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1
Pu, Xinming, Shu Gan, Xiping Yuan, and Raobo Li. "Feature Analysis of Scanning Point Cloud of Structure and Research on Hole Repair Technology Considering Space-Ground Multi-Source 3D Data Acquisition." Sensors 22, no. 24 (December 8, 2022): 9627. http://dx.doi.org/10.3390/s22249627.
Повний текст джерелаАнотація:
As one of the best means of obtaining the geometry information of special shaped structures, point cloud data acquisition can be achieved by laser scanning or photogrammetry. However, there are some differences in the quantity, quality, and information type of point clouds obtained by different methods when collecting point clouds of the same structure, due to differences in sensor mechanisms and collection paths. Thus, this study aimed to combine the complementary advantages of multi-source point cloud data and provide the high-quality basic data required for structure measurement and modeling. Specifically, low-altitude photogrammetry technologies such as hand-held laser scanners (HLS), terrestrial laser scanners (TLS), and unmanned aerial systems (UAS) were adopted to collect point cloud data of the same special-shaped structure in different paths. The advantages and disadvantages of different point cloud acquisition methods of special-shaped structures were analyzed from the perspective of the point cloud acquisition mechanism of different sensors, point cloud data integrity, and single-point geometric characteristics of the point cloud. Additionally, a point cloud void repair technology based on the TLS point cloud was proposed according to the analysis results. Under the premise of unifying the spatial position relationship of the three point clouds, the M3C2 distance algorithm was performed to extract the point clouds with significant spatial position differences in the same area of the structure from the three point clouds. Meanwhile, the single-point geometric feature differences of the multi-source point cloud in the area with the same neighborhood radius was calculated. With the kernel density distribution of the feature difference, the feature points filtered from the HLS point cloud and the TLS point cloud were fused to enrich the number of feature points in the TLS point cloud. In addition, the TLS point cloud voids were located by raster projection, and the point clouds within the void range were extracted, or the closest points were retrieved from the other two heterologous point clouds, to repair the top surface and façade voids of the TLS point cloud. Finally, high-quality basic point cloud data of the special-shaped structure were generated.
2
Cai, S., W. Zhang, J. Qi, P. Wan, J. Shao, and A. Shen. "APPLICABILITY ANALYSIS OF CLOTH SIMULATION FILTERING ALGORITHM FOR MOBILE LIDAR POINT CLOUD." ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLII-3 (April 30, 2018): 107–11. http://dx.doi.org/10.5194/isprs-archives-xlii-3-107-2018.
Повний текст джерелаАнотація:
Classifying the original point clouds into ground and non-ground points is a key step in LiDAR (light detection and ranging) data post-processing. Cloth simulation filtering (CSF) algorithm, which based on a physical process, has been validated to be an accurate, automatic and easy-to-use algorithm for airborne LiDAR point cloud. As a new technique of three-dimensional data collection, the mobile laser scanning (MLS) has been gradually applied in various fields, such as reconstruction of digital terrain models (DTM), 3D building modeling and forest inventory and management. Compared with airborne LiDAR point cloud, there are some different features (such as point density feature, distribution feature and complexity feature) for mobile LiDAR point cloud. Some filtering algorithms for airborne LiDAR data were directly used in mobile LiDAR point cloud, but it did not give satisfactory results. In this paper, we explore the ability of the CSF algorithm for mobile LiDAR point cloud. Three samples with different shape of the terrain are selected to test the performance of this algorithm, which respectively yields total errors of 0.44 %, 0.77 % and1.20 %. Additionally, large area dataset is also tested to further validate the effectiveness of this algorithm, and results show that it can quickly and accurately separate point clouds into ground and non-ground points. In summary, this algorithm is efficient and reliable for mobile LiDAR point cloud.
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Alsadik, B., M. Gerke, and G. Vosselman. "Visibility analysis of point cloud in close range photogrammetry." ISPRS Annals of Photogrammetry, Remote Sensing and Spatial Information Sciences II-5 (May 28, 2014): 9–16. http://dx.doi.org/10.5194/isprsannals-ii-5-9-2014.
Повний текст джерелаАнотація:
The ongoing development of advanced techniques in photogrammetry, computer vision (CV), robotics and laser scanning to efficiently acquire three dimensional geometric data offer new possibilities for many applications. The output of these techniques in the digital form is often a sparse or dense point cloud describing the 3D shape of an object. Viewing these point clouds in a computerized digital environment holds a difficulty in displaying the visible points of the object from a given viewpoint rather than the hidden points. This visibility problem is a major computer graphics topic and has been solved previously by using different mathematical techniques. However, to our knowledge, there is no study of presenting the different visibility analysis methods of point clouds from a photogrammetric viewpoint. The visibility approaches, which are surface based or voxel based, and the hidden point removal (HPR) will be presented. Three different problems in close range photogrammetry are presented: camera network design, guidance with synthetic images and the gap detection in a point cloud. The latter one introduces also a new concept of gap classification. Every problem utilizes a different visibility technique to show the valuable effect of visibility analysis on the final solution.
4
Liu, Chang, Haiyun Gan, Jialin Li, and Boqing Zhu. "Rasterize the Lidar Point Cloud on The Ground Out Method Optimization Analysis." Journal of Physics: Conference Series 2405, no. 1 (December 1, 2022): 012005. http://dx.doi.org/10.1088/1742-6596/2405/1/012005.
Повний текст джерелаАнотація:
Abstract This paper is mainly aimed at the over-segmentation or under-segmentation phenomenon of the ground point cloud in the moving scene of the unmanned platform to identify the target. This paper proposes an optimized rasterization method to remove the ground point clouds appropriately. First, the partitioned data by quadrant is processed based on the asymmetric distribution of front-to-back and left-to-right point cloud data. Then, the ground estimation of the lowest point is carried out by drilling into the laser layer data, and the connection between the estimated points and the ground detection point cloud data is segmented using the road reflection intensity correction. Next, the ground point cloud detection filtering is optimized using the raster elevation difference. Finally, an obstacle continuum hypothesis model is used to improve the under-segmentation phenomenon that occurs inside the raster. The overall ground point cloud detection filtering effect, the algorithm has a certain degree of universality and ideal ground detection filtering effect within the road, the overall achieve the desired goal.
5
Wu, Youping, and Zhihui Zhou. "Intelligent City 3D Modeling Model Based on Multisource Data Point Cloud Algorithm." Journal of Function Spaces 2022 (July 21, 2022): 1–10. http://dx.doi.org/10.1155/2022/6135829.
Повний текст джерелаАнотація:
With the rapid development of smart cities, intelligent navigation, and autonomous driving, how to quickly obtain 3D spatial information of urban buildings and build a high-precision 3D fine model has become a key problem to be solved. As the two-dimensional mapping results have constrained various needs in people’s social life, coupled with the concept of digital city and advocacy, making three-dimensional, virtualization and actualization become the common pursuit of people’s goals. However, the original point cloud obtained is always incomplete due to reasons such as occlusion during acquisition and data density decreasing with distance, resulting in extracted boundaries that are often incomplete as well. In this paper, based on the study of current mainstream 3D model data organization methods, geographic grids and map service specifications, and other related technologies, an intelligent urban 3D modeling model based on multisource data point cloud algorithm is designed for the two problems of unified organization and expression of urban multisource 3D model data. A point cloud preprocessing process is also designed: point cloud noise reduction and downsampling to ensure the original point cloud geometry structure remain unchanged, while improving the point cloud quality and reducing the number of point clouds. By outputting to a common 3D format, the 3D model constructed in this paper can be applied to many fields such as urban planning and design, architectural landscape design, urban management, emergency disaster relief, environmental protection, and virtual tourism.
