scholarly journals Intact Planar Abstraction of Buildings via Global Normal Refinement from Noisy Oblique Photogrammetric Point Clouds

2018 ◽  
Vol 7 (11) ◽  
pp. 431 ◽  
Author(s):  
Qing Zhu ◽  
Feng Wang ◽  
Han Hu ◽  
Yulin Ding ◽  
Jiali Xie ◽  
...  
Keyword(s):  
Global Optimization ◽  
Three Dimensional ◽  
Region Growing ◽  
Point Clouds ◽  
Spatial Relations ◽  
Global Scale ◽  
Surface Boundary ◽  
Planar Surfaces ◽  
Normal Vectors ◽  
Almost All

Oblique photogrammetric point clouds are currently one of the major data sources for the three-dimensional level-of-detail reconstruction of buildings. However, they are severely noise-laden and pose serious problems for the effective and automatic surface extraction of buildings. In addition, conventional methods generally use normal vectors estimated in a local neighborhood, which are liable to be affected by noise, leading to inferior results in successive building reconstruction. In this paper, we propose an intact planar abstraction method for buildings, which explicitly handles noise by integrating information in a larger context through global optimization. The information propagates hierarchically from a local to global scale through the following steps: first, based on voxel cloud connectivity segmentation, single points are clustered into supervoxels that are enforced to not cross the surface boundary; second, each supervoxel is expanded to nearby supervoxels through the maximal support region, which strictly enforces planarity; third, the relationships established by the maximal support regions are injected into a global optimization, which reorients the local normal vectors to be more consistent in a larger context; finally, the intact planar surfaces are obtained by region growing using robust normal and point connectivity in the established spatial relations. Experiments on the photogrammetric point clouds obtained from oblique images showed that the proposed method is effective in reducing the influence of noise and retrieving almost all of the major planar structures of the examined buildings.

scholarly journals FAST AND ROBUST SEGMENTATION AND CLASSIFICATION FOR CHANGE DETECTION IN URBAN POINT CLOUDS

2016 ◽  
Vol XLI-B3 ◽  
pp. 693-699 ◽  
Author(s):  
X. Roynard ◽  
J.-E. Deschaud ◽  
F. Goulette
Keyword(s):  
Change Detection ◽  
Laser Scanning ◽  
Three Dimensional ◽  
Region Growing ◽  
Point Clouds ◽  
Object Class ◽  
Robust Segmentation ◽  
Street Furniture ◽  
Fast Region

Change detection is an important issue in city monitoring to analyse street furniture, road works, car parking, etc. For example, parking surveys are needed but are currently a laborious task involving sending operators in the streets to identify the changes in car locations. In this paper, we propose a method that performs a fast and robust segmentation and classification of urban point clouds, that can be used for change detection. We apply this method to detect the cars, as a particular object class, in order to perform parking surveys automatically. A recently proposed method already addresses the need for fast segmentation and classification of urban point clouds, using elevation images. The interest to work on images is that processing is much faster, proven and robust. However there may be a loss of information in complex 3D cases: for example when objects are one above the other, typically a car under a tree or a pedestrian under a balcony. In this paper we propose a method that retain the three-dimensional information while preserving fast computation times and improving segmentation and classification accuracy. It is based on fast region-growing using an octree, for the segmentation, and specific descriptors with Random-Forest for the classification. Experiments have been performed on large urban point clouds acquired by Mobile Laser Scanning. They show that the method is as fast as the state of the art, and that it gives more robust results in the complex 3D cases.

scholarly journals RECONSTRUCTION OF 3D MODELS FROM POINT CLOUDS WITH HYBRID REPRESENTATION

2018 ◽  
Vol XLII-2 ◽  
pp. 449-454
Author(s):  
P. Hu ◽  
Z. Dong ◽  
P. Yuan ◽  
F. Liang ◽  
B. Yang
Keyword(s):  
3D Reconstruction ◽  
Three Dimensional ◽  
Region Growing ◽  
Reconstruction Algorithm ◽  
Point Clouds ◽  
3D Models ◽  
Building Structures ◽  
Freeform Surfaces ◽  
Irregular Surfaces ◽  
Regular Structures

