scholarly journals AUTOMATIC MODELLING OF RUBBLE MOUND BREAKWATERS FROM LIDAR DATA

2015 ◽  
Vol XL-3/W3 ◽  
pp. 9-13 ◽  
Author(s):  
M. Bueno ◽  
L. Díaz-Vilariño ◽  
H. González-Jorge ◽  
J. Martínez-Sánchez ◽  
P. Arias
Keyword(s):  
Collision Detection ◽  
Point Cloud ◽  
Accuracy Assessment ◽  
Computing Time ◽  
Point Clouds ◽  
Edge Length ◽  
Normal Vector ◽  
Normal Orientation ◽  
Rubble Mound ◽  
Rubble Mound Breakwaters

Rubble mound breakwaters maintenance is critical to the protection of beaches and ports. LiDAR systems provide accurate point clouds from the emerged part of the structure that can be modelled to make it more useful and easy to handle. This work introduces a methodology for the automatic modelling of breakwaters with armour units of cube shape. The algorithm is divided in three main steps: normal vector computation, plane segmentation, and cube reconstruction. Plane segmentation uses the normal orientation of the points and the edge length of the cube. Cube reconstruction uses the intersection of three perpendicular planes and the edge length. Three point clouds cropped from the main point cloud of the structure are used for the tests. The number of cubes detected is around 56 % for two of the point clouds and 32 % for the third one over the total physical cubes. Accuracy assessment is done by comparison with manually drawn cubes calculating the differences between the vertexes. It ranges between 6.4 cm and 15 cm. Computing time ranges between 578.5 s and 8018.2 s. The computing time increases with the number of cubes and the requirements of collision detection.

scholarly journals Accuracy assessment of building point clouds automatically generated from iphone images

2014 ◽  
Vol XL-5 ◽  
pp. 547-552 ◽  
Author(s):  
B. Sirmacek ◽  
R. Lindenbergh
Keyword(s):  
Measurement Errors ◽  
Point Cloud ◽  
Model Updating ◽  
Accuracy Assessment ◽  
Low Cost ◽  
Point Clouds ◽  
3D Models ◽  
Map Updating ◽  
Cloud Points ◽  
Point To Point

Low-cost sensor generated 3D models can be useful for quick 3D urban model updating, yet the quality of the models is questionable. In this article, we evaluate the reliability of an automatic point cloud generation method using multi-view iPhone images or an iPhone video file as an input. We register such automatically generated point cloud on a TLS point cloud of the same object to discuss accuracy, advantages and limitations of the iPhone generated point clouds. For the chosen example showcase, we have classified 1.23% of the iPhone point cloud points as outliers, and calculated the mean of the point to point distances to the TLS point cloud as 0.11 m. Since a TLS point cloud might also include measurement errors and noise, we computed local noise values for the point clouds from both sources. Mean (μ) and standard deviation (&amp;sigma;) of roughness histograms are calculated as (μ<sub>1</sub> = 0.44 m., &amp;sigma;<sub>1</sub> = 0.071 m.) and (μ<sub>2</sub> = 0.025 m., &amp;sigma;<sub>2</sub> = 0.037 m.) for the iPhone and TLS point clouds respectively. Our experimental results indicate possible usage of the proposed automatic 3D model generation framework for 3D urban map updating, fusion and detail enhancing, quick and real-time change detection purposes. However, further insights should be obtained first on the circumstances that are needed to guarantee a successful point cloud generation from smartphone images.

scholarly journals COMPARISON OF UAS-BASED PHOTOGRAMMETRY SOFTWARE FOR 3D POINT CLOUD GENERATION: A SURVEY OVER A HISTORICAL SITE

2017 ◽  
Vol IV-4/W4 ◽  
pp. 55-61 ◽  
Author(s):  
F. Alidoost ◽  
H. Arefi
Keyword(s):  
Point Cloud ◽  
Accuracy Assessment ◽  
Point Clouds ◽  
Surface Model ◽  
Geospatial Information ◽  
Software Packages ◽  
Comparison Results ◽  
3D City Modelling ◽  
Time Acquisition