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Yu, Ruixuan, and Jian Sun. "Learning Polynomial-Based Separable Convolution for 3D Point Cloud Analysis." Sensors 21, no. 12 (June 19, 2021): 4211. http://dx.doi.org/10.3390/s21124211.
Повний текст джерелаАнотація:
Shape classification and segmentation of point cloud data are two of the most demanding tasks in photogrammetry and remote sensing applications, which aim to recognize object categories or point labels. Point convolution is an essential operation when designing a network on point clouds for these tasks, which helps to explore 3D local points for feature learning. In this paper, we propose a novel point convolution (PSConv) using separable weights learned with polynomials for 3D point cloud analysis. Specifically, we generalize the traditional convolution defined on the regular data to a 3D point cloud by learning the point convolution kernels based on the polynomials of transformed local point coordinates. We further propose a separable assumption on the convolution kernels to reduce the parameter size and computational cost for our point convolution. Using this novel point convolution, a hierarchical network (PSNet) defined on the point cloud is proposed for 3D shape analysis tasks such as 3D shape classification and segmentation. Experiments are conducted on standard datasets, including synthetic and real scanned ones, and our PSNet achieves state-of-the-art accuracies for shape classification, as well as competitive results for shape segmentation compared with previous methods.
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Zhang, Yan, Wenhan Zhao, Bo Sun, Ying Zhang, and Wen Wen. "Point Cloud Upsampling Algorithm: A Systematic Review." Algorithms 15, no. 4 (April 8, 2022): 124. http://dx.doi.org/10.3390/a15040124.
Повний текст джерелаАнотація:
Point cloud upsampling algorithms can improve the resolution of point clouds and generate dense and uniform point clouds, and are an important image processing technology. Significant progress has been made in point cloud upsampling research in recent years. This paper provides a comprehensive survey of point cloud upsampling algorithms. We classify existing point cloud upsampling algorithms into optimization-based methods and deep learning-based methods, and analyze the advantages and limitations of different algorithms from a modular perspective. In addition, we cover some other important issues such as public datasets and performance evaluation metrics. Finally, we conclude this survey by highlighting several future research directions and open issues that should be further addressed.
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Pan, Liang, Pengfei Wang, and Chee-Meng Chew. "PointAtrousNet: Point Atrous Convolution for Point Cloud Analysis." IEEE Robotics and Automation Letters 4, no. 4 (October 2019): 4035–41. http://dx.doi.org/10.1109/lra.2019.2927948.
Повний текст джерела
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Ahmad, N., S. Azri, U. Ujang, M. G. Cuétara, G. M. Retortillo, and S. Mohd Salleh. "COMPARATIVE ANALYSIS OF VARIOUS CAMERA INPUT FOR VIDEOGRAMMETRY." ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLII-4/W16 (October 1, 2019): 63–70. http://dx.doi.org/10.5194/isprs-archives-xlii-4-w16-63-2019.
Повний текст джерелаАнотація:
Abstract. Videogrammetry is a technique to generate point clouds by using video frame sequences. It is a branch of photogrammetry that offers an attractive capabilities and make it an interesting choice for a 3D data acquisition. However, different camera input and specification will produce different quality of point cloud. Thus, it is the aim of this study to investigate the quality of point cloud that is produced from various camera input and specification. Several devices are using in this study such as Iphone 5s, Iphone 7+, Iphone X, Digital camera of Casio Exilim EX-ZR1000 and Nikon D7000 DSLR. For each device, different camera with different resolution and frame per second (fps) are used for video recording. The videos are processed using EyesCloud3D by eCapture. EyesCloud3D is a platform that receive input such as videos and images to generate point clouds. 3D model is constructed based on generated point clouds. The total number of point clouds produced is analyzed to determine which camera input and specification produce a good 3D model. Besides that, factor of generating number of point clouds is analyzed. Finally, each camera resolution and fps is suggested for certain applications based on generated number of point cloud.
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Xu, Y., Z. Sun, R. Boerner, T. Koch, L. Hoegner, and U. Stilla. "GENERATION OF GROUND TRUTH DATASETS FOR THE ANALYSIS OF 3D POINT CLOUDS IN URBAN SCENES ACQUIRED VIA DIFFERENT SENSORS." ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLII-3 (April 30, 2018): 2009–15. http://dx.doi.org/10.5194/isprs-archives-xlii-3-2009-2018.
Повний текст джерелаАнотація:
In this work, we report a novel way of generating ground truth dataset for analyzing point cloud from different sensors and the validation of algorithms. Instead of directly labeling large amount of 3D points requiring time consuming manual work, a multi-resolution 3D voxel grid for the testing site is generated. Then, with the help of a set of basic labeled points from the reference dataset, we can generate a 3D labeled space of the entire testing site with different resolutions. Specifically, an octree-based voxel structure is applied to voxelize the annotated reference point cloud, by which all the points are organized by 3D grids of multi-resolutions. When automatically annotating the new testing point clouds, a voting based approach is adopted to the labeled points within multiple resolution voxels, in order to assign a semantic label to the 3D space represented by the voxel. Lastly, robust line- and plane-based fast registration methods are developed for aligning point clouds obtained via various sensors. Benefiting from the labeled 3D spatial information, we can easily create new annotated 3D point clouds of different sensors of the same scene directly by considering the corresponding labels of 3D space the points located, which would be convenient for the validation and evaluation of algorithms related to point cloud interpretation and semantic segmentation.
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Boslim, N. I., S. A. Abdul Shukor, S. N. Mohd Isa, and R. Wong. "Performance analysis of different classifiers in segmenting point cloud data." Journal of Physics: Conference Series 2107, no. 1 (November 1, 2021): 012003. http://dx.doi.org/10.1088/1742-6596/2107/1/012003.
Повний текст джерелаАнотація:
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.
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Chen, X. H., J. Q. Dai, Y. R. He, and W. W. Ma. "POWER LINE EXTRACTION AND ANALYSIS BASED ON LIDAR." ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLII-3/W10 (February 7, 2020): 91–96. http://dx.doi.org/10.5194/isprs-archives-xlii-3-w10-91-2020.
Повний текст джерелаАнотація:
Abstract. The traditional electrical power line inspection method has the disadvantages of high labor intensity, low efficiency and long cycle of re-inspection. Airborne LiDAR can quickly obtain the high-precision three-dimensional spatial information of transmission line, and the data which collected by it can make it possible to accurately detect the dangerous points.It is proposed to use the grid method to divide the data into multiple regions for the elevation histogram statistical method to obtain the power line point cloud at the complex mountainous terrain. In the non-ground point data, part of the vegetation point cloud is separated according to the point cloud dimension feature, and then the power line point and the pole point are distinguished according to the density characteristics of the point cloud so as to realize the point cloud classification of the transmission line corridor. On this basis, the power line safety distance detection is carried out on the power line points and vegetation points extracted by the classification, and the early warning analysis of the dangerous points of the transmission line tree barrier is completed. The experimental results show that the method can classify the acquired power line corridor point cloud and extract the complete power line, which effectively eliminates the hidden dangers and has certain practical significance.
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Fateeva, Elizaveta, Vladimir Badenko, Alexandr Fedotov, and Ivan Kochetkov. "System analysis of the quality of meshes in HBIM." MATEC Web of Conferences 170 (2018): 03033. http://dx.doi.org/10.1051/matecconf/201817003033.
