scholarly journals Measurement of Particle Size of Loose Accumulation Based on Alpha Shapes (AS) and Hill Climbing-Region Growing (HC-RG) Algorithms

Sensors ◽  
2020 ◽  
Vol 20 (3) ◽  
pp. 883
Author(s):  
Yunfeng Ge ◽  
Zishan Lin ◽  
Huiming Tang ◽  
Peng Zhong ◽  
Bei Cao
Keyword(s):  
Physical Model ◽  
Laser Scanning ◽  
Particle Number ◽  
Region Growing ◽  
Point Clouds ◽  
Negative Influence ◽  
Hill Climbing ◽  
Process Data ◽  
Alpha Shapes ◽  
Geological Conditions

The loose accumulation CAUSED by landslide, collapse, debris flow, and mine blasting, exerts considerable negative influence to human activities. Besides, it can easily trigger secondary disaster under inner and outer geological conditions. Extraction and measurement of the particle of loose accumulation is of importance for prediction of slope stability and mine blasting. In this paper, the 3D laser scanning is utilized to collect the point clouds of granular materials in physical model (three types of materials) and landslide accumulation in field, respectively. Then, the alpha shapes (AS) and hill climbing-region growing (HC-RG) algorithms are introduced for identifying particles and finding their dimensions (e.g., particle number and radii). Comparison between the recognition results and reality shows that both algorithms can provide a good performance in laboratory physical model, and acceptable results can be obtained when applying two algorithm to field survey. AS algorithm needs less time to process data than HC-GR algorithm; however, the recognition from HC-RG algorithm is more accurate than that by AS algorithm.

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 Reconstructing 2-D/3-D Building Shapes from Spaceborne Tomographic Synthetic Aperture Radar Data

2014 ◽  
Vol XL-3 ◽  
pp. 313-320 ◽  
Author(s):  
M. Shahzad ◽  
X. X. Zhu
Keyword(s):  
Spatial Clustering ◽  
Region Growing ◽  
Point Clouds ◽  
Radar Data ◽  
Point Scatterer ◽  
Synthetic Aperture Radar Data ◽  
Interferometric Technique ◽  
Building Shape ◽  
Alpha Shapes ◽  
Seed Points

In this paper, we present an approach that allows automatic (parametric) reconstruction of building shapes in 2-D/3-D using TomoSAR point clouds. These point clouds are generated by processing radar image stacks via advanced interferometric technique, called SAR tomography. The proposed approach reconstructs the building outline by exploiting both the available roof and façade information. Roof points are extracted out by employing a surface normals based region growing procedure via selected seed points while the extraction of façade points is based on thresholding the point scatterer density <i>SD</i> estimated by robust M-estimator. Spatial clustering is then applied to the extracted roof points in a way such that each roof cluster represents an individual building. Extracted façade points are reconstructed and afterwards incorporated to the segmented roof cluster to reconstruct the complete building shape. Initial building footprints are derived by employing alpha shapes method that are later regularized. Finally, rectilinear constraints are added to yield better geometrically looking building shapes. The proposed approach is illustrated and validated by examples using TomoSAR point clouds generated from a stack of TerraSAR-X high-resolution spotlight images from ascending orbit only covering two different test areas with one containing relatively smaller buildings in densely populated regions and the other containing moderate sized buildings in the city of Las Vegas.

scholarly journals FAST AND ROBUST SEGMENTATION AND CLASSIFICATION FOR CHANGE DETECTION IN URBAN POINT CLOUDS

2016 ◽  
Vol XLI-B3 ◽  
pp. 693-699 ◽  
Author(s):  
X. Roynard ◽  
J.-E. Deschaud ◽  
F. Goulette
Keyword(s):  
Change Detection ◽  
Laser Scanning ◽  
Three Dimensional ◽  
Region Growing ◽  
Point Clouds ◽  
Object Class ◽  
Robust Segmentation ◽  
Street Furniture ◽  
Fast Region

Change detection is an important issue in city monitoring to analyse street furniture, road works, car parking, etc. For example, parking surveys are needed but are currently a laborious task involving sending operators in the streets to identify the changes in car locations. In this paper, we propose a method that performs a fast and robust segmentation and classification of urban point clouds, that can be used for change detection. We apply this method to detect the cars, as a particular object class, in order to perform parking surveys automatically. A recently proposed method already addresses the need for fast segmentation and classification of urban point clouds, using elevation images. The interest to work on images is that processing is much faster, proven and robust. However there may be a loss of information in complex 3D cases: for example when objects are one above the other, typically a car under a tree or a pedestrian under a balcony. In this paper we propose a method that retain the three-dimensional information while preserving fast computation times and improving segmentation and classification accuracy. It is based on fast region-growing using an octree, for the segmentation, and specific descriptors with Random-Forest for the classification. Experiments have been performed on large urban point clouds acquired by Mobile Laser Scanning. They show that the method is as fast as the state of the art, and that it gives more robust results in the complex 3D cases.

