scholarly journals A BOUNDARY-ENHANCED SUPERVOXEL METHOD FOR EXTRACTION OF ROAD EDGES IN MLS POINT CLOUDS

2020 ◽  
Vol XLIII-B1-2020 ◽  
pp. 65-71
Author(s):  
Z. Sha ◽  
Y. Chen ◽  
W. Li ◽  
C. Wang ◽  
A. Nurunnabi ◽  
...  
Keyword(s):  
Point Clouds ◽  
Drop Out ◽  
Spatial Distance ◽  
Road Extraction ◽  
High Definition ◽  
Geometric Information ◽  
3D Point Clouds ◽  
Different Types ◽  
Promising Solution ◽  
Normal Feature

Abstract. Road extraction plays a significant role in production of high definition maps (HD maps). This paper presents a novel boundary-enhanced supervoxel segmentation method for extracting road edge contours from MLS point clouds. The proposed method first leverages normal feature judgment to obtain 3D point clouds global geometric information, then clusters points according to an existing method with global geometric information to enhance the boundaries. Finally, it utilizes the neighbor spatial distance metric to extract the contours and drop out existing outliers. The proposed method is tested on two datasets acquired by a RIEGL VMX-450 MLS system that contain the major point cloud scenes with different types of road boundaries. The experimental results demonstrate that the proposed method provides a promising solution for extracting contours efficiently and completely. Results show that the precision values are 1.5 times higher and approximately equal than the other two existing methods when the recall value is 0 for both tested two road datasets.

scholarly journals PLANAR CONSTRAINTS FOR AN IMPROVED UAV-IMAGE-BASED DENSE POINT CLOUD GENERATION

2015 ◽  
Vol XL-1/W4 ◽  
pp. 269-274 ◽  
Author(s):  
F. He ◽  
A. Habib ◽  
A. Al-Rawabdeh
Keyword(s):  
Point Cloud ◽  
Experimental Tests ◽  
Point Clouds ◽  
Geometric Information ◽  
Planar Surfaces ◽  
3D Point Clouds ◽  
Dense Image ◽  
Dense Point ◽  
Uav Image

In this paper, we proposed a new refinement procedure for the semi-global dense image matching. In order to remove outliers and improve the disparity image derived from the semi-global algorithm, both the local smoothness constraint and point cloud segments are utilized. Compared with current refinement technique, which usually assumes the correspondences between planar surfaces and 2D image segments, our proposed approach can effectively deal with object with both planar and curved surfaces. Meanwhile, since 3D point clouds contain more precise geometric information regarding to the reconstructed objects, the planar surfaces identified in our approach can be more accurate. In order to illustrate the feasibility of our approach, several experimental tests are conducted on both Middlebury test and real UAV-image datasets. The results demonstrate that our approach has a good performance on improving the quality of the derived dense image-based point cloud.

scholarly journals Fast and Accurate Power Line Corridor Survey Using Spatial Line Clustering of Point Cloud

2021 ◽  
Vol 13 (8) ◽  
pp. 1571
Author(s):  
Yuchun Huang ◽  
Yingli Du ◽  
Wenxuan Shi
Keyword(s):  
High Voltage ◽  
Ground Truth ◽  
Point Clouds ◽  
Power Line ◽  
Power Lines ◽  
Spatial Distance ◽  
Stable Operation ◽  
3D Point Clouds ◽  
Overhead Power Lines ◽  
Power Line Corridor

High-voltage and ultra-high-voltage overhead power lines are important to meet the electricity demand of our daily activities and productions. Due to the overgrowth of trees/vegetation within the corridor area, the distance between the power lines and its surroundings may break through the safety threshold, which could cause potential hazards such as discharge and fire. To ensure the safe and stable operation of the power lines, it is necessary to survey them regularly so that the potential hazards from the surroundings within the power line corridor could be investigated timely. This paper is motivated to quickly and accurately survey the power line corridor with the 3D point clouds. The main contributions of this paper include: (1) the spatial line clustering is proposed to accurately classify and complete the power line points, which can greatly overcome the sparsity and missing of LiDAR points within the complex power line corridor. (2) The contextual relationship between power lines and pylon is well investigated by the grid-based analysis, so that the suspension points of power lines on the pylon are well located. (3) The catenary plane-based simplification of 3D spatial distance calculation between power lines and ground objects facilitates the survey of the power line corridor. Experimental results show that the accuracy of safety distance surveying is 5 cm for power line corridors of all voltage levels. Compared to the ground-truth point-to-point calculation, the speed of surveying is enhanced thousands of times. It is promising to greatly improve both the accuracy and efficiency of surveying the potential hazards of power line corridor.

scholarly journals Performance analysis of different classifiers in segmenting point cloud data