Повний текст джерелаАнотація:
Historical Building Information Modelling (HBIM) is nowadays used as a means to collect, store and preserve information about historical buildings and structures. The information is often collected via laser scanning. The resulting point cloud is manipulated and transformed into a polygon mesh, which is a type of model very easy to work with. This paper looks at the problems associated with creating mesh out of point clouds depending on various characteristics in context of façade reconstruction. The study is based on a point cloud recorded via terrestrial laser scanning in downtown Bremen, Germany that contains buildings completed in a number of different architectural styles, allowing to extract multiple architectural features. Analysis of meshes' quality depending on point cloud density was carried out. Conclusions were drawn as to what the rational solutions for effective surface extraction can be for each individual building in question. Recommendations on preprocessing of point clouds were given.
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Yu, W., J. Xi, Z. Wu, W. Lei, C. Zhu, and T. Tang. "A METHOD FOR EXTRACTING SUBSTATION EQUIPMENT BASED ON UAV LASER SCANNING POINT CLOUDS." ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLIV-4/W3-2020 (November 23, 2020): 413–19. http://dx.doi.org/10.5194/isprs-archives-xliv-4-w3-2020-413-2020.
Повний текст джерелаАнотація:
Abstract. Smart grid construction puts higher demands on the construction of 3D models of substations. However, duo to the complex and diverse structures of substation facilities, it is still a challenge to extract the fine three-dimensional structure of the substation facilities from the massive laser point clouds. To solve this problem, this paper proposes a method for extracting substation equipment from laser scanning point clouds. Firstly, in order to improve the processing efficiency and reduce the noises, the regular voxel grid sampling method is used to down-sample the input point cloud. Furthermore, the multi-scale morphological filtering algorithm is used to segment the point cloud into ground points and non-ground points. Based on the non-ground point cloud data, the substation region is extracted using plane detection in point clouds. Then, for the filtered substation point cloud data, a three-dimensional polygon prism segmentation algorithm based on point dimension feature is proposed to extract the substation equipment. Finally, the substation LiDAR point cloud data collected by the UAV laser scanning system is used to verify the algorithm, and the qualitative and quantitative comparison analysis between the detected results and the manually extracted results are carried out. The experimental results show that the proposed method can accurately extract the substation equipment structure from the laser point cloud data. The results are consistent with the manually extracted results, which demonstrate the great potential of the proposed method in substation extraction and power system 3D modelling applications.
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Wang, Yang, and Shunping Xiao. "Affinity-Point Graph Convolutional Network for 3D Point Cloud Analysis." Applied Sciences 12, no. 11 (May 25, 2022): 5328. http://dx.doi.org/10.3390/app12115328.
Повний текст джерелаАнотація:
Efficient learning of 3D shape representation from point cloud is one of the biggest requirements in 3D computer vision. In recent years, convolutional neural networks have achieved great success in 2D image representation learning. However, unlike images that have a Euclidean structure, 3D point clouds are irregular since the neighbors of each node are inconsistent. Many studies have tried to develop various convolutional graph neural networks to overcome this problem and to achieve great results. Nevertheless, these studies simply took the centroid point and its corresponding neighbors as the graph structure, thus ignoring the structural information. In this paper, an Affinity-Point Graph Convolutional Network (AP-GCN) is proposed to learn the graph structure for each reference point. In this method, the affinity between points is first defined using the feature of each point feature. Then, a graph with affinity information is built. After that, the edge-conditioned convolution is performed between the graph vertices and edges to obtain stronger neighborhood information. Finally, the learned information is used for recognition and segmentation tasks. Comprehensive experiments demonstrate that AP-GCN learned much more reasonable features and achieved significant improvements in 3D computer vision tasks such as object classification and segmentation.
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Kumar, A., K. Anders, L. Winiwarter, and B. Höfle. "FEATURE RELEVANCE ANALYSIS FOR 3D POINT CLOUD CLASSIFICATION USING DEEP LEARNING." ISPRS Annals of Photogrammetry, Remote Sensing and Spatial Information Sciences IV-2/W5 (May 29, 2019): 373–80. http://dx.doi.org/10.5194/isprs-annals-iv-2-w5-373-2019.
Повний текст джерелаАнотація:
<p><strong>Abstract.</strong> 3D point clouds acquired by laser scanning and other techniques are difficult to interpret because of their irregular structure. To make sense of this data and to allow for the derivation of useful information, a segmentation of the points in groups, units, or classes fit for the specific use case is required. In this paper, we present a non-end-to-end deep learning classifier for 3D point clouds using multiple sets of input features and compare it with an implementation of the state-of-the-art deep learning framework PointNet++. We first start by extracting features derived from the local normal vector (normal vectors, eigenvalues, and eigenvectors) from the point cloud, and study the result of classification for different local search radii. We extract additional features related to spatial point distribution and use them together with the normal vector-based features. We find that the classification accuracy improves by up to 33% as we include normal vector features with multiple search radii and features related to spatial point distribution. Our method achieves a mean Intersection over Union (mIoU) of 94% outperforming PointNet++’s Multi Scale Grouping by up to 12%. The study presents the importance of multiple search radii for different point cloud features for classification in an urban 3D point cloud scene acquired by terrestrial laser scanning.</p>
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Liu, K., and J. Boehm. "CLASSIFICATION OF BIG POINT CLOUD DATA USING CLOUD COMPUTING." ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XL-3/W3 (August 20, 2015): 553–57. http://dx.doi.org/10.5194/isprsarchives-xl-3-w3-553-2015.
Повний текст джерелаАнотація:
Point cloud data plays an significant role in various geospatial applications as it conveys plentiful information which can be used for different types of analysis. Semantic analysis, which is an important one of them, aims to label points as different categories. In machine learning, the problem is called classification. In addition, processing point data is becoming more and more challenging due to the growing data volume. In this paper, we address point data classification in a big data context. The popular cluster computing framework Apache Spark is used through the experiments and the promising results suggests a great potential of Apache Spark for large-scale point data processing.
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Zhou, Zehao, Yichun Tai, Jianlin Chen, and Zhijiang Zhang. "Local Feature Extraction Network for Point Cloud Analysis." Symmetry 13, no. 2 (February 16, 2021): 321. http://dx.doi.org/10.3390/sym13020321.
Повний текст джерелаАнотація:
Geometric feature extraction of 3D point clouds plays an important role in many 3D computer vision applications such as region labeling, 3D reconstruction, object segmentation, and recognition. However, hand-designed features on point clouds lack semantic information, so cannot meet these requirements. In this paper, we propose local feature extraction network (LFE-Net) which focus on extracting local feature for point clouds analysis. Such geometric features learning from a relation of local points can be used in a variety of shape analysis problems such as classification, part segmentation, and point matching. LFE-Net consists of local geometric relation (LGR) module which aims to learn a high-dimensional local feature to express the relation between points and their neighbors. Benefiting from the additional singular values of local points and hierarchical neural networks, the learned local features are robust to permutation and rigid transformation so that they can be transformed into 3D descriptors. Moreover, we embed prior spatial information of the local points into the sub-features for combining features from multiple levels. LFE-Net achieves state-of-the-art performances on standard benchmarks including ModelNet40, ShapeNetPart.
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Pang, Lei, Dayuan Liu, Conghua Li, and Fengli Zhang. "Automatic Registration of Homogeneous and Cross-Source TomoSAR Point Clouds in Urban Areas." Sensors 23, no. 2 (January 11, 2023): 852. http://dx.doi.org/10.3390/s23020852.