Nowadays, Unmanned Aerial System (UAS)-based photogrammetry offers an affordable, fast and effective approach to real-time acquisition of high resolution geospatial information and automatic 3D modelling of objects for numerous applications such as topography mapping, 3D city modelling, orthophoto generation, and cultural heritages preservation. In this paper, the capability of four different state-of-the-art software packages as 3DSurvey, Agisoft Photoscan, Pix4Dmapper Pro and SURE is examined to generate high density point cloud as well as a Digital Surface Model (DSM) over a historical site. The main steps of this study are including: image acquisition, point cloud generation, and accuracy assessment. The overlapping images are first captured using a quadcopter and next are processed by different software to generate point clouds and DSMs. In order to evaluate the accuracy and quality of point clouds and DSMs, both visual and geometric assessments are carry out and the comparison results are reported.

scholarly journals Accuracy Assessment of Deep Learning Based Classification of LiDAR and UAV Points Clouds for DTM Creation and Flood Risk Mapping

Geosciences ◽  
2019 ◽  
Vol 9 (7) ◽  
pp. 323 ◽  
Author(s):  
Gordana Jakovljevic ◽  
Miro Govedarica ◽  
Flor Alvarez-Taboada ◽  
Vladimir Pajic
Keyword(s):  
Deep Learning ◽  
Flood Risk ◽  
Point Cloud ◽  
Accuracy Assessment ◽  
Point Clouds ◽  
Water Levels ◽  
Cloud Classification ◽  
Ground Point ◽  
Flood Risk Mapping ◽  
Point Cloud Classification

Digital elevation model (DEM) has been frequently used for the reduction and management of flood risk. Various classification methods have been developed to extract DEM from point clouds. However, the accuracy and computational efficiency need to be improved. The objectives of this study were as follows: (1) to determine the suitability of a new method to produce DEM from unmanned aerial vehicle (UAV) and light detection and ranging (LiDAR) data, using a raw point cloud classification and ground point filtering based on deep learning and neural networks (NN); (2) to test the convenience of rebalancing datasets for point cloud classification; (3) to evaluate the effect of the land cover class on the algorithm performance and the elevation accuracy; and (4) to assess the usability of the LiDAR and UAV structure from motion (SfM) DEM in flood risk mapping. In this paper, a new method of raw point cloud classification and ground point filtering based on deep learning using NN is proposed and tested on LiDAR and UAV data. The NN was trained on approximately 6 million points from which local and global geometric features and intensity data were extracted. Pixel-by-pixel accuracy assessment and visual inspection confirmed that filtering point clouds based on deep learning using NN is an appropriate technique for ground classification and producing DEM, as for the test and validation areas, both ground and non-ground classes achieved high recall (>0.70) and high precision values (>0.85), which showed that the two classes were well handled by the model. The type of method used for balancing the original dataset did not have a significant influence in the algorithm accuracy, and it was suggested not to use any of them unless the distribution of the generated and real data set will remain the same. Furthermore, the comparisons between true data and LiDAR and a UAV structure from motion (UAV SfM) point clouds were analyzed, as well as the derived DEM. The root mean square error (RMSE) and the mean average error (MAE) of the DEM were 0.25 m and 0.05 m, respectively, for LiDAR data, and 0.59 m and –0.28 m, respectively, for UAV data. For all land cover classes, the UAV DEM overestimated the elevation, whereas the LIDAR DEM underestimated it. The accuracy was not significantly different in the LiDAR DEM for the different vegetation classes, while for the UAV DEM, the RMSE increased with the height of the vegetation class. The comparison of the inundation areas derived from true LiDAR and UAV data for different water levels showed that in all cases, the largest differences were obtained for the lowest water level tested, while they performed best for very high water levels. Overall, the approach presented in this work produced DEM from LiDAR and UAV data with the required accuracy for flood mapping according to European Flood Directive standards. Although LiDAR is the recommended technology for point cloud acquisition, a suitable alternative is also UAV SfM in hilly areas.

scholarly journals A New Simplification Algorithm for Scattered Point Clouds with Feature Preservation