2021 ◽  
Vol 2107 (1) ◽  
pp. 012003
Author(s):  
N I Boslim ◽  
S A Abdul Shukor ◽  
S N Mohd Isa ◽  
R Wong
Keyword(s):  
Random Forest ◽  
Point Cloud ◽  
Laser Scanner ◽  
Point Clouds ◽  
Nearest Neighbour ◽  
Point Cloud Data ◽  
Historical Building ◽  
Cloud Data ◽  
3D Point Clouds ◽  
Different Types

Abstract 3D point clouds are a set of point coordinates that can be obtained by using sensing device such as the Terrestrial Laser Scanner (TLS). Due to its high capability in collecting data and produce a strong density point cloud surrounding it, segmentation is needed to extract information from the massive point cloud containing different types of objects, apart from the object of interest. Bell Tower of Tawau, Sabah has been chosen as the object of interest to study the performance of different types of classifiers in segmenting the point cloud data. A state-of-the-art TLS was used to collect the data. This research’s aim is to segment the point cloud data of the historical building from its scene by using two different types of classifier and to study their performances. Two main classifiers commonly used in segmenting point cloud data of interest like building are tested here, which is Random Forest (RF) and k-Nearest Neighbour (kNN). As a result, it is found out that Random Forest classifier performs better in segmenting the existing point cloud data that represent the historic building compared to k-Nearest Neighbour classifier.

Automatic Global Registration of 3D Point Clouds for Reverse Engineering and Inspection Processes

2011 ◽  
Vol 110-116 ◽  
pp. 4907-4913
Author(s):  
Mariano Imbert ◽  
Xiao Xing Li
Keyword(s):  
Reverse Engineering ◽  
Point Clouds ◽  
Optimization Method ◽  
Global Coordinate System ◽  
Geometric Information ◽  
Data Set ◽  
Computing Power ◽  
New Approach ◽  
Global Registration ◽  
3D Point Clouds

Registration of 3D point clouds is one of the most fundamental phases during the process of reverse engineering and most challenging at the same time. This phase consists on matching two or more different point clouds into one data set in order to have them share the same global coordinate system. In this paper we present a new approach for automatic registration of 3D point clouds that uses the genetic algorithm (GA) as a global optimization method. We introduce a trips extraction technique for rough registration, which extracts important geometric information from a point cloud. Another contribution in this paper is the introduction of the Interpenetration Fraction Measure (IFM), which maximizes the number of points that overlap two different point clouds. The algorithm we present also takes advantage of the parallel computing power of today’s multi-core processors, and other techniques for further efficiency. Finally, we present some experimental data with comparisons for analysis and further discussion about the algorithm’s performance.

scholarly journals SEMANTIC LABELLING OF ROAD FURNITURE IN MOBILE LASER SCANNING DATA

2017 ◽  
Vol XLII-2/W7 ◽  
pp. 247-254 ◽  
Author(s):  
F. Li ◽  
S. Oude Elberink ◽  
G. Vosselman
Keyword(s):  
Laser Scanning ◽  
Large Scale ◽  
Point Clouds ◽  
Autonomous Driving ◽  
Urban Environments ◽  
3D Point Clouds ◽  
Promising Solution ◽  
Novel Method ◽  
Detailed Interpretation ◽  
Semantic Labelling

Road furniture semantic labelling is vital for large scale mapping and autonomous driving systems. Much research has been investigated on road furniture interpretation in both 2D images and 3D point clouds. Precise interpretation of road furniture in mobile laser scanning data still remains unexplored. In this paper, a novel method is proposed to interpret road furniture based on their logical relations and functionalities. Our work represents the most detailed interpretation of road furniture in mobile laser scanning data. 93.3 % of poles are correctly extracted and all of them are correctly recognised. 94.3 % of street light heads are detected and 76.9 % of them are correctly identified. Despite errors arising from the recognition of other components, our framework provides a promising solution to automatically map road furniture at a detailed level in urban environments.

scholarly journals Potential Use of Drone Ultra-High-Definition Videos for Detailed 3D City Modeling

2022 ◽  
Vol 11 (1) ◽  
pp. 34
Author(s):  
Bashar Alsadik ◽  
Yousif Hussein Khalaf
Keyword(s):  
Technology Development ◽  
Point Clouds ◽  
3D Models ◽  
Low Noise ◽  
High Definition ◽  
Sports Events ◽  
3D Point Clouds ◽  
Video Frames ◽  
Wide Range ◽  
City Modeling