Повний текст джерелаАнотація:
Building reconstruction using high-resolution satellite-based synthetic SAR tomography (TomoSAR) is of great importance in urban planning and city modeling applications. However, since the imaging mode of SAR is side-by-side, the TomoSAR point cloud of a single orbit cannot achieve a complete observation of buildings. It is difficult for existing methods to extract the same features, as well as to use the overlap rate to achieve the alignment of the homologous TomoSAR point cloud and the cross-source TomoSAR point cloud. Therefore, this paper proposes a robust alignment method for TomoSAR point clouds in urban areas. First, noise points and outlier points are filtered by statistical filtering, and density of projection point (DoPP)-based projection is used to extract TomoSAR building point clouds and obtain the facade points for subsequent calculations based on density clustering. Subsequently, coarse alignment of source and target point clouds was performed using principal component analysis (PCA). Lastly, the rotation and translation coefficients were calculated using the angle of the normal vector of the opposite facade of the building and the distance of the outer end of the facade projection. The experimental results verify the feasibility and robustness of the proposed method. For the homologous TomoSAR point cloud, the experimental results show that the average rotation error of the proposed method was less than 0.1°, and the average translation error was less than 0.25 m. The alignment accuracy of the cross-source TomoSAR point cloud was evaluated for the defined angle and distance, whose values were less than 0.2° and 0.25 m.
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Xu, Y., D. Yue, and P. He. "POINT CLOUD SEGMENTATION OF GULLY BASED ON CHARACTERISTIC DIFFERENCE USING AIRBORNE LIDAR DATA." ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLII-2/W7 (September 12, 2017): 307–11. http://dx.doi.org/10.5194/isprs-archives-xlii-2-w7-307-2017.
Повний текст джерелаАнотація:
The point cloud segmentation of gullies with high accuracy lays the groundwork for the gully parameter extraction and developing models. A point cloud segmentation method of gullies based on characteristic difference from airborne LIDAR is proposed. Firstly, point cloud characteristics of gullies are discussed, and then differences in surface features are obtained based on different scales after preprocessing of the point cloud. Initial gullies are segmented from point clouds combined with a curvature threshold. Finally, real gully point clouds are obtained based on the clustering analysis. The experimental results demonstrate that gullies can be detected accurately with airborne LIDAR point clouds, and this method provides a new idea for quantitative evaluation of gullies.
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Cuong, Cao Xuan, Le Van Canh, Pham Van Chung, Le Duc Tinh, Pham Trung Dung, and Ngo Sy Cuong. "Quality assessment of 3D point cloud of industrial buildings from imagery acquired by oblique and nadir UAV flights." Naukovyi Visnyk Natsionalnoho Hirnychoho Universytetu, no. 5 (2020): 131–39. http://dx.doi.org/10.33271/nvngu/2021-5/131.
Повний текст джерелаАнотація:
Purpose. The main objective of this paper is to assess the quality of the 3D model of industrial buildings generated from Unmanned Aerial Vehicle (UAV) imagery datasets, including nadir (N), oblique (O), and Nadir and Oblique (N+O) UAV datasets. Methodology. The quality of a 3D model is defined by the accuracy and density of point clouds created from UAV images. For this purpose, the UAV was deployed to acquire images with both O and N flight modes over an industrial mining area containing a mine shaft tower, factory housing and office buildings. The quality assessment was conducted for the 3D point cloud model of three main objects such as roofs, facades, and ground surfaces using CheckPoints (CPs) and terrestrial laser scanning (TLS) point clouds as the reference datasets. The Root Mean Square Errors (RMSE) were calculated using CP coordinates, and cloud to cloud distances were computed using TLS point clouds, which were used for the accuracy assessment. Findings. The results showed that the point cloud model generated by the N flight mode was the most accurate but least dense, whereas that of the O mode was the least accurate but most detailed level in comparison with the others. Also, the combination of O and N datasets takes advantages of individual mode as the point clouds accuracy is higher than that of case O, and its density is much higher than that of case N. Therefore, it is optimal to build exceptional accurate and dense point clouds of buildings. Originality. The paper provides a comparative analysis in quality of point cloud of roofs and facades generated from UAV photogrammetry for mining industrial buildings. Practical value. Findings of the study can be used as references for both UAV survey practices and applications of UAV point cloud. The paper provides useful information for making UAV flight planning, or which UAV points should be integrated into TLS points to have the best point cloud.
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Leslar, M., B. Hu, and J. G. Wang. "Error Analysis of a Mobile Terrestrial LiDAR System." GEOMATICA 68, no. 3 (September 2014): 183–94. http://dx.doi.org/10.5623/cig2014-303.
Повний текст джерелаАнотація:
The understanding of the effects of error on Mobile Terrestrial LiDAR (MTL) point clouds has not increased with their popularity. In this study, comprehensive error analyses based on error propagation theory and global sensitivity study were carried out to quantitatively describe the effects of various error sources in a MTL system on the point cloud. Two scenarios were envisioned; the first using the uncertainties for measurement and calibration variables that are normally expected for MTL systems as they exist today, and the second using an ideal situation where measurement and calibration values have been well adjusted. It was found that the highest proportion of error in the point cloud can be attributed to the boresight and lever arm parameters for MTL systems calibrated using non-rigours methods. In particular, under a loosely controlled error condition, the LiDAR to INS Z lever arm and the LiDAR to INS roll angle contributed more error in the output point cloud than any other parameter, including the INS position. Under tightly controlled error conditions, the INS position became the dominant source of error in the point cloud. In addition, conditional variance analysis has shown that the majority of the error in a point cloud can be attributed to the individual variables. Errors caused by the interactions between the diverse variables are minimal and can be regarded as insignificant.
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Lin, Xiuyun, Yulin Gong, Yuan Sun, Jiawen Jiang, Yanli Zhang, and Xiaorong Wen. "Analysis of Dynamic Forest Structures Based on Hierarchical Features Extracted from Multi-Station LiDAR Scanning." Environmental Sciences Proceedings 3, no. 1 (November 11, 2020): 21. http://dx.doi.org/10.3390/iecf2020-07871.
Повний текст джерелаАнотація:
This study aims at searching for characteristic parameters of tree trunks to establish a volume model and dynamic analysis of volume based on terrestrial laser scanning (TLS). We collected three phases of data over 5 years from an artificial Liriodendron chinense forest. The upper diameters of the tree stump and tree height data were obtained by using the multi-station scanning method. A novel hierarchical TLS point cloud feature named the height cumulative percentage (Hz%) was designed. The shape of the upper tree trunk extracted by the point cloud was equivalent to that of the analytical tree with inflection points at 25% and 50% of the height, and the dynamic volume change of the model, which was established by hierarchical features, was highly related to the volume change of the actual point cloud extraction. The obtained results reflected the fact that the Hz% value provided by multi-station scanning was closely related to the characteristic stumpage parameters and could be used to invert the dynamic forest structure. The volume model established based on point cloud hierarchical parameters in this study could be used to monitor the dynamic changes of forest volume and to provide a new reference for applying TLS point clouds for the dynamic monitoring of forest resources.
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Siewczyńska, Monika, and Tomasz Zioło. "Analysis of the Applicability of Photogrammetry in Building Façade." Civil and Environmental Engineering Reports 32, no. 3 (September 1, 2022): 182–206. http://dx.doi.org/10.2478/ceer-2022-0035.
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Abstract This article evaluates the accuracy of 3D models made from point clouds obtained from photogrammetry. Photographs were taken from ground level and using a drone, and data processing was performed in 3DF Zephyr. The models were compared with the actual dimensions of the buildings. Four different building objects with varying degrees of complexity were analysed. The aim of the research is to analyse the conditions for taking photographs and how they are transformed into a point cloud, and to see how and whether the complexity of the shape of the facade affects the accuracy of the 3D model made from the point cloud. The inaccuracy of the point cloud in the form of point spread for all analysed cases was 1.8±0.4 cm on average. The largest measurement error was found in the case of a multi-storey building. Despite the presented inaccuracies, it was considered advantageous to use the point cloud obtained through photogrammetry in the inventory. No difference was observed in the accuracy of the model depending on the complexity of the building. Recommendations were made regarding the conditions for taking photographs.