The three-dimensional (3D) reconstruction of urban buildings from point clouds has long been an active topic in applications related to human activities. However, due to the structures significantly differ in terms of complexity, the task of 3D reconstruction remains a challenging issue especially for the freeform surfaces. In this paper, we present a new reconstruction algorithm which allows the 3D-models of building as a combination of regular structures and irregular surfaces, where the regular structures are parameterized plane primitives and the irregular surfaces are expressed as meshes. The extraction of irregular surfaces starts with an over-segmented method for the unstructured point data, a region growing approach based the adjacent graph of super-voxels is then applied to collapse these super-voxels, and the freeform surfaces can be clustered from the voxels filtered by a thickness threshold. To achieve these regular planar primitives, the remaining voxels with a larger flatness will be further divided into multiscale super-voxels as basic units, and the final segmented planes are enriched and refined in a mutually reinforcing manner under the framework of a global energy optimization. We have implemented the proposed algorithms and mainly tested on two point clouds that differ in point density and urban characteristic, and experimental results on complex building structures illustrated the efficacy of the proposed framework.

scholarly journals FAST AND ROBUST SEGMENTATION AND CLASSIFICATION FOR CHANGE DETECTION IN URBAN POINT CLOUDS

2016 ◽  
Vol XLI-B3 ◽  
pp. 693-699 ◽  
Author(s):  
X. Roynard ◽  
J.-E. Deschaud ◽  
F. Goulette
Keyword(s):  
Change Detection ◽  
Laser Scanning ◽  
Three Dimensional ◽  
Region Growing ◽  
Point Clouds ◽  
Object Class ◽  
Robust Segmentation ◽  
Street Furniture ◽  
Fast Region

Change detection is an important issue in city monitoring to analyse street furniture, road works, car parking, etc. For example, parking surveys are needed but are currently a laborious task involving sending operators in the streets to identify the changes in car locations. In this paper, we propose a method that performs a fast and robust segmentation and classification of urban point clouds, that can be used for change detection. We apply this method to detect the cars, as a particular object class, in order to perform parking surveys automatically. A recently proposed method already addresses the need for fast segmentation and classification of urban point clouds, using elevation images. The interest to work on images is that processing is much faster, proven and robust. However there may be a loss of information in complex 3D cases: for example when objects are one above the other, typically a car under a tree or a pedestrian under a balcony. In this paper we propose a method that retain the three-dimensional information while preserving fast computation times and improving segmentation and classification accuracy. It is based on fast region-growing using an octree, for the segmentation, and specific descriptors with Random-Forest for the classification. Experiments have been performed on large urban point clouds acquired by Mobile Laser Scanning. They show that the method is as fast as the state of the art, and that it gives more robust results in the complex 3D cases.

scholarly journals Segmentation of planar surfaces in LiDAR point clouds of an electrical substation by exploring the structure of points neighbourhood

2014 ◽  
Vol XL-5 ◽  
pp. 55-62 ◽  
Author(s):  
M. Arastounia ◽  
D.D. Lichti
Keyword(s):  
Distribution System ◽  
Region Growing ◽  
Point Clouds ◽  
Department Of Energy ◽  
Power Outages ◽  
Terrestrial Lidar ◽  
Planar Surfaces ◽  
Computational Speed ◽  
The Usa ◽  
Electrical Substation

