scholarly journals Building Component Detection on Unstructured 3D Indoor Point Clouds Using RANSAC-Based Region Growing

2021 ◽  
Vol 13 (2) ◽  
pp. 161
Author(s):  
Sangmin Oh ◽  
Dongmin Lee ◽  
Minju Kim ◽  
Taehoon Kim ◽  
Hunhee Cho
Keyword(s):  
Indoor Environment ◽  
Laser Scanner ◽  
Region Growing ◽  
Point Clouds ◽  
Detection Algorithm ◽  
Cloud Data ◽  
Building Components ◽  
Building Component ◽  
Geometric Representations

With the advancement of light detection and ranging (LiDAR) technology, the mobile laser scanner (MLS) has been regarded as an important technology to collect geometric representations of the indoor environment. In particular, methods for detecting indoor objects from indoor point cloud data (PCD) captured through MLS have thus far been developed based on the trajectory of MLS. However, the existing methods have a limitation on applying to an indoor environment where the building components made by concrete impede obtaining the information of trajectory. Thus, this study aims to propose a building component detection algorithm for MLS-based indoor PCD without trajectory using random sample consensus (RANSAC)-based region growth. The proposed algorithm used the RANSAC and region growing to overcome the low accuracy and uniformity of MLS caused by the movement of LiDAR. This study ensures over 90% precision, recall, and proper segmentation rate of building component detection by testing the algorithm using the indoor PCD. The result of the case study shows that the proposed algorithm opens the possibility of accurately detecting interior objects from indoor PCD without trajectory information of MLS.

scholarly journals INDOOR MODELLING FROM SLAM-BASED LASER SCANNER: DOOR DETECTION TO ENVELOPE RECONSTRUCTION

2017 ◽  
Vol XLII-2/W7 ◽  
pp. 345-352 ◽  
Author(s):  
L. Díaz-Vilariño ◽  
E. Verbree ◽  
S. Zlatanova ◽  
A. Diakité
Keyword(s):  
Point Cloud ◽  
Minimum Energy ◽  
Laser Scanner ◽  
Point Clouds ◽  
Cloud Data ◽  
Indoor Space ◽  
Combined Use ◽  
Global Configuration ◽  
Envelope Reconstruction

Updated and detailed indoor models are being increasingly demanded for various applications such as emergency management or navigational assistance. The consolidation of new portable and mobile acquisition systems has led to a higher availability of 3D point cloud data from indoors. In this work, we explore the combined use of point clouds and trajectories from SLAM-based laser scanner to automate the reconstruction of building indoors. The methodology starts by door detection, since doors represent transitions from one indoor space to other, which constitutes an initial approach about the global configuration of the point cloud into building rooms. <br><br> For this purpose, the trajectory is used to create a vertical point cloud profile in which doors are detected as local minimum of vertical distances. As point cloud and trajectory are related by time stamp, this feature is used to subdivide the point cloud into subspaces according to the location of the doors. The correspondence between subspaces and building rooms is not unambiguous. One subspace always corresponds to one room, but one room is not necessarily depicted by just one subspace, for example, in case of a room containing several doors and in which the acquisition is performed in a discontinue way. The labelling problem is formulated as combinatorial approach solved as a minimum energy optimization. Once the point cloud is subdivided into building rooms, envelop (conformed by walls, ceilings and floors) is reconstructed for each space. The connectivity between spaces is included by adding the previously detected doors to the reconstructed model. The methodology is tested in a real case study.

scholarly journals The Combined Use of SLAM Laser Scanning and TLS for the 3D Indoor Mapping

Buildings ◽  
2021 ◽  
Vol 11 (9) ◽  
pp. 386
Author(s):  
Aino Keitaanniemi ◽  
Juho-Pekka Virtanen ◽  
Petri Rönnholm ◽  
Antero Kukko ◽  
Toni Rantanen ◽  
...  
Keyword(s):  
Indoor Environment ◽  
Laser Scanning ◽  
Laser Scanner ◽  
Point Clouds ◽  
Information Modeling ◽  
Mapping Strategy ◽  
Localization And Mapping ◽  
Combined Use ◽  
Building Information