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Wang, Wenxin, Changming Zhao, and Haiyang Zhang. "Accurate Extraction of Cableways Based on the LS-PCA Combination Analysis Method." Applied Sciences 13, no. 5 (February 23, 2023): 2875. http://dx.doi.org/10.3390/app13052875.
Повний текст джерелаАнотація:
In order to maintain a ski resort efficiently, regular inspections of the cableways are essential. However, there are some difficulties in discovering and observing the cable car cableways in the ski resort. This paper proposes a high-precision segmentation and extraction method based on the 3D laser point cloud data collected by airborne lidar to address these problems. In this method, first, an elevation filtering algorithm is used to remove ground points and low-height vegetation, followed by preliminary segmentation of the cableway using the spatial distribution characteristics of the point cloud. The ropeway segmentation and extraction are then completed using the least squares-principal component combination analysis method for parameter fitting. Additionally, we selected three samples of data from the National Alpine Ski Center to be used as test objects. The real value is determined by the number of point clouds manually deducted by CloudCompare. The extraction accuracy is defined as the ratio of the number of point clouds extracted by the algorithm to the number of point clouds manually extracted. While the environmental complexities of the samples differ, the algorithm proposed in this paper is capable of segmenting and extracting cableways with great accuracy, achieving a comprehensive and effective extraction accuracy rate of 90.59%, which is sufficient to meet the project’s requirements.
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Wałach, Daniel, and Grzegorz Piotr Kaczmarczyk. "Application of TLS Remote Sensing Data in the Analysis of the Load-Carrying Capacity of Structural Steel Elements." Remote Sensing 13, no. 14 (July 14, 2021): 2759. http://dx.doi.org/10.3390/rs13142759.
Повний текст джерелаАнотація:
This paper proposes the use of terrestrial laser scanning (TLS) measurements together with finite element method (FEM) numerical modeling to assess the current technical condition. The main aim of the paper was to evaluate the effect of point cloud size reduction on the quality of the geometric model and the ability to represent the corrosion level in assessing its load-carrying capacity. In this study, a standard scanning was performed on a historical object and a point cloud of a selected corroded element was generated. In order to further process the data, gradual reductions were made in the number of points from which meshes representing the geometry of the selected beam were created. Inaccuracy analyses of the meshes generated on the reduced point clouds were performed. Numerical analysis was then conducted for the selected mesh generated from the reduced point cloud. The results identified the locations of maximum stresses. The presented analysis showed that by developing the presented measurement and computational technique, laser scanning can be used to determine the degree of corrosion of hard-to-reach steel elements.
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Mayr, A., M. Rutzinger, and C. Geitner. "MULTITEMPORAL ANALYSIS OF OBJECTS IN 3D POINT CLOUDS FOR LANDSLIDE MONITORING." ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLII-2 (May 30, 2018): 691–97. http://dx.doi.org/10.5194/isprs-archives-xlii-2-691-2018.
Повний текст джерелаАнотація:
To date multi-temporal 3D point clouds from close-range sensing are used for landslide and erosion monitoring in an operational manner. Morphological changes are typically derived by calculating distances between points from different acquisition epochs. The identification of the underlying processes resulting in surface changes, however, is often challenging, for example due to the complex surface structures and influences from seasonal vegetation dynamics. We present an approach for object-based 3D landslide monitoring based on topographic LiDAR point cloud time series separating specific surface change types automatically. The workflow removes vegetation and relates surface changes derived from a point cloud time series directly to (i) geomorphological object classes (landslide scarp, eroded area, deposit) and (ii) to individual, spatially contiguous objects (such as parts of the landslide scarp and clods of material moving in the landslide). We apply this approach to a time series of nine point cloud epochs from a slope affected by two shallow landslides. A parameter test addresses the influence of the registration error and the associated level of detection on the magnitude of derived object changes. The results of our case study are in accordance with field observations at the test site as well as conceptual landslide models, where retrogressive erosion of the scarp and downslope movement of the sliding mass are major principles of secondary landslide development. We conclude that the presented methods are well suited to extract information on geomorphological process dynamics from the complex point clouds and aggregate it at different levels of abstraction to assist landslide and erosion assessment.
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Zhang, Jie, Jian Liu, Xiuping Liu, Jiang Wei, Junjie Cao, and Kewei Tang. "Feature interpolation convolution for point cloud analysis." Computers & Graphics 99 (October 2021): 182–91. http://dx.doi.org/10.1016/j.cag.2021.06.015.
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João, Prazeres, Pereira Manuela, and Pinheiro Antonio. "Quality analysis of point cloud coding solutions." Electronic Imaging 34, no. 17 (January 16, 2022): 225–1. http://dx.doi.org/10.2352/ei.2022.34.17.3dia-225.
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Vallet, Bruno, Mathieu Brédif, Andres Serna, Beatriz Marcotegui, and Nicolas Paparoditis. "TerraMobilita/iQmulus urban point cloud analysis benchmark." Computers & Graphics 49 (June 2015): 126–33. http://dx.doi.org/10.1016/j.cag.2015.03.004.
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Chazal, Frédéric, Leonidas J. Guibas, Steve Y. Oudot, and Primoz Skraba. "Scalar Field Analysis over Point Cloud Data." Discrete & Computational Geometry 46, no. 4 (May 17, 2011): 743–75. http://dx.doi.org/10.1007/s00454-011-9360-x.
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Dayal, K. R., S. Raghavendra, H. Pande, P. S. Tiwari, and I. Chauhan. "COMPARATIVE ANALYSIS OF 3D POINT CLOUDS GENERATED FROM A FREEWARE AND TERRESTRIAL LASER SCANNER." ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLII-4/W2 (July 5, 2017): 67–71. http://dx.doi.org/10.5194/isprs-archives-xlii-4-w2-67-2017.
Повний текст джерелаАнотація:
In the recent past, several heritage structures have faced destruction due to both human-made incidents and natural calamities that have caused a great loss to the human race regarding its cultural achievements. In this context, the importance of documenting such structures to create a substantial database cannot be emphasised enough. The Clock Tower of Dehradun, India is one such structure. There is a lack of sufficient information in the digital domain, which justified the need to carry out this study. Thus, an attempt has been made to gauge the possibilities of using open source 3D tools such as VSfM to quickly and easily obtain point clouds of an object and assess its quality. The photographs were collected using consumer grade cameras with reasonable effort to ensure overlap. The sparse reconstruction and dense reconstruction were carried out to generate a 3D point cloud model of the tower. A terrestrial laser scanner (TLS) was also used to obtain a point cloud of the tower. The point clouds obtained from the two methods were analyzed to understand the quality of the information present; TLS acquired point cloud being a benchmark to assess the VSfM point cloud. They were compared to analyze the point density and subjected to a plane-fitting test for sample flat portions on the structure. The plane-fitting test revealed the <q>planarity</q> of the point clouds. A Gauss distribution fit yielded a standard deviation of 0.002 and 0.01 for TLS and VSfM, respectively. For more insight, comparisons with Agisoft Photoscan results were also made.
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Liang, Xin He, and Jian Wei Liu. "Accuracy Analysis of Point Cloud Registration Based on Mark Point." Advanced Materials Research 945-949 (June 2014): 2067–70. http://dx.doi.org/10.4028/www.scientific.net/amr.945-949.2067.
