A method of level of details control table for 3D point density scalability in video based point cloud compression

2019 ◽  
Author(s):  
Jiheon Im ◽  
Junsik Kim ◽  
Sungryeul Rhyu ◽  
Kyuheon Kim
Keyword(s):  
Point Cloud ◽  
Level Of Details ◽  
Point Density

scholarly journals POINT CLOUD ANALYSIS FOR UAV-BORNE LASER SCANNING WITH HORIZONTALLY AND VERTICALLY ORIENTED LINE SCANNERS – CONCEPT AND FIRST RESULTS

2017 ◽  
Vol XLII-2/W6 ◽  
pp. 399-406 ◽  
Author(s):  
M. Weinmann ◽  
M. S. Müller ◽  
M. Hillemann ◽  
N. Reydel ◽  
S. Hinz ◽  
...  
Keyword(s):  
Point Cloud ◽  
Laser Scanning ◽  
Vertical Direction ◽  
Flight Direction ◽  
Multiple Line ◽  
Point Density ◽  
Oriented Line ◽  
First Results ◽  
Line Scanner ◽  
Point Cloud Analysis

In this paper, we focus on UAV-borne laser scanning with the objective of densely sampling object surfaces in the local surrounding of the UAV. In this regard, using a line scanner which scans along the vertical direction and perpendicular to the flight direction results in a point cloud with low point density if the UAV moves fast. Using a line scanner which scans along the horizontal direction only delivers data corresponding to the altitude of the UAV and thus a low scene coverage. For these reasons, we present a concept and a system for UAV-borne laser scanning using multiple line scanners. Our system consists of a quadcopter equipped with horizontally and vertically oriented line scanners. We demonstrate the capabilities of our system by presenting first results obtained for a flight within an outdoor scene. Thereby, we use a downsampling of the original point cloud and different neighborhood types to extract fundamental geometric features which in turn can be used for scene interpretation with respect to linear, planar or volumetric structures.

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 ROBUST AND ACCURATE PLANE SEGMENTATION FROM POINT CLOUDS OF STRUCTURED SCENES

2020 ◽  
Vol V-2-2020 ◽  
pp. 221-226
Author(s):  
P. Hu ◽  
Y. Liu ◽  
M. Tian ◽  
M. Hou
Keyword(s):  
Energy Minimization ◽  
Point Cloud ◽  
Point Clouds ◽  
Segmentation Method ◽  
Topological Graph ◽  
New Approach ◽  
Input Point ◽  
Planar Surfaces ◽  
Point Density ◽  
Efficiency And Effectiveness

Abstract. Plane segmentation from the point cloud is an important step in various types of geo-information related to human activities. In this paper, we present a new approach to accurate segment planar primitives simultaneously by transforming it into the best matching issue between the over-segmented super-voxels and the 3D plane models. The super-voxels and its adjacent topological graph are firstly derived from the input point cloud as over-segmented small patches. Such initial 3D plane models are then enriched by fitting centroids of randomly sampled super-voxels, and translating these grouped planar super-voxels by structured scene prior (e.g. orthogonality, parallelism), while the generated adjacent graph will be updated along with planar clustering. To achieve the final super-voxels to planes assignment problem, an energy minimization framework is constructed using the productions of candidate planes, initial super-voxels, and the improved adjacent graph, and optimized to segment multiple consistent planar surfaces in the scenes simultaneously. The proposed algorithms are implemented, and three types of point clouds differing in feature characteristics (e.g. point density, complexity) are mainly tested to validate the efficiency and effectiveness of our segmentation method.

scholarly journals Self-Organized Model Fitting Method for Railway Structures Monitoring Using LiDAR Point Cloud

2020 ◽  
Vol 12 (22) ◽  
pp. 3702
Author(s):  
Amila Karunathilake ◽  
Ryohei Honma ◽  
Yasuhito Niina
Keyword(s):  
Point Cloud ◽  
Spatial Location ◽  
Railway Track ◽  
Computational Time ◽  
Accurate Estimation ◽  
Long Distance ◽  
Rail Track ◽  
Railway Line ◽  
Point Density ◽  
Vertical Slice

