scholarly journals Fast and Accurate Normal Estimation for Point Clouds Via Patch Stitching

2022 ◽  
Vol 142 ◽  
pp. 103121
Author(s):  
Jun Zhou ◽  
Wei Jin ◽  
Mingjie Wang ◽  
Xiuping Liu ◽  
Zhiyang Li ◽  
...  
Keyword(s):  
Point Clouds ◽  
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.

Normal Estimation for Mass Point Clouds of Irregular Model in the 3D Reconstruction based on Fuzzy Inference

2014 ◽  
Vol 7 (5) ◽  
pp. 131-138
Author(s):  
Liu Yan-ju ◽  
Jiang Jin-gang ◽  
Miao Feng-juan ◽  
Tao Bai-rui ◽  
Zhang Hong-lie
Keyword(s):  
3D Reconstruction ◽  
Fuzzy Inference ◽  
Point Clouds ◽  
Mass Point ◽  
Normal Estimation

scholarly journals Robust Normal Estimation for 3D LiDAR Point Clouds in Urban Environments

Sensors ◽  
2019 ◽  
Vol 19 (5) ◽  
pp. 1248 ◽  
Author(s):  
Ruibin Zhao ◽  
Mingyong Pang ◽  
Caixia Liu ◽  
Yanling Zhang
Keyword(s):  
Ground Surface ◽  
Ground Cover ◽  
Point Clouds ◽  
Urban Environments ◽  
Lidar Data ◽  
Road Extraction ◽  
Normal Estimation ◽  
3D Lidar ◽  
Trees And Shrubs ◽  
Frequent Presence

Normal estimation is a crucial first step for numerous light detection and ranging (LiDAR) data-processing algorithms, from building reconstruction, road extraction, and ground-cover classification to scene rendering. For LiDAR point clouds in urban environments, this paper presents a robust method to estimate normals by constructing an octree-based hierarchical representation for the data and detecting a group of large enough consistent neighborhoods at multiscales. Consistent neighborhoods are mainly determined based on the observation that an urban environment is typically comprised of regular objects, e.g., buildings, roads, and the ground surface, and irregular objects, e.g., trees and shrubs; the surfaces of most regular objects can be approximatively represented by a group of local planes. Even in the frequent presence of heavy noise and anisotropic point samplings in LiDAR data, our method is capable of estimating robust normals for kinds of objects in urban environments, and the estimated normals are beneficial to more accurately segment and identify the objects, as well as preserving their sharp features and complete outlines. The proposed method was experimentally validated both on synthetic and real urban LiDAR datasets, and was compared to state-of-the-art methods.

Facial bones segmentation from skull point clouds based on deviation angle

2021 ◽  
Author(s):  
Masy Ari Ulinuha ◽  
Eko Mulyanto Yuniarno ◽  
I. Ketut Eddy Purnama ◽  
Mochamad Hariadi
Keyword(s):  
Computed Tomography ◽  
Feature Extraction ◽  
Point Clouds ◽  
Manual Segmentation ◽  
Deviation Angle ◽  
Facial Bones ◽  
Normal Estimation ◽  
Computed Tomography Images ◽  
Surface Normal ◽  
Three Phases

AbstractFacial bones segmentation is an important step to understanding a skull. In this paper, a method for segmenting facial bones from skull point clouds is proposed. The segmentation is based on the deviation angle features. The method consists of three phases: surface normal estimation, feature extraction, and point clouds classification. The method is applied to skull point clouds derived from computed tomography images. For evaluation, the method is compared with manual segmentation. The method has succeeded in segmenting facial bones with Precision = 0.836, Recall = 0.951, and F = 0.890.

Normal estimation of fuzzy inference for mass point clouds of irregular model in the 3D reconstruction

2014 ◽  
Author(s):  
Yan Ju Liu ◽  
Jin Gang Jiang ◽  
Feng Juang Miao ◽  
Bai Rui Tao ◽  
Hong Lie Zhang
Keyword(s):  
3D Reconstruction ◽  
Fuzzy Inference ◽  
Point Clouds ◽  
Mass Point ◽  
Normal Estimation

scholarly journals A 3D Point Cloud Filtering Method for Leaves Based on Manifold Distance and Normal Estimation

2019 ◽  
Vol 11 (2) ◽  
pp. 198 ◽  
Author(s):  
Chunhua Hu ◽  
Zhou Pan ◽  
Pingping Li
Keyword(s):  
Leaf Area ◽  
Point Cloud ◽  
Mean Absolute Error ◽  
Point Clouds ◽  
Absolute Error ◽  
Area Measurement ◽  
3D Point Cloud ◽  
Filtering Method ◽  
Normal Estimation ◽  
3D Point Clouds

Leaves are used extensively as an indicator in research on tree growth. Leaf area, as one of the most important index in leaf morphology, is also a comprehensive growth index for evaluating the effects of environmental factors. When scanning tree surfaces using a 3D laser scanner, the scanned point cloud data usually contain many outliers and noise. These outliers can be clusters or sparse points, whereas the noise is usually non-isolated but exhibits different attributes from valid points. In this study, a 3D point cloud filtering method for leaves based on manifold distance and normal estimation is proposed. First, leaf was extracted from the tree point cloud and initial clustering was performed as the preprocessing step. Second, outlier clusters filtering and outlier points filtering were successively performed using a manifold distance and truncation method. Third, noise points in each cluster were filtered based on the local surface normal estimation. The 3D reconstruction results of leaves after applying the proposed filtering method prove that this method outperforms other classic filtering methods. Comparisons of leaf areas with real values and area assessments of the mean absolute error (MAE) and mean absolute error percent (MAE%) for leaves in different levels were also conducted. The root mean square error (RMSE) for leaf area was 2.49 cm2. The MAE values for small leaves, medium leaves and large leaves were 0.92 cm2, 1.05 cm2 and 3.39 cm2, respectively, with corresponding MAE% values of 10.63, 4.83 and 3.8. These results demonstrate that the method proposed can be used to filter outliers and noise for 3D point clouds of leaves and improve 3D leaf visualization authenticity and leaf area measurement accuracy.

scholarly journals Fast and Robust Normal Estimation for Point Clouds with Sharp Features

2012 ◽  
Vol 31 (5) ◽  
pp. 1765-1774 ◽  
Author(s):  
Alexandre Boulch ◽  
Renaud Marlet
Keyword(s):  
Point Clouds ◽  
Normal Estimation

scholarly journals Normal Estimation for 3D Point Clouds via Local Plane Constraint and Multi-scale Selection

2020 ◽  
Vol 129 ◽  
pp. 102916 ◽  
Author(s):  
Jun Zhou ◽  
Hua Huang ◽  
Bin Liu ◽  
Xiuping Liu
Keyword(s):  
Point Clouds ◽  
Scale Selection ◽  
Normal Estimation ◽  
Multi Scale ◽  
3D Point Clouds ◽  
Local Plane

Detection of closed sharp edges in point clouds using normal estimation and graph theory

2007 ◽  
Vol 39 (4) ◽  
pp. 276-283 ◽  
Author(s):  
Kris Demarsin ◽  
Denis Vanderstraeten ◽  
Tim Volodine ◽  
Dirk Roose
Keyword(s):  
Graph Theory ◽  
Point Clouds ◽  
Normal Estimation ◽  
Sharp Edges
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