Ongoing developments in video resolution either using consumer-grade or professional cameras has opened opportunities for different applications such as in sports events broadcasting and digital cinematography. In the field of geoinformation science and photogrammetry, image-based 3D city modeling is expected to benefit from this technology development. Highly detailed 3D point clouds with low noise are expected to be produced when using ultra high definition UHD videos (e.g., 4K, 8K). Furthermore, a greater benefit is expected when the UHD videos are captured from the air by consumer-grade or professional drones. To the best of our knowledge, no studies have been published to quantify the expected outputs when using UHD cameras in terms of 3D modeling and point cloud density. In this paper, a quantification is shown about the expected point clouds and orthophotos qualities when using UHD videos from consumer-grade drones and a review of which applications they can be applied in. The results show that an improvement in 3D models of ≅65% relative accuracy and ≅90% in point density can be attained when using 8K video frames compared with HD video frames which will open a wide range of applications and business cases in the near future.

scholarly journals Symmetry Analysis of Oriental Polygonal Pagodas Using 3D Point Clouds for Cultural Heritage

Sensors ◽  
2021 ◽  
Vol 21 (4) ◽  
pp. 1228
Author(s):  
Ting On Chan ◽  
Linyuan Xia ◽  
Yimin Chen ◽  
Wei Lang ◽  
Tingting Chen ◽  
...  
Keyword(s):  
Cultural Heritage ◽  
Unmanned Aerial Vehicle ◽  
Point Cloud ◽  
Geometric Model ◽  
Digital Camera ◽  
Point Clouds ◽  
Symmetry Analysis ◽  
3D Point Clouds ◽  
Transmission Towers ◽  
Aerial Vehicle

Ancient pagodas are usually parts of hot tourist spots in many oriental countries due to their unique historical backgrounds. They are usually polygonal structures comprised by multiple floors, which are separated by eaves. In this paper, we propose a new method to investigate both the rotational and reflectional symmetry of such polygonal pagodas through developing novel geometric models to fit to the 3D point clouds obtained from photogrammetric reconstruction. The geometric model consists of multiple polygonal pyramid/prism models but has a common central axis. The method was verified by four datasets collected by an unmanned aerial vehicle (UAV) and a hand-held digital camera. The results indicate that the models fit accurately to the pagodas’ point clouds. The symmetry was realized by rotating and reflecting the pagodas’ point clouds after a complete leveling of the point cloud was achieved using the estimated central axes. The results show that there are RMSEs of 5.04 cm and 5.20 cm deviated from the perfect (theoretical) rotational and reflectional symmetries, respectively. This concludes that the examined pagodas are highly symmetric, both rotationally and reflectionally. The concept presented in the paper not only work for polygonal pagodas, but it can also be readily transformed and implemented for other applications for other pagoda-like objects such as transmission towers.

scholarly journals Using Least-Squares Residuals to Assess the Stochasticity of Measurements—Example: Terrestrial Laser Scanner and Surface Modeling

2021 ◽  
Vol 5 (1) ◽  
pp. 59
Author(s):  
Gaël Kermarrec ◽  
Niklas Schild ◽  
Jan Hartmann
Keyword(s):  
Least Squares ◽  
Laser Scanner ◽  
Real Data ◽  
Point Clouds ◽  
Statistical Testing ◽  
Normal Vector ◽  
Engineering Structures ◽  
3D Point Clouds ◽  
Least Squares Adjustment ◽  
Correlation Parameters

Terrestrial laser scanners (TLS) capture a large number of 3D points rapidly, with high precision and spatial resolution. These scanners are used for applications as diverse as modeling architectural or engineering structures, but also high-resolution mapping of terrain. The noise of the observations cannot be assumed to be strictly corresponding to white noise: besides being heteroscedastic, correlations between observations are likely to appear due to the high scanning rate. Unfortunately, if the variance can sometimes be modeled based on physical or empirical considerations, the latter are more often neglected. Trustworthy knowledge is, however, mandatory to avoid the overestimation of the precision of the point cloud and, potentially, the non-detection of deformation between scans recorded at different epochs using statistical testing strategies. The TLS point clouds can be approximated with parametric surfaces, such as planes, using the Gauss–Helmert model, or the newly introduced T-splines surfaces. In both cases, the goal is to minimize the squared distance between the observations and the approximated surfaces in order to estimate parameters, such as normal vector or control points. In this contribution, we will show how the residuals of the surface approximation can be used to derive the correlation structure of the noise of the observations. We will estimate the correlation parameters using the Whittle maximum likelihood and use comparable simulations and real data to validate our methodology. Using the least-squares adjustment as a “filter of the geometry” paves the way for the determination of a correlation model for many sensors recording 3D point clouds.

A method of calculating the leafstalk angle of the soybean canopy based on 3D point clouds

2021 ◽  
Vol 42 (7) ◽  
pp. 2463-2484
Author(s):  
Kexin Zhu ◽  
Xiaodan Ma ◽  
Haiou Guan ◽  
Jiarui Feng ◽  
Zhichao Zhang ◽  
...  
Keyword(s):  
Point Clouds ◽  
3D Point Clouds ◽  
Soybean Canopy
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