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When optical 3D shape measurement equipment works, they gather dense point cloud using mark points as artificial feature for the purpose of global registration; as a result, rough registration error of multiple scans depends primarily on the location accuracy of these mark points. This paper analyses the 3D measuring error distribution law of the mark points in difference direction, proposes that the measurement error in z direction varies as positive proportion with z square, and inversely proportion with distance between two cameras.
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Zhang, Ju, Qingwu Hu, Hongyu Wu, Junying Su, and Pengcheng Zhao. "Application of Fractal Dimension of Terrestrial Laser Point Cloud in Classification of Independent Trees." Fractal and Fractional 5, no. 1 (February 1, 2021): 14. http://dx.doi.org/10.3390/fractalfract5010014.
Повний текст джерелаАнотація:
Tree precise classification and identification of forest species is a core issue of forestry resource monitoring and ecological effect assessment. In this paper, an independent tree species classification method based on fractal features of terrestrial laser point cloud is proposed. Firstly, the terrestrial laser point cloud data of an independent tree is preprocessed to obtain terrestrial point clouds of independent tree canopy. Secondly, the multi-scale box-counting dimension calculation algorithm of independent tree canopy dense terrestrial laser point cloud is proposed. Furthermore, a robust box-counting algorithm is proposed to improve the stability and accuracy of fractal dimension expression of independent tree point cloud, which implementing gross error elimination based on Random Sample Consensus. Finally, the fractal dimension of a dense terrestrial laser point cloud of independent trees is used to classify different types of independent tree species. Experiments on nine independent trees of three types show that the fractal dimension can be stabilized under large density variations, proving that the fractal features of terrestrial laser point cloud can stably express tree species characteristics, and can be used for accurate classification and recognition of forest species.
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Zhang, Chaoyi, Yang Song, Lina Yao, and Weidong Cai. "Shape-Oriented Convolution Neural Network for Point Cloud Analysis." Proceedings of the AAAI Conference on Artificial Intelligence 34, no. 07 (April 3, 2020): 12773–80. http://dx.doi.org/10.1609/aaai.v34i07.6972.
Повний текст джерелаАнотація:
Point cloud is a principal data structure adopted for 3D geometric information encoding. Unlike other conventional visual data, such as images and videos, these irregular points describe the complex shape features of 3D objects, which makes shape feature learning an essential component of point cloud analysis. To this end, a shape-oriented message passing scheme dubbed ShapeConv is proposed to focus on the representation learning of the underlying shape formed by each local neighboring point. Despite this intra-shape relationship learning, ShapeConv is also designed to incorporate the contextual effects from the inter-shape relationship through capturing the long-ranged dependencies between local underlying shapes. This shape-oriented operator is stacked into our hierarchical learning architecture, namely Shape-Oriented Convolutional Neural Network (SOCNN), developed for point cloud analysis. Extensive experiments have been performed to evaluate its significance in the tasks of point cloud classification and part segmentation.
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Wang, Yong, Guohua Geng, Pengbo Zhou, Qi Zhang, Zhan Li, and Ruihang Feng. "GC-MLP: Graph Convolution MLP for Point Cloud Analysis." Sensors 22, no. 23 (December 5, 2022): 9488. http://dx.doi.org/10.3390/s22239488.
Повний текст джерелаАнотація:
With the objective of addressing the problem of the fixed convolutional kernel of a standard convolution neural network and the isotropy of features making 3D point cloud data ineffective in feature learning, this paper proposes a point cloud processing method based on graph convolution multilayer perceptron, named GC-MLP. Unlike traditional local aggregation operations, the algorithm generates an adaptive kernel through the dynamic learning features of points, so that it can dynamically adapt to the structure of the object, i.e., the algorithm first adaptively assigns different weights to adjacent points according to the different relationships between the different points captured. Furthermore, local information interaction is then performed with the convolutional layers through a weight-sharing multilayer perceptron. Experimental results show that, under different task benchmark datasets (including ModelNet40 dataset, ShapeNet Part dataset, S3DIS dataset), our proposed algorithm achieves state-of-the-art for both point cloud classification and segmentation tasks.
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Kumar, K., H. Ledoux, and J. Stoter. "COMPARATIVE ANALYSIS OF DATA STRUCTURES FOR STORING MASSIVE TINS IN A DBMS." ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLI-B2 (June 7, 2016): 123–30. http://dx.doi.org/10.5194/isprs-archives-xli-b2-123-2016.
Повний текст джерелаАнотація:
Point cloud data are an important source for 3D geoinformation. Modern day 3D data acquisition and processing techniques such as airborne laser scanning and multi-beam echosounding generate billions of 3D points for simply an area of few square kilometers. With the size of the point clouds exceeding the billion mark for even a small area, there is a need for their efficient storage and management. These point clouds are sometimes associated with attributes and constraints as well. Storing billions of 3D points is currently possible which is confirmed by the initial implementations in Oracle Spatial SDO PC and the PostgreSQL Point Cloud extension. But to be able to analyse and extract useful information from point clouds, we need more than just points i.e. we require the surface defined by these points in space. There are different ways to represent surfaces in GIS including grids, TINs, boundary representations, etc. In this study, we investigate the database solutions for the storage and management of massive TINs. The classical (face and edge based) and compact (star based) data structures are discussed at length with reference to their structure, advantages and limitations in handling massive triangulations and are compared with the current solution of PostGIS Simple Feature. The main test dataset is the TIN generated from third national elevation model of the Netherlands (AHN3) with a point density of over 10 points/m<sup>2</sup>. PostgreSQL/PostGIS DBMS is used for storing the generated TIN. The data structures are tested with the generated TIN models to account for their geometry, topology, storage, indexing, and loading time in a database. Our study is useful in identifying what are the limitations of the existing data structures for storing massive TINs and what is required to optimise these structures for managing massive triangulations in a database.
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Kumar, K., H. Ledoux, and J. Stoter. "COMPARATIVE ANALYSIS OF DATA STRUCTURES FOR STORING MASSIVE TINS IN A DBMS." ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLI-B2 (June 7, 2016): 123–30. http://dx.doi.org/10.5194/isprsarchives-xli-b2-123-2016.
Повний текст джерелаАнотація:
Point cloud data are an important source for 3D geoinformation. Modern day 3D data acquisition and processing techniques such as airborne laser scanning and multi-beam echosounding generate billions of 3D points for simply an area of few square kilometers. With the size of the point clouds exceeding the billion mark for even a small area, there is a need for their efficient storage and management. These point clouds are sometimes associated with attributes and constraints as well. Storing billions of 3D points is currently possible which is confirmed by the initial implementations in Oracle Spatial SDO PC and the PostgreSQL Point Cloud extension. But to be able to analyse and extract useful information from point clouds, we need more than just points i.e. we require the surface defined by these points in space. There are different ways to represent surfaces in GIS including grids, TINs, boundary representations, etc. In this study, we investigate the database solutions for the storage and management of massive TINs. The classical (face and edge based) and compact (star based) data structures are discussed at length with reference to their structure, advantages and limitations in handling massive triangulations and are compared with the current solution of PostGIS Simple Feature. The main test dataset is the TIN generated from third national elevation model of the Netherlands (AHN3) with a point density of over 10 points/m<sup>2</sup>. PostgreSQL/PostGIS DBMS is used for storing the generated TIN. The data structures are tested with the generated TIN models to account for their geometry, topology, storage, indexing, and loading time in a database. Our study is useful in identifying what are the limitations of the existing data structures for storing massive TINs and what is required to optimise these structures for managing massive triangulations in a database.