Symmetry ◽  
2021 ◽  
Vol 13 (3) ◽  
pp. 399
Author(s):  
Miao Gong ◽  
Zhijiang Zhang ◽  
Dan Zeng
Keyword(s):  
Data Processing ◽  
Point Cloud ◽  
Point Clouds ◽  
Least Square ◽  
Normal Vector ◽  
Feature Points ◽  
Moving Least Square ◽  
Redundant Data ◽  
Cloud Models ◽  
Feature Preservation

High-precision and high-density three-dimensional point cloud models usually contain redundant data, which implies extra time and hardware costs in the subsequent data processing stage. To analyze and extract data more effectively, the point cloud must be simplified before data processing. Given that point cloud simplification must be sensitive to features to ensure that more valid information can be saved, in this paper, a new simplification algorithm for scattered point clouds with feature preservation, which can reduce the amount of data while retaining the features of data, is proposed. First, the Delaunay neighborhood of the point cloud is constructed, and then the edge points of the point cloud are extracted by the edge distribution characteristics of the point cloud. Second, the moving least-square method is used to obtain the normal vector of the point cloud and the valley ridge points of the model. Then, potential feature points are identified further and retained on the basis of the discrete gradient idea. Finally, non-feature points are extracted. Experimental results show that our method can be applied to models with different curvatures and effectively avoid the hole phenomenon in the simplification process. To further improve the robustness and anti-noise ability of the method, the neighborhood of the point cloud can be extended to multiple levels, and a balance between simplification speed and accuracy needs to be found.

scholarly journals A Saliency-Based Sparse Representation Method for Point Cloud Simplification

Sensors ◽  
2021 ◽  
Vol 21 (13) ◽  
pp. 4279
Author(s):  
Esmeide Leal ◽  
German Sanchez-Torres ◽  
John W. Branch-Bedoya ◽  
Francisco Abad ◽  
Nallig Leal
Keyword(s):  
Point Cloud ◽  
Feature Vector ◽  
Sparse Matrix ◽  
Point Clouds ◽  
Reconstruction Error ◽  
Normal Vector ◽  
Surface Simplification ◽  
Two Measures ◽  
Representation Method ◽  
Point Cloud Simplification

High-resolution 3D scanning devices produce high-density point clouds, which require a large capacity of storage and time-consuming processing algorithms. In order to reduce both needs, it is common to apply surface simplification algorithms as a preprocessing stage. The goal of point cloud simplification algorithms is to reduce the volume of data while preserving the most relevant features of the original point cloud. In this paper, we present a new point cloud feature-preserving simplification algorithm. We use a global approach to detect saliencies on a given point cloud. Our method estimates a feature vector for each point in the cloud. The components of the feature vector are the normal vector coordinates, the point coordinates, and the surface curvature at each point. Feature vectors are used as basis signals to carry out a dictionary learning process, producing a trained dictionary. We perform the corresponding sparse coding process to produce a sparse matrix. To detect the saliencies, the proposed method uses two measures, the first of which takes into account the quantity of nonzero elements in each column vector of the sparse matrix and the second the reconstruction error of each signal. These measures are then combined to produce the final saliency value for each point in the cloud. Next, we proceed with the simplification of the point cloud, guided by the detected saliency and using the saliency values of each point as a dynamic clusterization radius. We validate the proposed method by comparing it with a set of state-of-the-art methods, demonstrating the effectiveness of the simplification method.

scholarly journals POINT CLOUD DERIVED FROMVIDEO FRAMES: ACCURACY ASSESSMENT IN RELATION TO TERRESTRIAL LASER SCANNINGAND DIGITAL CAMERA DATA

2017 ◽  
Vol XLII-2/W3 ◽  
pp. 217-223 ◽  
Author(s):  
P. Delis ◽  
M. Zacharek ◽  
D. Wierzbicki ◽  
A. Grochala
Keyword(s):  
Data Storage ◽  
Point Cloud ◽  
Laser Scanning ◽  
3D Model ◽  
Accuracy Assessment ◽  
Point Clouds ◽  
Histogram Equalization ◽  
Video Data ◽  
Video Frame ◽  
Video Frames