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 VO-SMOG: A VERSATILE, SMOOTH SEGMENT-BASED GROUND FILTER FOR POINT CLOUDS VIA MULTI-SCALE VOXELIZATION

2021 ◽  
Vol VIII-4/W2-2021 ◽  
pp. 59-66
Author(s):  
E. Che ◽  
A. Senogles ◽  
M. J. Olsen
Keyword(s):  
Laser Scanning ◽  
Large Scale ◽  
Mean Squared Error ◽  
Point Clouds ◽  
Process Data ◽  
Multi Scale ◽  
Digital Elevation ◽  
Real World Datasets ◽  
Aircraft System ◽  
Elevation Model

Abstract. Point clouds acquired by light detection and ranging (lidar) and photogrammetry technology (e.g., structure from motion/multi-view stereo-SfM/MVS) are widely used for various applications such topographic mapping due to their high resolution and accuracy. To generate a digital elevation model (DEM) or extract other features in the data, the ground points and non-ground points usually need to be separated first. This process, called ground filtering, can be tedious and time consuming as it requires substantial manual effort for high quality results. Although many have developed automated ground filtering algorithms, very few have the versatility to process data acquired from different scenes and systems. In this paper, we propose a versatile ground filter based on multi-scale voxelization and smooth segments, named Vo-SmoG. The proposed method introduces a novel voxelization approach, followed by isolated voxel filtering, lowest point filtering, local smooth filtering, and ground clustering. The result of the Vo-SmoG ground filtering is a classified point cloud. The effectiveness and efficiency of our method are demonstrated qualitatively and quantitatively. The quantitative evaluation consists of both point-wise and grid-wise comparisons. The recall, precision, and F1-score are over 97% in terms of classification while the root mean squared error (RMSE) of the DEM is within 0.1 m, which is on par with the reported vertical accuracy of the tested data. We further demonstrate the versatility of the Vo-SmoG via large-scale, real-world datasets collected from different environments with mobile laser scanning, airborne laser scanning, terrestrial laser scanning, uncrewed aircraft system (UAS)-SfM, and UAS-lidar.

scholarly journals FAST AND ROBUST SEGMENTATION AND CLASSIFICATION FOR CHANGE DETECTION IN URBAN POINT CLOUDS

2016 ◽  
Vol XLI-B3 ◽  
pp. 693-699 ◽  
Author(s):  
X. Roynard ◽  
J.-E. Deschaud ◽  
F. Goulette
Keyword(s):  
Change Detection ◽  
Laser Scanning ◽  
Three Dimensional ◽  
Region Growing ◽  
Point Clouds ◽  
Object Class ◽  
Robust Segmentation ◽  
Street Furniture ◽  
Fast Region

Change detection is an important issue in city monitoring to analyse street furniture, road works, car parking, etc. For example, parking surveys are needed but are currently a laborious task involving sending operators in the streets to identify the changes in car locations. In this paper, we propose a method that performs a fast and robust segmentation and classification of urban point clouds, that can be used for change detection. We apply this method to detect the cars, as a particular object class, in order to perform parking surveys automatically. A recently proposed method already addresses the need for fast segmentation and classification of urban point clouds, using elevation images. The interest to work on images is that processing is much faster, proven and robust. However there may be a loss of information in complex 3D cases: for example when objects are one above the other, typically a car under a tree or a pedestrian under a balcony. In this paper we propose a method that retain the three-dimensional information while preserving fast computation times and improving segmentation and classification accuracy. It is based on fast region-growing using an octree, for the segmentation, and specific descriptors with Random-Forest for the classification. Experiments have been performed on large urban point clouds acquired by Mobile Laser Scanning. They show that the method is as fast as the state of the art, and that it gives more robust results in the complex 3D cases.

scholarly journals ISPRS BENCHMARK ON MULTISENSORY INDOOR MAPPING AND POSITIONING

2020 ◽  
Vol V-5-2020 ◽  
pp. 117-123
Author(s):  
C. Wang ◽  
Y. Dai ◽  
N. Elsheimy ◽  
C. Wen ◽  
G. Retscher ◽  
...  
Keyword(s):  
Feature Extraction ◽  
Laser Scanning ◽  
Point Clouds ◽  
Indoor Positioning ◽  
Benchmark Dataset ◽  
Scanning System ◽  
Indoor Environments ◽  
Process Data ◽  
Localization And Mapping ◽  
Common Framework