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Mwangangi, K. K., P. O. Mc’Okeyo, S. J. Oude Elberink, and F. Nex. "EXPLORING THE POTENTIALS OF UAV PHOTOGRAMMETRIC POINT CLOUDS IN FAÇADE DETECTION AND 3D RECONSTRUCTION OF BUILDINGS." International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLIII-B2-2022 (May 30, 2022): 433–40. http://dx.doi.org/10.5194/isprs-archives-xliii-b2-2022-433-2022.
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Abstract. The use of Airborne Laser Scanner (ALS) point clouds has dominated 3D buildings reconstruction research, thus giving photogrammetric point clouds less attention. Point cloud density, occlusion and vegetation cover are some of the concerns that promote the necessity to understand and question the completeness and correctness of UAV photogrammetric point clouds for 3D buildings reconstruction. This research explores the potentials of modelling 3D buildings from nadir and oblique UAV image data vis a vis airborne laser data. Optimal parameter settings for dense matching and reconstruction are analysed for both UAV image-based and lidar point clouds. This research employs an automatic data driven model approach to 3D building reconstruction. A proper segmentation into planar roof faces is crucial, followed by façade detection to capture the real extent of the buildings’ roof overhang. An analysis of the quality of point density and point noise, in relation to setting parameter indicates that with a minimum of 50 points/m2, most of the planar surfaces are reconstructed comfortably. But for smaller features than dormers on the roof, a denser point cloud than 80 points/m2 is needed. 3D buildings from UAVs point cloud can be improved by enhancing roof boundary by use of edge information from images. It can also be improved by merging the imagery building outlines, point clouds roof boundary and the walls outline to extract the real extent of the building.
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Huang, R., W. Yao, Z. Ye, Y. Xu, and U. Stilla. "RIDF: A ROBUST ROTATION-INVARIANT DESCRIPTOR FOR 3D POINT CLOUD REGISTRATION IN THE FREQUENCY DOMAIN." ISPRS Annals of Photogrammetry, Remote Sensing and Spatial Information Sciences V-2-2020 (August 3, 2020): 235–42. http://dx.doi.org/10.5194/isprs-annals-v-2-2020-235-2020.
Повний текст джерелаАнотація:
Abstract. Registration of point clouds is a fundamental problem in the community of photogrammetry and 3D computer vision. Generally, point cloud registration consists of two steps: the search of correspondences and the estimation of transformation parameters. However, to find correspondences from point clouds, generating robust and discriminative features is of necessity. In this paper, we address the problem of extracting robust rotation-invariant features for fast coarse registration of point clouds under the assumption that the pairwise point clouds are transformed with rigid transformation. With a Fourier-based descriptor, point clouds represented by volumetric images can be mapped from the image to feature space. It is achieved by considering a gradient histogram as a continuous angular signal which can be well represented by the spherical harmonics. The rotation-invariance is established based on the Fourier-based analysis, in which high-frequency signals can be filtered out. This makes the extracted features robust to noises and outliers. Then, with the extracted features, pairwise correspondence can be found by the fast search. Finally, the transformation parameters can be estimated by fitting the rigid transformation model using the corresponding points and RANSAC algorithm. Experiments are conducted to prove the effectiveness of our proposed method in the task of point cloud registration. Regarding the experimental results of the point cloud registration using two TLS benchmark point cloud datasets, featuring with limited overlaps and uneven point densities and covering different urban scenes, our proposed method can achieve a fast coarse registration with rotation errors of less than 1 degree and translation errors of less than 1m.
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Seo, Hyungjoon, Yang Zhao, and Cheng Chen. "Displacement Estimation Error in Laser Scanning Monitoring of Retaining Structures Considering Roughness." Sensors 21, no. 21 (November 5, 2021): 7370. http://dx.doi.org/10.3390/s21217370.
Повний текст джерелаАнотація:
Point clouds were obtained after laser scanning of the concrete panel, SMW, and sheet pile which is most widely used in retaining structures. The surface condition of the point cloud affects the displacement calculation, and hence both local roughness and global curvature of each point cloud were analyzed using the different sizes of the kernel. The curvature of the three retaining structures was also analyzed by the azimuth angle. In this paper, artificial displacements are generated for the point clouds of 100%, 80%, 60%, 40%, and 20% of the retaining structures, and displacement and analysis errors were calculated using the C2C, C2M, and M3C2 methods. C2C method is affected by the resolution of the point cloud, and the C2M method underestimates the displacement by the location of the points in the curvature of the retaining structures. M3C2 method had the lowest error, and the optimized M3C2 parameters for analyzing the displacement were presented.
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Carlsson, Gunnar. "Topological pattern recognition for point cloud data." Acta Numerica 23 (May 2014): 289–368. http://dx.doi.org/10.1017/s0962492914000051.
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In this paper we discuss the adaptation of the methods of homology from algebraic topology to the problem of pattern recognition in point cloud data sets. The method is referred to aspersistent homology, and has numerous applications to scientific problems. We discuss the definition and computation of homology in the standard setting of simplicial complexes and topological spaces, then show how one can obtain useful signatures, called barcodes, from finite metric spaces, thought of as sampled from a continuous object. We present several different cases where persistent homology is used, to illustrate the different ways in which the method can be applied.
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Hidaka, Nao, Takashi Michikawa, Ali Motamedi, Nobuyoshi Yabuki, and Tomohiro Fukuda. "Polygonization of Point Cloud of Tunnels Using Lofting Operation." International Journal of Automation Technology 12, no. 3 (May 1, 2018): 356–68. http://dx.doi.org/10.20965/ijat.2018.p0356.
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This paper proposes a novel method for polygonizing scanned point cloud data of tunnels to feature-preserved polygons to be used for maintenance purposes. The proposed method uses 2D cross-sections of structures and polygonizes them by a lofting operation. In order to extract valid cross-sections from the input point cloud, center lines and orthogonal planes are used. Center lines of the point cloud are extracted using local symmetry analysis. In addition, this research segments a point cloud of a tunnel into lining concrete, road, and other facilities. The results of applying the proposed method to the point clouds of three types of tunnels are demonstrated, and the advantages and limitations of the proposed method are discussed.
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Ruchay, A. N., K. A. Dorofeev, and V. V. Kalschikov. "Accuracy analysis of 3D object reconstruction using point cloud filtering algorithms." Information Technology and Nanotechnology, no. 2391 (2019): 169–74. http://dx.doi.org/10.18287/1613-0073-2019-2391-169-174.
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In this paper, we first analyze the accuracy of 3D object reconstruction using point cloud filtering applied on data from a RGB-D sensor. Point cloud filtering algorithms carry out upsampling for defective point cloud. Various methods of point cloud filtering are tested and compared with respect to the reconstruction accuracy using real data. In order to improve the accuracy of 3D object reconstruction, an efficient method of point cloud filtering is designed. The presented results show an improvement in the accuracy of 3D object reconstruction using the proposed point cloud filtering algorithm.
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Zaczek-Peplinska, Janina, and Maria Kowalska. "Terrestrial laser scanning in monitoring of anthropogenic objects." Geodesy and Cartography 66, no. 2 (December 20, 2017): 347–64. http://dx.doi.org/10.1515/geocart-2017-0011.