An efficient 3D survey of a complex indoor environment remains a challenging task, especially if the accuracy requirements for the geometric data are high for instance in building information modeling (BIM) or construction. The registration of non-overlapping terrestrial laser scanning (TLS) point clouds is laborious. We propose a novel indoor mapping strategy that uses a simultaneous localization and mapping (SLAM) laser scanner (LS) to support the building-scale registration of non-overlapping TLS point clouds in order to reconstruct comprehensive building floor/3D maps. This strategy improves efficiency since it allows georeferenced TLS data to only be collected from those parts of the building that require such accuracy. The rest of the building is measured with SLAM LS accuracy. Based on the results of the case study, the introduced method can locate non-overlapping TLS point clouds with an accuracy of 18–51 mm using target sphere comparison.

scholarly journals Comparison of Depth Camera and Terrestrial Laser Scanner in Monitoring Structural Deflections

Sensors ◽  
2020 ◽  
Vol 21 (1) ◽  
pp. 201
Author(s):  
Michael Bekele Maru ◽  
Donghwan Lee ◽  
Kassahun Demissie Tola ◽  
Seunghee Park
Keyword(s):  
Optical Sensors ◽  
Point Cloud ◽  
Three Dimensional ◽  
Laser Scanner ◽  
Point Clouds ◽  
Depth Camera ◽  
Terrestrial Laser Scanner ◽  
Cloud Data ◽  
3D Point Clouds ◽  
3D Information

Modeling a structure in the virtual world using three-dimensional (3D) information enhances our understanding, while also aiding in the visualization, of how a structure reacts to any disturbance. Generally, 3D point clouds are used for determining structural behavioral changes. Light detection and ranging (LiDAR) is one of the crucial ways by which a 3D point cloud dataset can be generated. Additionally, 3D cameras are commonly used to develop a point cloud containing many points on the external surface of an object around it. The main objective of this study was to compare the performance of optical sensors, namely a depth camera (DC) and terrestrial laser scanner (TLS) in estimating structural deflection. We also utilized bilateral filtering techniques, which are commonly used in image processing, on the point cloud data for enhancing their accuracy and increasing the application prospects of these sensors in structure health monitoring. The results from these sensors were validated by comparing them with the outputs from a linear variable differential transformer sensor, which was mounted on the beam during an indoor experiment. The results showed that the datasets obtained from both the sensors were acceptable for nominal deflections of 3 mm and above because the error range was less than ±10%. However, the result obtained from the TLS were better than those obtained from the DC.

scholarly journals Segmentation and Multi-Scale Convolutional Neural Network-Based Classification of Airborne Laser Scanner Data

Sensors ◽  
2018 ◽  
Vol 18 (10) ◽  
pp. 3347 ◽  
Author(s):  
Zhishuang Yang ◽  
Bo Tan ◽  
Huikun Pei ◽  
Wanshou Jiang
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Laser Scanning ◽  
Laser Scanner ◽  
Region Growing ◽  
Point Clouds ◽  
Classification Method ◽  
Multi Scale ◽  
Airborne Laser

The classification of point clouds is a basic task in airborne laser scanning (ALS) point cloud processing. It is quite a challenge when facing complex observed scenes and irregular point distributions. In order to reduce the computational burden of the point-based classification method and improve the classification accuracy, we present a segmentation and multi-scale convolutional neural network-based classification method. Firstly, a three-step region-growing segmentation method was proposed to reduce both under-segmentation and over-segmentation. Then, a feature image generation method was used to transform the 3D neighborhood features of a point into a 2D image. Finally, feature images were treated as the input of a multi-scale convolutional neural network for training and testing tasks. In order to obtain performance comparisons with existing approaches, we evaluated our framework using the International Society for Photogrammetry and Remote Sensing Working Groups II/4 (ISPRS WG II/4) 3D labeling benchmark tests. The experiment result, which achieved 84.9% overall accuracy and 69.2% of average F1 scores, has a satisfactory performance over all participating approaches analyzed.