According to the Department of Energy of the USA, today’s electrical distribution system is 97.97% reliable. However, power outages and interruptions still impact many people. Many power outages are caused by animals coming into contact with the conductive elements of the electrical substations. This can be prevented by covering the conductive electrical objects with insulating materials. The design of these custom-built insulating covers requires a 3D as-built plan of the substation. This research aims to develop automated methods to create such a 3D as-built plan using terrestrial LiDAR data for which objects first need to be recognized in the LiDAR point clouds. This paper reports on the application of a new algorithm for the segmentation of planar surfaces found at electrical substations. The proposed approach is a region growing method that aggregates points based on their proximity to each other and their neighbourhood dispersion direction. PCA (principal components analysis) is also employed to segment planar surfaces in the electrical substation. In this research two different laser scanners, Leica HDS 6100 and Faro Focus3D, were utilized to scan an electrical substation in Airdrie, a city located in north of Calgary, Canada. In this research, three subsets incorporating one subset of Leica dataset with approximately 1.7 million points and two subsets of the Faro dataset with 587 and 79 thousand points were utilized. The performance of our proposed method is compared with the performance of PCA by performing check point analysis and investigation of computational speed. Both methods managed to detect a great proportion of planar points (about 70%). However, the proposed method slightly outperformed PCA. 95% of the points that were segmented by both methods as planar points did actually lie on a planar surface. This exhibits the high ability of both methods to identify planar points. The results also indicate that the computational speed of our method is superior to that of PCA by 50%. It is concluded that our proposed method achieves better results with higher computational speed than PCA in the segmentation of planar surfaces.

scholarly journals Roof Plane Segmentation from Airborne LiDAR Data Using Hierarchical Clustering and Boundary Relabeling

2020 ◽  
Vol 12 (9) ◽  
pp. 1363 ◽  
Author(s):  
Li Li ◽  
Jian Yao ◽  
Jingmin Tu ◽  
Xinyi Liu ◽  
Yinxuan Li ◽  
...  
Keyword(s):  
Hierarchical Clustering ◽  
Three Dimensional ◽  
Region Growing ◽  
Point Clouds ◽  
Airborne Lidar ◽  
Lidar Data ◽  
Building Models ◽  
Novel Approach ◽  
Airborne Lidar Data ◽  
Seed Points

The roof plane segmentation is one of the key issues for constructing accurate three-dimensional building models from airborne light detection and ranging (LiDAR) data. Region growing is one of the most widely used methods to detect roof planes. It first selects one point or region as a seed, and then iteratively expands to neighboring points. However, region growing has two problems. The first problem is that it is hard to select the robust seed points. The other problem is that it is difficult to detect the accurate boundaries between two roof planes. In this paper, to solve these two problems, we propose a novel approach to segment the roof planes from airborne LiDAR point clouds using hierarchical clustering and boundary relabeling. For the first problem, we first extract the initial set of robust planar patches via an octree-based method, and then apply the hierarchical clustering method to iteratively merge the adjacent planar patches belonging to the same plane until the merging cost exceeds a predefined threshold. These merged planar patches are regarded as the robust seed patches for the next region growing. The coarse roof planes are generated by adding the non-planar points into the seed patches in sequence using region growing. However, the boundaries of coarse roof planes may be inaccurate. To solve this problem, namely, the second problem, we refine the boundaries between adjacent coarse planes by relabeling the boundary points. At last, we can effectively extract high-quality roof planes with smooth and accurate boundaries from airborne LiDAR data. We conducted our experiments on two datasets captured from Vaihingen and Wuhan using Leica ALS50 and Trimble Harrier 68i, respectively. The experimental results show that our proposed approach outperforms several representative approaches in both visual quality and quantitative metrics.

scholarly journals A line-based approach for precise extraction of road and curb region from mobile mapping data

2014 ◽  
Vol II-5 ◽  
pp. 243-250 ◽  
Author(s):  
R. Miyazaki ◽  
M. Yamamoto ◽  
E. Hanamoto ◽  
H. Izumi ◽  
K. Harada
Keyword(s):  
Laser Scanning ◽  
Distribution Density ◽  
Region Growing ◽  
Point Clouds ◽  
Planar Structure ◽  
Scanning System ◽  
Normal Vector ◽  
Mobile Mapping ◽  
Planar Surfaces ◽  
Structure Detection