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Abstract The registered xyz coordinates in the form of a point cloud captured by terrestrial laser scanner and the intensity values (I) assigned to them make it possible to perform geometric and spectral analyses. Comparison of point clouds registered in different time periods requires conversion of the data to a common coordinate system and proper data selection is necessary. Factors like point distribution dependant on the distance between the scanner and the surveyed surface, angle of incidence, tasked scan’s density and intensity value have to be taken into consideration. A prerequisite for running a correct analysis of the obtained point clouds registered during periodic measurements using a laser scanner is the ability to determine the quality and accuracy of the analysed data. The article presents a concept of spectral data adjustment based on geometric analysis of a surface as well as examples of geometric analyses integrating geometric and physical data in one cloud of points: cloud point coordinates, recorded intensity values, and thermal images of an object. The experiments described here show multiple possibilities of usage of terrestrial laser scanning data and display the necessity of using multi-aspect and multi-source analyses in anthropogenic object monitoring. The article presents examples of multisource data analyses with regard to Intensity value correction due to the beam’s incidence angle. The measurements were performed using a Leica Nova MS50 scanning total station, Z+F Imager 5010 scanner and the integrated Z+F T-Cam thermal camera.
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Gao, Yanfeng, Cicao Ping, Ling Wang, and Binrui Wang. "A Simplification Method for Point Cloud of T-Profile Steel Plate for Shipbuilding." Algorithms 14, no. 7 (June 30, 2021): 202. http://dx.doi.org/10.3390/a14070202.
Повний текст джерелаАнотація:
According to the requirements of point cloud simplification for T-profile steel plate welding in shipbuilding, the disadvantages of the existing simplification algorithms are analyzed. In this paper, a point cloud simplification method is proposed based on octree coding and the threshold of the surface curvature feature. In this method, the original point cloud data are divided into multiple sub-cubes with specified side lengths by octree coding, and the points that are closest to the gravity center of the sub-cube are kept. The k-neighborhood method and the curvature calculation are performed in order to obtain the curvature features of the point cloud. Additionally, the point cloud data are divided into several regions based on the given adjustable curvature threshold. Finally, combining the random sampling method with the simplification method based on the regional gravity center, the T-profile point cloud data can be simplified. In this study, after obtaining the point cloud data of a T-profile plate, the proposed simplification method is compared with some other simplification methods. It is found that the proposed simplification method for the point cloud of the T-profile steel plate for shipbuilding is faster than the three existing simplification methods, while retaining more feature points and having approximately the same reduction rates.
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He, Peipei, Zheng Ma, Meiqi Fei, Wenkai Liu, Guihai Guo, and Mingwei Wang. "A Multiscale Multi-Feature Deep Learning Model for Airborne Point-Cloud Semantic Segmentation." Applied Sciences 12, no. 22 (November 20, 2022): 11801. http://dx.doi.org/10.3390/app122211801.
Повний текст джерелаАнотація:
In point-cloud scenes, semantic segmentation is the basis for achieving an understanding of a 3D scene. The disorderly and irregular nature of 3D point clouds makes it impossible for traditional convolutional neural networks to be applied directly, and most deep learning point-cloud models often suffer from an inadequate utilization of spatial information and of other related point-cloud features. Therefore, to facilitate the capture of spatial point neighborhood information and obtain better performance in point-cloud analysis for point-cloud semantic segmentation, a multiscale, multi-feature PointNet (MSMF-PointNet) deep learning point-cloud model is proposed in this paper. MSMF-PointNet is based on the classical point-cloud model PointNet, and two small feature-extraction networks called Mini-PointNets are added to operate in parallel with the modified PointNet; these additional networks extract multiscale, multi-neighborhood features for classification. In this paper, we use the spherical neighborhood method to obtain the local neighborhood features of the point cloud, and then we adjust the radius of the spherical neighborhood to obtain the multiscale point-cloud features. The obtained multiscale neighborhood feature point set is used as the input of the network. In this paper, a cross-sectional comparison analysis is conducted on the Vaihingen urban test dataset from the single-scale and single-feature perspectives.
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Jia, Dongfeng, Weiping Zhang, and Yanping Liu. "Systematic Approach for Tunnel Deformation Monitoring with Terrestrial Laser Scanning." Remote Sensing 13, no. 17 (September 4, 2021): 3519. http://dx.doi.org/10.3390/rs13173519.
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The use of terrestrial laser scanning (TLS) point clouds for tunnel deformation measurement has elicited much interest. However, general methods of point-cloud processing in tunnels are still under investigation, given the high accuracy and efficiency requirements in this area. This study discusses a systematic method of analyzing tunnel deformation. Point clouds from different stations need to be registered rapidly and with high accuracy before point-cloud processing. An orientation method of TLS in tunnels that uses a positioning base made in the laboratory is proposed for fast point-cloud registration. The calibration methods of the positioning base are demonstrated herein. In addition, an improved moving least-squares method is proposed as a way to reconstruct the centerline of a tunnel from unorganized point clouds. Then, the normal planes of the centerline are calculated and are used to serve as the reference plane for point-cloud projection. The convergence of the tunnel cross-section is analyzed, based on each point cloud slice, to determine the safety status of the tunnel. Furthermore, the results of the deformation analysis of a particular shield tunnel site are briefly discussed.
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Hese, S., C. Thiel, and A. Henkel. "UAV BASED MULTI SEASONAL DECIDUOUS TREE SPECIES ANALYSIS IN THE HAINICH NATIONAL PARK USING MULTI TEMPORAL AND POINT CLOUD CURVATURE FEATURES." ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLII-2/W13 (June 4, 2019): 363–70. http://dx.doi.org/10.5194/isprs-archives-xlii-2-w13-363-2019.
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<p><strong>Abstract.</strong> Low cost UAV systems are a flexible and mobile platform for very detailed spatial high-resolution point cloud and surface height mapping projects. This study investigates the potential of the DJI Phantom 4 Pro 3D point clouds and derived crown surface height information in combination with RGB spectral information for mapping of deciduous tree species in the Hainich national park area. RGB image data was captured in August, early October and November 2018 to create a multi seasonal spectral dataset for a 100&thinsp;ha test area. The flight campaigns were controlled from the Hainich flux tower platform in 40&thinsp;m height owned and operated by University of Göttingen in the central part of the park area. Absolut georeferencing accuracy of the datasets was improved using 7 DGPS measured control points within the stand structure on small forest clearings. Image files and ground control points were processed to a dense point cloud model with 2.6 billion points (approximately 200k points per tree crown object) using the Agisoft Metashape cluster processing environment. Additionally, a digital surface model and a true ortho image mosaic with 3&thinsp;cm spatial resolution was generated. For the differentiation of deciduous tree species, a reference data set with coordinates for the tree species <i>Fagus sylvatica</i> (beech), <i>Fraxinus excelsior</i> (ash), <i>Acer pseudoplatanus</i> (sycamore maple), <i>Carpinus betulus</i> (hornbeam) and dead trees and early defoliated trees was defined. The study site is however dominated by Fagus sylvatica and Fraxinus excelsior. We studied two different groups of features: tree crown surface height variability parameters using point cloud densities, point cloud height variance, local standard deviation of gaussian curvature, standard deviation of local point cloud roughness and multi temporal normalised spectral features using multi seasonal uncalibrated UAV RGB data. Analysis of feature separability showed that very high-resolution point cloud surface curvature properties with small neighbourhood radii can differentiate some tree species types but we also found multitemporal spectral ratios based on RGB data to be very successful in differentiating the main tree species.</p><p>Results of this work show that super fine very dense point cloud models and derived roughness measures of mixed forest stand surfaces hold valuable information for deciduous species discrimination and will likely also be very useful for morphological analysis of tree crown types.</p>
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Cheng, Silin, Xiwu Chen, Xinwei He, Zhe Liu, and Xiang Bai. "PRA-Net: Point Relation-Aware Network for 3D Point Cloud Analysis." IEEE Transactions on Image Processing 30 (2021): 4436–48. http://dx.doi.org/10.1109/tip.2021.3072214.
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