Mobile laser scanning (MLS) has been successfully used for infrastructure monitoring apt to its fine accuracy and higher point density, which is favorable for object reconstruction. The massive data size, computational time, wider spatial distribution and feature extraction become a challenging task for 3D point data processing with MLS point cloud receives from terrestrial structures such as buildings, roads and railway tracks. In this paper, we propose a new approach to detect the structures in-line with railway track geometry such as railway crossings, turnouts and quantitatively estimate their dimensions and spatial location by iteratively applying a vertical slice to point cloud data for long distance laser measurement. The rectangular vertical slices were defined and their boundary coordinates were estimated based on a geometrical method. Estimated vertical slice boundaries were iteratively used to evaluate the point density of each vertical slice along with a cross-track direction of the railway line. Those point densities were further analyzed to detect the railway line track objects by their shape and spatial location along with the rail bed. Herein, the survey dataset is used as a dictionary to preidentify the spatial location of the object and then as an accurate estimation for the rail-track, by estimating the gauge corner (GC) from dense point cloud. The proposed method has shown a significant improvement in the rail-track extraction process, which becomes a challenge for existing remote sensing technologies. This adaptive object detection method can be used to identify the railway track structures prior to the railway track extraction, which allows in finding the GC position precisely. Further, it is based on the parallelism of the railway track, which is distinct from conventional railway track extraction methods. Therefore it does not require any inertial measurements along with the MLS survey and can be applied with less background information of the observed MLS point cloud. The proposed algorithm was tested for the MLS data set acquired during the pilot project collaborated with West Japan Railway Company. The results indicate 100% accuracy for railway structure detection and enhance the GC extraction for railway structure monitoring.

scholarly journals Effects of LiDAR Point Density on Extraction of Traffic Signs: A Sensitivity Study

2019 ◽  
Vol 2673 (1) ◽  
pp. 41-51 ◽  
Author(s):  
Suliman Gargoum ◽  
Karim El-Basyouny
Keyword(s):  
Point Cloud ◽  
Point Clouds ◽  
Sensitivity Study ◽  
Angle Of Incidence ◽  
Traffic Signs ◽  
Detection Rates ◽  
Point Density ◽  
Dense Point ◽  
Different Levels ◽  
Cloud Density

Datasets collected using light detection and ranging (LiDAR) technology often consist of dense point clouds. However, the density of the point cloud could vary depending on several different factors including the capabilities of the data collection equipment, the conditions in which data are collected, and other features such as range and angle of incidence. Although variation in point density is expected to influence the quality of the information extracted from LiDAR, the extent to which changes in density could affect the extraction is unknown. Understanding such impacts is essential for agencies looking to adopt LiDAR technology and researchers looking to develop algorithms to extract information from LiDAR. This paper focuses specifically on understanding the impacts of point density on extracting traffic signs from LiDAR datasets. The densities of point clouds are first reduced using stratified random sampling; traffic signs are then extracted from those datasets at different levels of point density. The precision and accuracy of the detection process was assessed at the different levels of point cloud density and on four different highway segments. In general, it was found that for signs with large panels along the approach on which LiDAR data were collected, reducing the point cloud density by up to 70% of the original point cloud had minimal impacts on the sign detection rates. Results of this study provide practical guidance to transportation agencies interested in understanding the tradeoff in price, quality, and coverage, when acquiring LiDAR equipment for the inventory of traffic signs on their transportation networks.

scholarly journals A COMPARISON OF SINGLE PHOTON AND FULL WAVEFORM LIDAR

2019 ◽  
Vol IV-2/W5 ◽  
pp. 397-404 ◽  
Author(s):  
G. Mandlburger ◽  
H. Lehner ◽  
N. Pfeifer
Keyword(s):  
Point Cloud ◽  
Laser Scanning ◽  
Single Photon ◽  
Full Waveform ◽  
Point Density ◽  
Satisfactory Precision ◽  
Waveform Lidar ◽  
Full Waveform Lidar ◽  
The City ◽  
Scanning Systems

<p><strong>Abstract.</strong> Single photon sensitive LiDAR sensors are currently competing with conventional multi-photon laser scanning systems. The advantage of the prior is the potentially higher area coverage performance, which comes at the price of an increased outlier rate and a lower ranging accuracy. In this contribution, the principles of both technologies are reviewed with special emphasis on their respective properties. In addition, a comparison of Single Photon LiDAR (SPL) and FullWaveform LiDAR data acquired in July and September 2018 in the City of Vienna are presented. From data analysis we concluded that (i) less flight strips are needed to cover the same area with comparable point density with SPL, (ii) the sharpness of the resulting 3D point cloud is higher for the waveform LiDAR dataset, (iii) SPL exhibits moderate vegetation penetration under leaf-on conditions, and (iv) the dispersion of the SPL point cloud assessed in smooth horizontal surface parts competes with waveform LiDAR but is higher by a factor of 2&amp;ndash;3 for inclined and grassy surfaces, respectively. Still, SPL yielded satisfactory precision measures mostly below 10&amp;thinsp;cm.</p>