Abstract. In this paper, we present a publicly available benchmark dataset on multisensorial indoor mapping and positioning (MiMAP), which is sponsored by ISPRS scientific initiatives. The benchmark dataset includes point clouds captured by an indoor mobile laser scanning system in indoor environments of various complexity. The benchmark aims to stimulate and promote research in the following three fields: (1) LiDAR-based Simultaneous Localization and Mapping (SLAM); (2) automated Building Information Model (BIM) feature extraction; and (3) multisensory indoor positioning. The MiMAP project provides a common framework for the evaluation and comparison of LiDAR-based SLAM, BIM feature extraction, and smartphone-based indoor positioning methods. This paper describes the multisensory setup, data acquisition process, data description, challenges, and evaluation metrics included in the MiMAP project.

scholarly journals A line-based approach for precise extraction of road and curb region from mobile mapping data

2014 ◽  
Vol II-5 ◽  
pp. 243-250 ◽  
Author(s):  
R. Miyazaki ◽  
M. Yamamoto ◽  
E. Hanamoto ◽  
H. Izumi ◽  
K. Harada
Keyword(s):  
Laser Scanning ◽  
Distribution Density ◽  
Region Growing ◽  
Point Clouds ◽  
Planar Structure ◽  
Scanning System ◽  
Normal Vector ◽  
Mobile Mapping ◽  
Planar Surfaces ◽  
Structure Detection

Planar structure detection from point clouds is important process in many applications such as maintenance of infrastructure facility including roads and curbs because most artificial structures consists of planar surfaces. The Mobile Mapping System can obtain a large amount of points with traveling at a standard speed. However, in the case that the high-end laser scanning system is equipped, the distribution density of points is uneven. In the point-based method, this situation causes the problem to the method of calculating geometric information using neighborhood points. In this paper, we propose a line-based region growing method in order to detect planar structures with precise boundary from point clouds with uneven distribution density of points. The precise boundary of a planar structure is maintained by appropriately creating line segments from the input clouds. We adapt the definition of neighborhood and the estimation of the normal vector to the line-based region growing. The evaluation by comparing our result with manually extracted points shows that more than 98% of curb points are detected. And, about 90% of the boundary points between a road and a curb are detected with less than 0.005 meters of the distance error.

Line-Based Planar Structure Extraction from a Point Cloud with an Anisotropic Distribution

2017 ◽  
Vol 11 (4) ◽  
pp. 657-665 ◽  
Author(s):  
Ryuji Miyazaki ◽  
Makoto Yamamoto ◽  
Koichi Harada ◽  
◽  
◽  
...  
Keyword(s):  
Point Cloud ◽  
Laser Scanning ◽  
Region Growing ◽  
Point Clouds ◽  
Planar Structure ◽  
Scanning System ◽  
Normal Vector ◽  
Real Point ◽  
Planar Regions ◽  
Structure Extraction

We propose a line-based region growing method for extracting planar regions with precise boundaries from a point cloud with an anisotropic distribution. Planar structure extraction from point clouds is an important process in many applications, such as maintenance of infrastructure components including roads and curbstones, because most artificial structures consist of planar surfaces. A mobile mapping system (MMS) is able to obtain a large number of points while traveling at a standard speed. However, if a high-end laser scanning system is equipped, the point cloud has an anisotropic distribution. In traditional point-based methods, this causes problems when calculating geometric information using neighboring points. In the proposed method, the precise boundary of a planar structure is maintained by appropriately creating line segments from an input point cloud. Furthermore, a normal vector at a line segment is precisely estimated for the region growing process. An experiment using the point cloud from an MMS simulation indicates that the proposed method extracts planar regions accurately. Additionally, we apply the proposed method to several real point clouds and evaluate its effectiveness via visual inspection.

scholarly journals AUTOMATIC RAILWAY POWER LINE EXTRACTION USING MOBILE LASER SCANNING DATA

2016 ◽  
Vol XLI-B5 ◽  
pp. 615-619
Author(s):  
Shanxin Zhang ◽  
Cheng Wang ◽  
Zhuang Yang ◽  
Yiping Chen ◽  
Jonathan Li
Keyword(s):  
Laser Scanning ◽  
Region Growing ◽  
Point Clouds ◽  
Power Line ◽  
Power Lines ◽  
Practical Significance ◽  
Space Region ◽  
Extraction Technology ◽  
Line Extraction ◽  
Fitting Algorithm

Research on power line extraction technology using mobile laser point clouds has important practical significance on railway power lines patrol work. In this paper, we presents a new method for automatic extracting railway power line from MLS (Mobile Laser Scanning) data. Firstly, according to the spatial structure characteristics of power-line and trajectory, the significant data is segmented piecewise. Then, use the self-adaptive space region growing method to extract power lines parallel with rails. Finally use PCA (Principal Components Analysis) combine with information entropy theory method to judge a section of the power line whether is junction or not and which type of junction it belongs to. The least squares fitting algorithm is introduced to model the power line. An evaluation of the proposed method over a complicated railway point clouds acquired by a RIEGL VMX450 MLS system shows that the proposed method is promising.

Sign in / Sign up

Export Citation Format

Share Document