The use of image sequences in the form of video frames recorded on data storage is very useful in especially when working with large and complex structures. Two cameras were used in this study: Sony NEX-5N (for the test object) and Sony NEX-VG10 E (for the historic building). In both cases, a Sony α f&amp;thinsp;=&amp;thinsp;16&amp;thinsp;mm fixed focus wide-angle lens was used. Single frames with sufficient overlap were selected from the video sequence using an equation for automatic frame selection. In order to improve the quality of the generated point clouds, each video frame underwent histogram equalization and image sharpening. Point clouds were generated from the video frames using the SGM-like image matching algorithm. The accuracy assessment was based on two reference point clouds: the first from terrestrial laser scanning and the second generated based on images acquired using a high resolution camera, the NIKON D800. The performed research has shown, that highest accuracies are obtained for point clouds generated from video frames, for which a high pass filtration and histogram equalization had been performed. Studies have shown that to obtain a point cloud density comparable to TLS, an overlap between subsequent video frames must be 85&amp;thinsp;% or more. Based on the point cloud generated from video data, a parametric 3D model can be generated. This type of the 3D model can be used in HBIM construction.

scholarly journals Integrating Normal Vector Features into an Atrous Convolution Residual Network for LiDAR Point Cloud Classification

2021 ◽  
Vol 13 (17) ◽  
pp. 3427
Author(s):  
Chunjiao Zhang ◽  
Shenghua Xu ◽  
Tao Jiang ◽  
Jiping Liu ◽  
Zhengjun Liu ◽  
...  
Keyword(s):  
Point Cloud ◽  
Spatial Information ◽  
Point Clouds ◽  
Local Feature ◽  
Normal Vector ◽  
Residual Network ◽  
Semantic Classification ◽  
Cloud Classification ◽  
Feature Aggregation ◽  
Point Cloud Classification

LiDAR point clouds are rich in spatial information and can effectively express the size, shape, position, and direction of objects; thus, they have the advantage of high spatial utilization. The point cloud focuses on describing the shape of the external surface of the object itself and will not store useless redundant information to describe the occupation. Therefore, point clouds have become the research focus of 3D data models and are widely used in large-scale scene reconstruction, virtual reality, digital elevation model production, and other fields. Since point clouds have various characteristics, such as disorder, density inconsistency, unstructuredness, and incomplete information, point cloud classification is still complex and challenging. To realize the semantic classification of LiDAR point clouds in complex scenarios, this paper proposes the integration of normal vector features into an atrous convolution residual network. Based on the RandLA-Net network structure, the proposed network integrates the atrous convolution into the residual module to extract global and local features of the point clouds. The atrous convolution can learn more valuable point cloud feature information by expanding the receptive field. Then, the point cloud normal vector is embedded in the local feature aggregation module of the RandLA-Net network to extract local semantic aggregation features. The improved local feature aggregation module can merge the deep features of the point cloud and mine the fine-grained information of the point cloud to improve the model’s segmentation ability in complex scenes. Finally, to resolve the imbalance of the distribution of the various categories of point clouds, the original loss function is optimized by adopting a reweighted method to prevent overfitting so that the network can focus on small target categories in the training process to effectively improve the classification performance. Through the experimental analysis of a Vaihingen (Germany) urban 3D semantic dataset from the ISPRS website, it is verified that the proposed algorithm has a strong generalization ability. The overall accuracy (OA) of the proposed algorithm on the Vaihingen urban 3D semantic dataset reached 97.9%, and the average reached 96.1%. Experiments show that the proposed algorithm fully exploits the semantic features of point clouds and effectively improves the accuracy of point cloud classification.

scholarly journals Rigorous Calibration of UAV-Based LiDAR Systems with Refinement of the Boresight Angles Using a Point-to-Plane Approach

Sensors ◽  
2019 ◽  
Vol 19 (23) ◽  
pp. 5224
Author(s):  
Elizeu Martins de Oliveira Junior ◽  
Daniel Rodrigues dos Santos
Keyword(s):  
Point Cloud ◽  
State Of The Art ◽  
Accuracy Assessment ◽  
Point Clouds ◽  
Navigation Systems ◽  
The Novel ◽  
Positional Accuracy ◽  
Before And After ◽  
Very High ◽  
Calibration Parameters

