An Efficient Planar Feature Fitting Method Using Point Cloud Simplification and Threshold-Independent BaySAC

2016 ◽  
Vol 13 (12) ◽  
pp. 1842-1846 ◽  
Author(s):  
Zhizhong Kang ◽  
Ruofei Zhong ◽  
Ai Wu ◽  
Zhenwei Shi ◽  
Zhongfei Luo
Keyword(s):  
Point Cloud ◽  
Fitting Method ◽  
Feature Fitting ◽  
Point Cloud Simplification ◽  
Planar Feature

scholarly journals 3D Point Cloud Simplification Based on k-Nearest Neighbor and Clustering

2020 ◽  
Vol 2020 ◽  
pp. 1-10 ◽  
Author(s):  
Abdelaaziz Mahdaoui ◽  
El Hassan Sbai
Keyword(s):  
Point Cloud ◽  
Clustering Algorithm ◽  
Nearest Neighbor ◽  
3D Point Cloud ◽  
K Nearest Neighbor ◽  
Test Dataset ◽  
New Approach ◽  
Entropy Estimation ◽  
3D Objects ◽  
Point Cloud Simplification

While the reconstruction of 3D objects is increasingly used today, the simplification of 3D point cloud, however, becomes a substantial phase in this process of reconstruction. This is due to the huge amounts of dense 3D point cloud produced by 3D scanning devices. In this paper, a new approach is proposed to simplify 3D point cloud based on k-nearest neighbor (k-NN) and clustering algorithm. Initially, 3D point cloud is divided into clusters using k-means algorithm. Then, an entropy estimation is performed for each cluster to remove the ones that have minimal entropy. In this paper, MATLAB is used to carry out the simulation, and the performance of our method is testified by test dataset. Numerous experiments demonstrate the effectiveness of the proposed simplification method of 3D point cloud.

scholarly journals Processing Chain for Estimation of Tree Diameter from GNSS-IMU-Based Mobile Laser Scanning Data

2019 ◽  
Vol 11 (6) ◽  
pp. 615 ◽  
Author(s):  
Juraj Čerňava ◽  
Martin Mokroš ◽  
Ján Tuček ◽  
Michal Antal ◽  
Zuzana Slatkovská
Keyword(s):  
Point Cloud ◽  
Laser Scanning ◽  
Forest Canopy ◽  
Point Clouds ◽  
Satellite System ◽  
Forest Road ◽  
Forest Mapping ◽  
Fitting Method ◽  
Progressive Technology ◽  
Global Navigation Satellite

Mobile laser scanning (MLS) is a progressive technology that has already demonstrated its ability to provide highly accurate measurements of road networks. Mobile innovation of the laser scanning has also found its use in forest mapping over the last decade. In most cases, existing methods for forest data acquisition using MLS result in misaligned scenes of the forest, scanned from different views appearing in one point cloud. These difficulties are caused mainly by forest canopy blocking the global navigation satellite system (GNSS) signal and limited access to the forest. In this study, we propose an approach to the processing of MLS data of forest scanned from different views with two mobile laser scanners under heavy canopy. Data from two scanners, as part of the mobile mapping system (MMS) Riegl VMX-250, were acquired by scanning from five parallel skid trails that are connected to the forest road. Misaligned scenes of the forest acquired from different views were successfully extracted from the raw MLS point cloud using GNSS time based clustering. At first, point clouds with correctly aligned sets of ground points were generated using this method. The loss of points after the clustering amounted to 33.48%. Extracted point clouds were then reduced to 1.15 m thick horizontal slices, and tree stems were detected. Point clusters from individual stems were grouped based on the diameter and mean GNSS time of the cluster acquisition. Horizontal overlap was calculated for the clusters from individual stems, and sufficiently overlapping clusters were aligned using the OPALS ICP module. An average misalignment of 7.2 mm was observed for the aligned point clusters. A 5-cm thick horizontal slice of the aligned point cloud was used for estimation of the stem diameter at breast height (DBH). DBH was estimated using a simple circle-fitting method with a root-mean-square error of 3.06 cm. The methods presented in this study have the potential to process MLS data acquired under heavy forest canopy with any commercial MMS.

scholarly journals An Improved Building Reconstruction Algorithm Based on Manhattan World Assumption and Line-Restricted Hypothetical Plane Fitting

2020 ◽  
Vol 2020 ◽  
pp. 1-16
Author(s):  
Xiaoguo Zhang ◽  
Guo Wang ◽  
Ye Gao ◽  
Huiqing Wang ◽  
Qing Wang
Keyword(s):  
Line Segment ◽  
Point Cloud ◽  
Mean Shift ◽  
Reconstruction Algorithm ◽  
Triangle Mesh ◽  
Line Segments ◽  
Plane Fitting ◽  
Fitting Method ◽  
Dense Point ◽  
Seed Points

An improved patch-based multiview stereo (PMVS) algorithm based on Manhattan world assumption and the line-restricted hypothetical plane fitting method according to buildings’ spatial characteristics is proposed. Different from the original PMVS algorithm, our approach generates seed points purely from 3D line segments instead of using those feature points. First, 3D line segments are extracted using the existing Line3D++ algorithm, and the 3D line segment clustering criterion of buildings is established based on Manhattan world assumption. Next, by using the normal direction obtained using the result of 3D line segment clustering, we propose a multihypothetical plane fitting algorithm based on the mean shift method. Then, through subdividing on the triangle mesh constructed based on the building hypothetical plane model, semidense point cloud can be quickly obtained, and it is used as seed points of the PMVS pipeline instead of the sparse and noisy seed points generated by PMVS itself. After that, dense point cloud can be obtained through the existing PMVS expansion pipeline. Finally, unit and integration experiments are designed; the test results show that the proposed algorithm is 15%∼23% faster than the original PMWS in running time, and at the same time, the reconstruction quality of buildings is improved as well by successfully removing many noise points in the buildings.

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