4D feature of point cloud based on robust normal estimation

2013 ◽  
Author(s):  
Liu Ran ◽  
Wan Wanggen ◽  
Lu Libing ◽  
Zhou Yiyuan ◽  
Zhang Ximin
Keyword(s):  
Point Cloud ◽  
Normal Estimation

scholarly journals Geological Modeling Method Based on the Normal Dynamic Estimation of Sparse Point Clouds

Mathematics ◽  
2021 ◽  
Vol 9 (15) ◽  
pp. 1819
Author(s):  
Tiandong Shi ◽  
Deyun Zhong ◽  
Liguan Wang
Keyword(s):  
Point Cloud ◽  
Minimum Spanning Tree ◽  
Point Clouds ◽  
Original Method ◽  
Modeling Method ◽  
Geological Modeling ◽  
Improved Method ◽  
Normal Estimation ◽  
Sampling Points ◽  
Correct Point

The effect of geological modeling largely depends on the normal estimation results of geological sampling points. However, due to the sparse and uneven characteristics of geological sampling points, the results of normal estimation have great uncertainty. This paper proposes a geological modeling method based on the dynamic normal estimation of sparse point clouds. The improved method consists of three stages: (1) using an improved local plane fitting method to estimate the normals of the point clouds; (2) using an improved minimum spanning tree method to redirect the normals of the point clouds; (3) using an implicit function to construct a geological model. The innovation of this method is an iterative estimation of the point cloud normal. The geological engineer adjusts the normal direction of some point clouds according to the geological law, and then the method uses these correct point cloud normals as a reference to estimate the normals of all point clouds. By continuously repeating the iterative process, the normal estimation result will be more accurate. Experimental results show that compared with the original method, the improved method is more suitable for the normal estimation of sparse point clouds by adjusting normals, according to prior knowledge, dynamically.

ESTIMATING SURFACE NORMALS IN NOISY POINT CLOUD DATA

2004 ◽  
Vol 14 (04n05) ◽  
pp. 261-276 ◽  
Author(s):  
NILOY J. MITRA ◽  
AN NGUYEN ◽  
LEONIDAS GUIBAS
Keyword(s):  
Point Cloud ◽  
Smooth Surface ◽  
Smooth Curve ◽  
Local Information ◽  
Least Square ◽  
Neighborhood Size ◽  
Point Cloud Data ◽  
Cloud Data ◽  
Normal Estimation ◽  
Least Square Fitting

In this paper we describe and analyze a method based on local least square fitting for estimating the normals at all sample points of a point cloud data (PCD) set, in the presence of noise. We study the effects of neighborhood size, curvature, sampling density, and noise on the normal estimation when the PCD is sampled from a smooth curve in ℝ2or a smooth surface in ℝ3, and noise is added. The analysis allows us to find the optimal neighborhood size using other local information from the PCD. Experimental results are also provided.

scholarly journals Deep feature-preserving normal estimation for point cloud filtering

2020 ◽  
Vol 125 ◽  
pp. 102860 ◽  
Author(s):  
Dening Lu ◽  
Xuequan Lu ◽  
Yangxing Sun ◽  
Jun Wang
Keyword(s):  
Point Cloud ◽  
Normal Estimation ◽  
Deep Feature ◽  
Feature Preserving

scholarly journals Point Cloud Normal Estimation by Fast Guided Least Squares Representation

IEEE Access ◽  
2020 ◽  
Vol 8 ◽  
pp. 101580-101590 ◽  
Author(s):  
Jie Zhang ◽  
Jiahui Duan ◽  
Kewei Tang ◽  
Junjie Cao ◽  
Xiuping Liu
Keyword(s):  
Least Squares ◽  
Point Cloud ◽  
Normal Estimation

scholarly journals Anisotropic neighborhood searching for point cloud with sharp feature

2020 ◽  
pp. 002029402096424
Author(s):  
Xiaocui Yuan ◽  
Baoling Liu ◽  
Yongli Ma
Keyword(s):  
Data Processing ◽  
Point Cloud ◽  
Accurate Result ◽  
Noise Removal ◽  
Feature Point ◽  
Normal Vector ◽  
Feature Points ◽  
Normal Estimation ◽  
Sharp Feature ◽  
Anisotropic Neighborhood

The k-nearest neighborhoods (kNN) of feature points of complex surface model are usually isotropic, which may lead to sharp feature blurring during data processing, such as noise removal and surface reconstruction. To address this issue, a new method was proposed to search the anisotropic neighborhood for point cloud with sharp feature. Constructing KD tree and calculating kNN for point cloud data, the principal component analysis method was employed to detect feature points and estimate normal vectors of points. Moreover, improved bilateral normal filter was used to refine the normal vector of feature point to obtain more accurate normal vector. The isotropic kNN of feature point were segmented by mapping the kNN into Gaussian sphere to form different data-clusters, with the hierarchical clustering method used to separate the data in Gaussian sphere into different clusters. The optimal anisotropic neighborhoods of feature point corresponded to the cluster data with the maximum point number. To validate the effectiveness of our method, the anisotropic neighbors are applied to point data processing, such as normal estimation and point cloud denoising. Experimental results demonstrate that the proposed algorithm in the work is more time-consuming, but provides a more accurate result for point cloud processing by comparing with other kNN searching methods. The anisotropic neighborhood searched by our method can be used to normal estimation, denoising, surface fitting and reconstruction et al. for point cloud with sharp feature, and our method can provide more accurate result comparing with isotropic neighborhood.

scholarly journals Robust point cloud normal estimation via neighborhood reconstruction

2019 ◽  
Vol 11 (4) ◽  
pp. 168781401983604
Author(s):  
Zhiqiang Yu ◽  
Taiyong Wang ◽  
Ting Guo ◽  
Hongbin Li ◽  
Jingchuan Dong
Keyword(s):  
Point Cloud ◽  
Normal Estimation
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