Planar structure detection from point clouds is important process in many applications such as maintenance of infrastructure facility including roads and curbs because most artificial structures consists of planar surfaces. The Mobile Mapping System can obtain a large amount of points with traveling at a standard speed. However, in the case that the high-end laser scanning system is equipped, the distribution density of points is uneven. In the point-based method, this situation causes the problem to the method of calculating geometric information using neighborhood points. In this paper, we propose a line-based region growing method in order to detect planar structures with precise boundary from point clouds with uneven distribution density of points. The precise boundary of a planar structure is maintained by appropriately creating line segments from the input clouds. We adapt the definition of neighborhood and the estimation of the normal vector to the line-based region growing. The evaluation by comparing our result with manually extracted points shows that more than 98% of curb points are detected. And, about 90% of the boundary points between a road and a curb are detected with less than 0.005 meters of the distance error.

scholarly journals A DATA DRIVEN METHOD FOR BUILDING RECONSTRUCTION FROM LiDAR POINT CLOUDS

2014 ◽  
Vol XL-2/W3 ◽  
pp. 225-230 ◽  
Author(s):  
M. Sajadian ◽  
H. Arefi
Keyword(s):  
High Speed ◽  
Laser Scanning ◽  
Pulse Length ◽  
Three Dimensional ◽  
Point Clouds ◽  
Building Model ◽  
Building Models ◽  
Edge Points ◽  
Normal Vectors ◽  
Building Reconstruction

Airborne laser scanning, commonly referred to as LiDAR, is a superior technology for three-dimensional data acquisition from Earth's surface with high speed and density. Building reconstruction is one of the main applications of LiDAR system which is considered in this study. For a 3D reconstruction of the buildings, the buildings points should be first separated from the other points such as; ground and vegetation. In this paper, a multi-agent strategy has been proposed for simultaneous extraction and segmentation of buildings from LiDAR point clouds. Height values, number of returned pulse, length of triangles, direction of normal vectors, and area are five criteria which have been utilized in this step. Next, the building edge points are detected using a new method named "Grid Erosion". A RANSAC based technique has been employed for edge line extraction. Regularization constraints are performed to achieve the final lines. Finally, by modelling of the roofs and walls, 3D building model is reconstructed. The results indicate that the proposed method could successfully extract the building from LiDAR data and generate the building models automatically. A qualitative and quantitative assessment of the proposed method is then provided.

scholarly journals AUTOMATED COARSE REGISTRATION OF POINT CLOUDS IN 3D URBAN SCENES USING VOXEL BASED PLANE CONSTRAINT

2017 ◽  
Vol IV-2/W4 ◽  
pp. 185-191 ◽  
Author(s):  
Y. Xu ◽  
R. Boerner ◽  
W. Yao ◽  
L. Hoegner ◽  
U. Stilla
Keyword(s):  
Point Cloud ◽  
Laser Scanning ◽  
Large Scale ◽  
Point Clouds ◽  
Coordinate Frame ◽  
Small Individual ◽  
Planar Surfaces ◽  
Coarse Registration ◽  
Transformation Parameters ◽  
Normal Vectors

For obtaining a full coverage of 3D scans in a large-scale urban area, the registration between point clouds acquired via terrestrial laser scanning (TLS) is normally mandatory. However, due to the complex urban environment, the automatic registration of different scans is still a challenging problem. In this work, we propose an automatic marker free method for fast and coarse registration between point clouds using the geometric constrains of planar patches under a voxel structure. Our proposed method consists of four major steps: the voxelization of the point cloud, the approximation of planar patches, the matching of corresponding patches, and the estimation of transformation parameters. In the voxelization step, the point cloud of each scan is organized with a 3D voxel structure, by which the entire point cloud is partitioned into small individual patches. In the following step, we represent points of each voxel with the approximated plane function, and select those patches resembling planar surfaces. Afterwards, for matching the corresponding patches, a RANSAC-based strategy is applied. Among all the planar patches of a scan, we randomly select a planar patches set of three planar surfaces, in order to build a coordinate frame via their normal vectors and their intersection points. The transformation parameters between scans are calculated from these two coordinate frames. The planar patches set with its transformation parameters owning the largest number of coplanar patches are identified as the optimal candidate set for estimating the correct transformation parameters. The experimental results using TLS datasets of different scenes reveal that our proposed method can be both effective and efficient for the coarse registration task. Especially, for the fast orientation between scans, our proposed method can achieve a registration error of less than around 2 degrees using the testing datasets, and much more efficient than the classical baseline methods.

