scholarly journals Fast Point Cloud Skeleton Extraction via Octree-graph

2015 ◽  
Author(s):  
Jia Cao
2018 ◽  
Vol 12 (2) ◽  
pp. 161-171
Author(s):  
Linming Gao ◽  
Dong Zhang ◽  
Nan Li ◽  
Lei Chen

2009 ◽  
Vol 28 (3) ◽  
pp. 1-9 ◽  
Author(s):  
Andrea Tagliasacchi ◽  
Hao Zhang ◽  
Daniel Cohen-Or

Author(s):  
M. Deidda ◽  
A. Pala ◽  
G. Sanna

Abstract. The surveying and management of telecommunication towers poses a series of engineering challenges. Not only they must be regularly inspected for the purpose of checking for issues that require maintenance interventions, but they are often sub-let by their owners to communication companies, requiring a survey of the many (several thousand per company) installed appliances to check that they respect the established contracts. This requires a surveying methodology that is fast and possibly automated. Photogrammetric techniques using UAV-mounted cameras seem to offer a solution that is both suitable and economical. Our research team was asked to evaluate whether, from the information acquired by small drones it was possible to obtain geometric information on the structure, with what degree of accuracy and what level of detail. The workflow of this process is naturally articulated in three steps: the acquisition, the construction of the point cloud, and the extraction of geometries. The case study is a tower carrying antennas owned by several operators and placed in the industrial district of Cagliari. The article examines the problems found in modelling such structures using point clouds derived from the Structure-from-Motion technique, in order to obtain a model of nodes and beams suitable for the reconstruction of the structure’s geometric elements, and possibly for a finite elements analysis or for populating GIS and BIM, either automatically or with minimal user intervention. In order to achieve this, we have used voxelization and skeleton extraction algorithms to obtain a 3D graph of the structure. The analysis of the results was carried out by varying the parameters relating to the voxel size, which defines the resolution, and the density of the points contained inside each voxel.


2012 ◽  
Vol 9 (6) ◽  
pp. 869-879 ◽  
Author(s):  
Vanna Sam ◽  
Hiroaki Kawata ◽  
Takashi Kanai

2017 ◽  
Vol 47 (7) ◽  
pp. 832 ◽  
Author(s):  
Xiao PAN ◽  
Caiming ZHANG ◽  
Xiaojie WANG ◽  
Yuanfeng ZHOU

2020 ◽  
Vol 12 (22) ◽  
pp. 3824
Author(s):  
Mingyao Ai ◽  
Yuan Yao ◽  
Qingwu Hu ◽  
Yue Wang ◽  
Wei Wang

Effective 3D tree reconstruction based on point clouds from terrestrial Light Detection and Ranging (LiDAR) scans (TLS) has been widely recognized as a critical technology in forestry and ecology modeling. The major advantages of using TLS lie in its rapidly and automatically capturing tree information at millimeter level, providing massive high-density data. In addition, TLS 3D tree reconstruction allows for occlusions and complex structures from the derived point cloud of trees to be obtained. In this paper, an automatic tree skeleton extraction approach based on multi-view slicing is proposed to improve the TLS 3D tree reconstruction, which borrowed the idea from the medical imaging technology of X-ray computed tomography. Firstly, we extracted the precise trunk center and then cut the point cloud of the tree into slices. Next, the skeleton from each slice was generated using the kernel mean shift and principal component analysis algorithms. Accordingly, these isolated skeletons were smoothed and morphologically synthetized. Finally, the validation in point clouds of two trees acquired from multi-view TLS further demonstrated the potential of the proposed framework in efficiently dealing with TLS point cloud data.


2020 ◽  
Author(s):  
Ayan Chaudhury ◽  
Christophe Godin

AbstractSkeleton extraction from 3D plant point cloud data is an essential prior for myriads of phenotyping studies. Although skeleton extraction from 3D shapes have been studied extensively in the computer vision and graphics literature, handling the case of plants is still an open problem. Drawbacks of the existing approaches include the zigzag structure of the skeleton, nonuniform density of skeleton points, lack of points in the areas having complex geometry structure, and most importantly the lack of biological relevance. With the aim to improve existing skeleton structures of state-of-the-art, we propose a stochastic framework which is supported by the biological structure of the original plant (we consider plants without any leaves). Initially we estimate the branching structure of the plant by the notion of β-splines to form a curve tree defined as a finite set of curves joined in a tree topology with certain level of smoothness. In the next phase, we force the discrete points in the curve tree to move towards the original point cloud by treating each point in the curve tree as a center of Gaussian, and points in the input cloud data as observations from the Gaussians. The task is to find the correct locations of the Gaussian centroids by maximizing a likelihood. The optimization technique is iterative and is based on the Expectation Maximization (EM) algorithm. The E-step estimates which Gaussian the observed point cloud was sampled from, and the M-step maximizes the negative log-likelihood that the observed points were sampled from the Gaussian Mixture Model (GMM) with respect to the model parameters. We experiment with several real world and synthetic datasets and demonstrate the robustness of the approach over the state-of-the-art.


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