Impacts of Point Cloud Density Reductions on Extracting Road Geometric Features from mobile LiDAR data

2021 ◽  
Author(s):  
Suliman A Gargoum ◽  
Karim El-Basyouny
Keyword(s):  
Point Cloud ◽  
Original Data ◽  
Geometric Features ◽  
Cloud Data ◽  
Density Reduction ◽  
Sight Distance ◽  
Point Density ◽  
Slope Estimation ◽  
Low Sensitivity ◽  
Higher Sensitivity

Density of point cloud data varies depending on several different factors. Nonetheless, the extent to which changes in density could impact the accuracy of extracting roadway geometric features from the data is unknown. This paper investigates the impacts of point density reduction on the extraction of four critical geometric features. The density of the data was first reduced, and the different features were extracted at different levels of point density. The information obtained at lower point density was compared to what was obtained using the at 100% point density. It was found that clearance assessments and sight distance assessments had low sensitivity to reductions in point density (i.e. reducing the point density to as low as 10% of the original data (30ppm2 on the pavement surface) had minor impacts on the assessments). In contrast, for cross section slope estimation and curve attribute estimation higher sensitivity to point density was observed.

Using Hybrid Strategy for Region-Growing Mesh Reconstruction

2007 ◽  
Vol 10-12 ◽  
pp. 777-781 ◽  
Author(s):  
Yang Wang ◽  
Han Ming Lv ◽  
Shi Jun Ji
Keyword(s):  
Point Cloud ◽  
Region Growing ◽  
Triangle Mesh ◽  
Test Technique ◽  
Hybrid Strategy ◽  
High Point ◽  
Geometry Structure ◽  
Point Density ◽  
Mesh Reconstruction ◽  
Two Phases

A triangle mesh was reconstructed from an unorganized point cloud through two phases of mesh growing based on different strategies, where regions with high point density usually grow at the first phase and the remaining regions grow later. In each phase of mesh growing, the smoothest regions always grow firstly and then largely avoid errors emerging in sharp regions. The presented test technique of geometric integrity as well as the abnormality disposing method pledged the reconstructed mesh has correct geometry structure. Experiments show that the algorithm is efficient and effective.

scholarly journals AUTOMATIC CLASSIFICATION OF TREES FROM LASER SCANNING POINT CLOUDS

2015 ◽  
Vol II-3/W5 ◽  
pp. 137-144 ◽  
Author(s):  
B. Sirmacek ◽  
R. Lindenbergh
Keyword(s):  
Point Cloud ◽  
Laser Scanning ◽  
Point Clouds ◽  
Detection Methods ◽  
Local Maxima ◽  
Advantages And Disadvantages ◽  
Close Proximity ◽  
Point Density ◽  
Grid Surface

Development of laser scanning technologies has promoted tree monitoring studies to a new level, as the laser scanning point clouds enable accurate 3D measurements in a fast and environmental friendly manner. In this paper, we introduce a probability matrix computation based algorithm for automatically classifying laser scanning point clouds into ’tree’ and ’non-tree’ classes. Our method uses the 3D coordinates of the laser scanning points as input and generates a new point cloud which holds a label for each point indicating if it belongs to the ’tree’ or ’non-tree’ class. To do so, a grid surface is assigned to the lowest height level of the point cloud. The grids are filled with probability values which are calculated by checking the point density above the grid. Since the tree trunk locations appear with very high values in the probability matrix, selecting the local maxima of the grid surface help to detect the tree trunks. Further points are assigned to tree trunks if they appear in the close proximity of trunks. Since heavy mathematical computations (such as point cloud organization, detailed shape 3D detection methods, graph network generation) are not required, the proposed algorithm works very fast compared to the existing methods. The tree classification results are found reliable even on point clouds of cities containing many different objects. As the most significant weakness, false detection of light poles, traffic signs and other objects close to trees cannot be prevented. Nevertheless, the experimental results on mobile and airborne laser scanning point clouds indicate the possible usage of the algorithm as an important step for tree growth observation, tree counting and similar applications. While the laser scanning point cloud is giving opportunity to classify even very small trees, accuracy of the results is reduced in the low point density areas further away than the scanning location. These advantages and disadvantages of two laser scanning point cloud sources are discussed in detail.

Sign in / Sign up

Export Citation Format

Share Document