximation. The centroid point and direction for each line are estimated in this step. Finally, 3D model is reconstructed by integrating the roof and wall models. In the end, a qualitative and quantitative assessment of the proposed method is implemented. The results show that the proposed method could successfully model the flat roof buildings using LiDAR point cloud automatically.

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