scholarly journals Automatic Extraction and Labelling of Memorial Objects From 3D Point Clouds

2021 ◽  
Vol 4 (1) ◽  
pp. 79-93
Author(s):  
Nicholas Arnold ◽  
Plamen Angelov ◽  
Tim Viney ◽  
Peter Atkinson
Keyword(s):  
Point Clouds ◽  
Automatic Extraction ◽  
3D Point Clouds

scholarly journals SEMANTIC ENRICHMENT OF POINT CLOUD BY AUTOMATIC EXTRACTION AND ENHANCEMENT OF 360° PANORAMAS

2019 ◽  
Vol XLII-2/W17 ◽  
pp. 355-362 ◽  
Author(s):  
A. Tabkha ◽  
R. Hajji ◽  
R. Billen ◽  
F. Poux
Keyword(s):  
Point Cloud ◽  
State Of The Art ◽  
Point Clouds ◽  
Automatic Process ◽  
Automatic Extraction ◽  
Learning Approaches ◽  
Semantic Enrichment ◽  
3D Point Clouds ◽  
Important Challenge

Abstract. The raw nature of point clouds is an important challenge for their direct exploitation in architecture, engineering and construction applications. Particularly, their lack of semantics hinders their utility for automatic workflows (Poux, 2019). In addition, the volume and the irregularity of the structure of point clouds makes it difficult to directly and automatically classify datasets efficiently, especially when compared to the state-of-the art 2D raster classification. Recently, with the advances in deep learning models such as convolutional neural networks (CNNs) , the performance of image-based classification of remote sensing scenes has improved considerably (Chen et al., 2018; Cheng et al., 2017). In this research, we examine a simple and innovative approach that represent large 3D point clouds through multiple 2D projections to leverage learning approaches based on 2D images. In other words, the approach in this study proposes an automatic process for extracting 360° panoramas, enhancing these to be able to leverage raster data to obtain domain-base semantic enrichment possibilities. Indeed, it is very important to obtain a rigorous characterization for use in the classification of a point cloud. Especially because there is a very large variety of 3D point cloud domain applications. In order to test the adequacy of the method and its potential for generalization, several tests were performed on different datasets. The developed semantic augmentation algorithm uses only the attributes X, Y, Z and camera positions as inputs.

Automatic extraction of blocks from 3D point clouds of fractured rock

2017 ◽  
Vol 109 ◽  
pp. 149-161 ◽  
Author(s):  
Na Chen ◽  
John Kemeny ◽  
Qinghui Jiang ◽  
Zhiwen Pan
Keyword(s):  
Fractured Rock ◽  
Point Clouds ◽  
Automatic Extraction ◽  
3D Point Clouds

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

A method for calculating the leaf inclination of soybean canopy based on 3D point clouds

2021 ◽  
Vol 42 (15) ◽  
pp. 5721-5742
Author(s):  
Zhichao Zhang ◽  
Xiaodan Ma ◽  
Haiou Guan ◽  
Kexin Zhu ◽  
Jiarui Feng ◽  
...  
Keyword(s):  
Point Clouds ◽  
3D Point Clouds ◽  
Leaf Inclination ◽  
Soybean Canopy

scholarly journals Seismic Damage Semantics on Post-Earthquake LOD3 Building Models Generated by UAS

2021 ◽  
Vol 10 (5) ◽  
pp. 345
Author(s):  
Konstantinos Chaidas ◽  
George Tataris ◽  
Nikolaos Soulakellis
Keyword(s):  
Point Clouds ◽  
Recovery Phase ◽  
Seismic Damage ◽  
Unmanned Aircraft ◽  
Earthquake Scenario ◽  
Geography Markup Language ◽  
3D Point Clouds ◽  
Building Models ◽  
Building Walls ◽  
Oblique Images

In a post-earthquake scenario, the semantic enrichment of 3D building models with seismic damage is crucial from the perspective of disaster management. This paper aims to present the methodology and the results for the Level of Detail 3 (LOD3) building modelling (after an earthquake) with the enrichment of the semantics of the seismic damage based on the European Macroseismic Scale (EMS-98). The study area is the Vrisa traditional settlement on the island of Lesvos, Greece, which was affected by a devastating earthquake of Mw = 6.3 on 12 June 2017. The applied methodology consists of the following steps: (a) unmanned aircraft systems (UAS) nadir and oblique images are acquired and photogrammetrically processed for 3D point cloud generation, (b) 3D building models are created based on 3D point clouds and (c) 3D building models are transformed into a LOD3 City Geography Markup Language (CityGML) standard with enriched semantics of the related seismic damage of every part of the building (walls, roof, etc.). The results show that in following this methodology, CityGML LOD3 models can be generated and enriched with buildings’ seismic damage. These models can assist in the decision-making process during the recovery phase of a settlement as well as be the basis for its monitoring over time. Finally, these models can contribute to the estimation of the reconstruction cost of the buildings.

scholarly journals Canopy Parameter Estimation of Citrus grandis var. Longanyou Based on LiDAR 3D Point Clouds

2021 ◽  
Vol 13 (9) ◽  
pp. 1859
Author(s):  
Xiangyang Liu ◽  
Yaxiong Wang ◽  
Feng Kang ◽  
Yang Yue ◽  
Yongjun Zheng
Keyword(s):  
Convex Hull ◽  
Clustering Algorithm ◽  
Plant Protection ◽  
Point Clouds ◽  
Biomass Estimation ◽  
Reconstruction Method ◽  
Volume Calculation ◽  
Alpha Shape ◽  
3D Point Clouds ◽  
Canopy Volume

The characteristic parameters of Citrus grandis var. Longanyou canopies are important when measuring yield and spraying pesticides. However, the feasibility of the canopy reconstruction method based on point clouds has not been confirmed with these canopies. Therefore, LiDAR point cloud data for C. grandis var. Longanyou were obtained to facilitate the management of groves of this species. Then, a cloth simulation filter and European clustering algorithm were used to realize individual canopy extraction. After calculating canopy height and width, canopy reconstruction and volume calculation were realized using six approaches: by a manual method and using five algorithms based on point clouds (convex hull, CH; convex hull by slices; voxel-based, VB; alpha-shape, AS; alpha-shape by slices, ASBS). ASBS is an innovative algorithm that combines AS with slices optimization, and can best approximate the actual canopy shape. Moreover, the CH algorithm had the shortest run time, and the R2 values of VCH, VVB, VAS, and VASBS algorithms were above 0.87. The volume with the highest accuracy was obtained from the ASBS algorithm, and the CH algorithm had the shortest computation time. In addition, a theoretical but preliminarily system suitable for the calculation of the canopy volume of C. grandis var. Longanyou was developed, which provides a theoretical reference for the efficient and accurate realization of future functional modules such as accurate plant protection, orchard obstacle avoidance, and biomass estimation.

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