Advances in micro-electro-mechanical navigation systems and lightweight LIDAR (light detection and ranging) sensors onboard unmanned aerial vehicles (UAVs) provide the feasibility of deriving point clouds with very high and homogeneous point density. However, the deformations caused by numerous sources of errors should be carefully treated. This work presents a rigorous calibration of UAV-based LiDAR systems with refinement of the boresight angles using a point-to-plane approach. Our method is divided into a calibration and a parameter mounting refinement part. It starts with the estimation of the calibration parameters and then refines the boresight angles. The novel contribution of the paper is two-fold. First, we estimate the calibration parameters conditioning the centroid of a plane segmented to lie on its corresponding segmented plane without an additional surveying campaign. Second, we refine the boresight angles using a new point-to-plane model. The proposed method is evaluated by analyzing the accuracy assessment of the adjusted point cloud to point/planar features before and after the proposed method. Compared with the state-of-the-art method, our proposed method achieves better positional accuracy.

scholarly journals LIDAR Point Cloud Registration for Sensing and Reconstruction of Unstructured Terrain

2018 ◽  
Vol 8 (11) ◽  
pp. 2318 ◽  
Author(s):  
Qingyuan Zhu ◽  
Jinjin Wu ◽  
Huosheng Hu ◽  
Chunsheng Xiao ◽  
Wei Chen
Keyword(s):  
Autonomous Vehicles ◽  
Point Cloud ◽  
Laser Scanning ◽  
Point Clouds ◽  
Normal Vector ◽  
Registration Method ◽  
High Noise ◽  
Key Points ◽  
Scanning Lidar ◽  
Multisensor Data

When 3D laser scanning (LIDAR) is used for navigation of autonomous vehicles operated on unstructured terrain, it is necessary to register the acquired point cloud and accurately perform point cloud reconstruction of the terrain in time. This paper proposes a novel registration method to deal with uneven-density and high-noise of unstructured terrain point clouds. It has two steps of operation, namely initial registration and accurate registration. Multisensor data is firstly used for initial registration. An improved Iterative Closest Point (ICP) algorithm is then deployed for accurate registration. This algorithm extracts key points and builds feature descriptors based on the neighborhood normal vector, point cloud density and curvature. An adaptive threshold is introduced to accelerate iterative convergence. Experimental results are given to show that our two-step registration method can effectively solve the uneven-density and high-noise problem in registration of unstructured terrain point clouds, thereby improving the accuracy of terrain point cloud reconstruction.

scholarly journals Impact of UAV Surveying Parameters on Mixed Urban Landuse Surface Modelling

2020 ◽  
Vol 9 (11) ◽  
pp. 656
Author(s):  
Muhammad Hamid Chaudhry ◽  
Anuar Ahmad ◽  
Qudsia Gulzar
Keyword(s):  
Point Cloud ◽  
Large Scale ◽  
Accuracy Assessment ◽  
Computational Cost ◽  
Three Dimensional ◽  
Point Clouds ◽  
Ground Control ◽  
Control Points ◽  
Ground Control Points ◽  
High Computational Cost

Unmanned Aerial Vehicles (UAVs) as a surveying tool are mainly characterized by a large amount of data and high computational cost. This research investigates the use of a small amount of data with less computational cost for more accurate three-dimensional (3D) photogrammetric products by manipulating UAV surveying parameters such as flight lines pattern and image overlap percentages. Sixteen photogrammetric projects with perpendicular flight plans and a variation of 55% to 85% side and forward overlap were processed in Pix4DMapper. For UAV data georeferencing and accuracy assessment, 10 Ground Control Points (GCPs) and 18 Check Points (CPs) were used. Comparative analysis was done by incorporating the median of tie points, the number of 3D point cloud, horizontal/vertical Root Mean Square Error (RMSE), and large-scale topographic variations. The results show that an increased forward overlap also increases the median of the tie points, and an increase in both side and forward overlap results in the increased number of point clouds. The horizontal accuracy of 16 projects varies from ±0.13m to ±0.17m whereas the vertical accuracy varies from ± 0.09 m to ± 0.32 m. However, the lowest vertical RMSE value was not for highest overlap percentage. The tradeoff among UAV surveying parameters can result in high accuracy products with less computational cost.

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