scholarly journals HoPE: Horizontal Plane Extractor for Cluttered 3D Scenes

Sensors ◽  
2018 ◽  
Vol 18 (10) ◽  
pp. 3214 ◽  
Author(s):  
Zhipeng Dong ◽  
Yi Gao ◽  
Jinfeng Zhang ◽  
Yunhui Yan ◽  
Xin Wang ◽  
...  
Keyword(s):  
Horizontal Plane ◽  
Point Cloud ◽  
Nearest Neighbor ◽  
Three Dimensional ◽  
Principal Component ◽  
Region Growing ◽  
Point Clouds ◽  
Measurement Unit ◽  
Coordinate Frame ◽  
3D Point Clouds

Extracting horizontal planes in heavily cluttered three-dimensional (3D) scenes is an essential procedure for many robotic applications. Aiming at the limitations of general plane segmentation methods on this subject, we present HoPE, a Horizontal Plane Extractor that is able to extract multiple horizontal planes in cluttered scenes with both organized and unorganized 3D point clouds. It transforms the source point cloud in the first stage to the reference coordinate frame using the sensor orientation acquired either by pre-calibration or an inertial measurement unit, thereby leveraging the inner structure of the transformed point cloud to ease the subsequent processes that use two concise thresholds for producing the results. A revised region growing algorithm named Z clustering and a principal component analysis (PCA)-based approach are presented for point clustering and refinement, respectively. Furthermore, we provide a nearest neighbor plane matching (NNPM) strategy to preserve the identities of extracted planes across successive sequences. Qualitative and quantitative evaluations of both real and synthetic scenes demonstrate that our approach outperforms several state-of-the-art methods under challenging circumstances, in terms of robustness to clutter, accuracy, and efficiency. We make our algorithm an off-the-shelf toolbox which is publicly available.

scholarly journals Multifaceted Geometric Assessment towards Simplified Urban Surfaces Built by 3D Reconstruction

2019 ◽  
Vol 8 (8) ◽  
pp. 360
Author(s):  
Sheng’en Liu ◽  
Hui Yi ◽  
Xiangning Chen ◽  
Decheng Wang ◽  
Wei Jin
Keyword(s):  
3D Reconstruction ◽  
Urban Areas ◽  
Large Scale ◽  
Smart Cities ◽  
Three Dimensional ◽  
Research Fields ◽  
Planar Surfaces ◽  
Surface Simplification ◽  
Significant Step ◽  
Almost All

Large-scale three-dimensional (3D) reconstruction from multi-view images is used to generate 3D mesh surfaces, which are usually built for urban areas and are widely applied in many research hotspots, such as smart cities. Their simplification is a significant step for 3D roaming, pattern recognition, and other research fields. The simplification quality has been assessed in several studies. On the one hand, almost all studies on surface simplification have measured simplification errors using the surface comparison tool Metro, which does not preserve sufficient detail. On the other hand, the reconstruction precision of urban surfaces varies as a result of homogeneity or heterogeneity. Therefore, it is difficult to assess simplification quality without surface classification. These difficulties are addressed in this study by first classifying urban surfaces into planar surfaces, detailed surfaces, and urban frameworks according to the simplification errors of different types of surfaces and then measuring these errors after sampling. A series of assessment indexes are also provided to contribute to the advancement of simplification algorithms.

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