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 Automatic 2D Floorplan CAD Generation from 3D Point Clouds

2020 ◽  
Vol 10 (8) ◽  
pp. 2817 ◽  
Author(s):  
Uuganbayar Gankhuyag ◽  
Ji-Hyeong Han
Keyword(s):  
Point Clouds ◽  
Detection Algorithm ◽  
Information Modeling ◽  
Ongoing Research ◽  
Cloud Data ◽  
Industry Foundation Classes ◽  
Automated Method ◽  
3D Point Clouds ◽  
Building Information ◽  
Geometric Quality

In the architecture, engineering, and construction (AEC) industry, creating an indoor model of existing buildings has been a challenging task since the introduction of building information modeling (BIM). Because the process of BIM is primarily manual and implies a high possibility of error, the automated creation of indoor models remains an ongoing research. In this paper, we propose a fully automated method to generate 2D floorplan computer-aided designs (CADs) from 3D point clouds. The proposed method consists of two main parts. The first is to detect planes in buildings, such as walls, floors, and ceilings, from unstructured 3D point clouds and to classify them based on the Manhattan-World (MW) assumption. The second is to generate 3D BIM in the industry foundation classes (IFC) format and a 2D floorplan CAD using the proposed line-detection algorithm. We experimented the proposed method on 3D point cloud data from a university building, residential houses, and apartments and evaluated the geometric quality of a wall reconstruction. We also offer the source code for the proposed method on GitHub.

scholarly journals The city model as a tool for participatory urban planning – a case study: The Bilotti open air museum of Cosenza

2014 ◽  
Vol II-5 ◽  
pp. 25-32
Author(s):  
S. Artese
Keyword(s):  
Urban Planning ◽  
Laser Scanner ◽  
Point Clouds ◽  
Web Gis ◽  
Scale Model ◽  
3D City Model ◽  
City Model ◽  
The City ◽  
Citizen’S Participation

The paper describes the implementation of the 3D city model of the pedestrian area of Cosenza, which in recent years has become the Bilotti Open Air Museum (MAB). For this purpose were used both the data available (regional technical map, city maps, orthophotos) and acquired through several surveys of buildings and "Corso Mazzini" street (photos, topographic measurements, laser scanner point clouds). In addition to the urban scale model, the survey of the statues of the MAB was carried out. By means of data processing, the models of the same statues have been created, that can be used as objects within the city model. <br><br> The 3D model of the MAB open air museum has been used to implement a Web-GIS allowing the citizen's participation, understanding and suggestions. The 3D city model is intended as a new tool for urban planning, therefore it has been used both for representing the current situation of the MAB and for design purposes, by acknowledging suggestions regarding a possible different location of the statues and a new way to enjoy the museum.

scholarly journals SEGMENTATION OF LARGE UNSTRUCTURED POINT CLOUDS USING OCTREE-BASED REGION GROWING AND CONDITIONAL RANDOM FIELDS

2017 ◽  
Vol XLII-2/W8 ◽  
pp. 25-30 ◽  
Author(s):  
M. Bassier ◽  
M. Bonduel ◽  
B. Van Genechten ◽  
M. Vergauwen
Keyword(s):  
Random Fields ◽  
Point Cloud ◽  
Conditional Random Fields ◽  
Conditional Random Field ◽  
Region Growing ◽  
Point Clouds ◽  
Single Type ◽  
Point Cloud Data ◽  
Cloud Data ◽  
Point Cloud Segmentation

Point cloud segmentation is a crucial step in scene understanding and interpretation. The goal is to decompose the initial data into sets of workable clusters with similar properties. Additionally, it is a key aspect in the automated procedure from point cloud data to BIM. Current approaches typically only segment a single type of primitive such as planes or cylinders. Also, current algorithms suffer from oversegmenting the data and are often sensor or scene dependent.<br><br> In this work, a method is presented to automatically segment large unstructured point clouds of buildings. More specifically, the segmentation is formulated as a graph optimisation problem. First, the data is oversegmented with a greedy octree-based region growing method. The growing is conditioned on the segmentation of planes as well as smooth surfaces. Next, the candidate clusters are represented by a Conditional Random Field after which the most likely configuration of candidate clusters is computed given a set of local and contextual features. The experiments prove that the used method is a fast and reliable framework for unstructured point cloud segmentation. Processing speeds up to 40,000 points per second are recorded for the region growing. Additionally, the recall and precision of the graph clustering is approximately 80%. Overall, nearly 22% of oversegmentation is reduced by clustering the data. These clusters will be classified and used as a basis for the reconstruction of BIM models.

scholarly journals AUGMENTED REALITY FOR CULTURAL HERITAGE: THE REBIRTH OF A HISTORICAL SQUARE

2019 ◽  
Vol XLII-2/W17 ◽  
pp. 303-308
Author(s):  
A. Scianna ◽  
G. F. Gaglio ◽  
M. La Guardia
Keyword(s):  
Augmented Reality ◽  
Rapid Prototyping ◽  
Cultural Heritage ◽  
Laser Scanner ◽  
Point Clouds ◽  
New Methods ◽  
Study Case ◽  
3D Information ◽  
Architectural Barriers

Abstract. The case study, faced in this paper, arises in the context of Interreg Italia-Malta European project named I-Access, dedicated to the improvement of accessibility to Cultural Heritage (CH). Accessibility considered not only as the demolition of physical architectural barriers, but also as the possibility of fruition of CH through technological tools that can increase its perception and knowledge. Last achievements in photogrammetry and terrestrial laser scanner (TLS) technology offered new methods of data acquisition in the field of CH, giving the possibility of monitoring and processing big data, in the form of point clouds. Ever in this field, reverse engineering techniques and computer graphics are even more used for involving visitors to discover CH, with navigation into 3D reconstructions, empowering the real visualization adding further 3D information through the Augmented Reality (AR). At the same time, recent advances on rapid prototyping technologies grant the automated 3D printing of scaled 3D model reconstructions of real CH elements allowing the tactile fruition of visitors that suffer from visual defects and the connection with 3D AR visualizations. The presented work shows how these technologies could revive an historical square, the Piazza Garraffo in Palermo (Italy), with the virtual insertion of its baroque fountain, originally placed there. The final products of this work are an indoor and an outdoor AR mobile application, that allow the visualization of the historical original asset of the square. This study case shows how the mixing of AR and the rapid prototyping technologies could be useful for the improvement of the fruition of CH. This work could be considered a multidisciplinary experimentation, where different technologies, today still in development, contribute to the same goal aimed at improving the accessibility of the monument for enhancing the fruition of CH.

scholarly journals Rockfall monitoring by Terrestrial Laser Scanning – case study of the basaltic rock face at Castellfollit de la Roca (Catalonia, Spain)

2011 ◽  
Vol 11 (3) ◽  
pp. 829-841 ◽  
Author(s):  
A. Abellán ◽  
J. M. Vilaplana ◽  
J. Calvet ◽  
D. García-Sellés ◽  
E. Asensio
Keyword(s):  
Urban Areas ◽  
Laser Scanning ◽  
Mass Movement ◽  
Laser Scanner ◽  
Point Clouds ◽  
Basaltic Rock ◽  
Historical Record ◽  
Rock Face ◽  
The Village

Abstract. This case study deals with a rock face monitoring in urban areas using a Terrestrial Laser Scanner. The pilot study area is an almost vertical, fifty meter high cliff, on top of which the village of Castellfollit de la Roca is located. Rockfall activity is currently causing a retreat of the rock face, which may endanger the houses located at its edge. TLS datasets consist of high density 3-D point clouds acquired from five stations, nine times in a time span of 22 months (from March 2006 to January 2008). The change detection, i.e. rockfalls, was performed through a sequential comparison of datasets. Two types of mass movement were detected in the monitoring period: (a) detachment of single basaltic columns, with magnitudes below 1.5 m3 and (b) detachment of groups of columns, with magnitudes of 1.5 to 150 m3. Furthermore, the historical record revealed (c) the occurrence of slab failures with magnitudes higher than 150 m3. Displacements of a likely slab failure were measured, suggesting an apparent stationary stage. Even failures are clearly episodic, our results, together with the study of the historical record, enabled us to estimate a mean detachment of material from 46 to 91.5 m3 year−1. The application of TLS considerably improved our understanding of rockfall phenomena in the study area.

Sign in / Sign up

Export Citation Format

Share Document