A Curvature Based Adaptive Neighborhood for Individual Point Cloud Classification

He, Enqiu; Chen, Q.; Wang, H.; Liu, X.

doi:10.5194/isprs-archives-xlii-2-w7-219-2017

Cited by 11 publications

(16 citation statements)

References 18 publications

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“…To avoid these issues, some authors proposed adaptive approaches instead of using the fixed minimal neighbourhood. For example, Elong et al [29] used a curvature-based adaptive neighbourhood selection technique to classify point cloud data. Considering the calculated curvature value of each point, the author divided an input point cloud into scatter and regular regions.…”

Section: Neighbourhood Selectionmentioning

confidence: 99%

Effective Selection of Variable Point Neighbourhood for Feature Point Extraction from Aerial Building Point Cloud Data

et al. 2021

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Existing approaches that extract buildings from point cloud data do not select the appropriate neighbourhood for estimation of normals on individual points. However, the success of these approaches depends on correct estimation of the normal vector. In most cases, a fixed neighbourhood is selected without considering the geometric structure of the object and the distribution of the input point cloud. Thus, considering the object structure and the heterogeneous distribution of the point cloud, this paper proposes a new effective approach for selecting a minimal neighbourhood, which can vary for each input point. For each point, a minimal number of neighbouring points are iteratively selected. At each iteration, based on the calculated standard deviation from a fitted 3D line to the selected points, a decision is made adaptively about the neighbourhood. The selected minimal neighbouring points make the calculation of the normal vector accurate. The direction of the normal vector is then used to calculate the inside fold feature points. In addition, the Euclidean distance from a point to the calculated mean of its neighbouring points is used to make a decision about the boundary point. In the context of the accuracy evaluation, the experimental results confirm the competitive performance of the proposed approach of neighbourhood selection over the state-of-the-art methods. Based on our generated ground truth data, the proposed fold and boundary point extraction techniques show more than 90% F1-scores.

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Section: Neighbourhood Selectionmentioning

confidence: 99%

Effective Selection of Variable Point Neighbourhood for Feature Point Extraction from Aerial Building Point Cloud Data

et al. 2021

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“…Recently, statistical methods that use eigenvalue-based 3D feature values are becoming popular (West et al, 2004, Rusu, 2010, Toshev et al, 2010, Demantké et al, 2011, Weinmann et al, 2013, Weinmann et al, 2014, Dittrich et al, 2017, He et al, 2017. In these statistical methods, the 3D feature values are defined based on the eigenvalues of the local 3D covariance matrix, which is also called the 3D structure tensor (Jutzi, Gross, 2009).…”

Section: Related Workmentioning

confidence: 99%

“…For each 3D point, the 3D structure tensor is calculated by numerically investigating local variances and covariances of point distributions within a certain radius of a spherical neighborhood. It is also useful for adaptively tuning the spherical radius based on local distributional properties (Weinmann et al, 2014, He et al, 2017.…”

Section: Related Workmentioning

confidence: 99%

Opacity-Based Edge Highlighting for Transparent Visualization of 3d Scanned Point Clouds

Kawakami

Hasegawa

et al. 2020

ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci.

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Abstract. The recent development of 3D scanning technologies has made it possible to quickly and accurately record various 3D objects in the real world. The 3D scanned data take the form of large-scale point clouds, which describe complex 3D structures of the target objects and the surrounding scenes. The complexity becomes significant in cases that a scanned object has internal 3D structures, and the acquired point cloud is created by merging the scanning results of both the interior and surface shapes. To observe the whole 3D structure of such complex point-based objects, the point-based transparent visualization, which we recently proposed, is useful because we can observe the internal 3D structures as well as the surface shapes based on high-quality see-through 3D images. However, transparent visualization sometimes shows us too much information so that the generated images become confusing. To address this problem, in this paper, we propose to combine “edge highlighting” with transparent visualization. This combination makes the created see-through images quite understandable because we can highlight the 3D edges of visualized shapes as high-curvature areas. In addition, to make the combination more effective, we propose a new edge highlighting method applicable to 3D scanned point clouds. We call the method “opacity-based edge highlighting,” which appropriately utilizes the effect of transparency to make the 3D edge regions look clearer. The proposed method works well for both sharp (high-curvature) and soft (low-curvature) 3D edges. We show several experiments that demonstrate our method’s effectiveness by using real 3D scanned point clouds.

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“…Among the state-of-the-art adaptive neighborhood selection approaches, there are several methods including entropy-based [ 1 ], curvature-based [ 14 , 20 ], and omnivariance-based [ 21 ] techniques. Omnivariance-based, and entropy-based methods calculate local geometric features using various k neighborhood sizes.…”

Section: Introductionmentioning

confidence: 99%

Segmentation of LiDAR point cloud data in urban areas using adaptive neighborhood selection technique

Chakraborty,

Dey

2024

PLoS ONE

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Semantic segmentation of urban areas using Light Detection and Ranging (LiDAR) point cloud data is challenging due to the complexity, outliers, and heterogeneous nature of the input point cloud data. The machine learning-based methods for segmenting point clouds suffer from the imprecise computation of the training feature values. The most important factor that influences how precisely the feature values are computed is the neighborhood chosen by each point. This research addresses this issue and proposes a suitable adaptive neighborhood selection approach for individual points by completely considering the complex and heterogeneous nature of the input LiDAR point cloud data. The proposed approach is evaluated on high-density mobile and low-density aerial LiDAR point cloud datasets using the Random Forest machine learning classifier. In the context of performance evaluation, the proposed approach confirms the competitive performance over the state-of-the-art approaches. The computed accuracy and F1-score for the high-density Toronto and low-density Vaihingen datasets are greater than 91% and 82%, respectively.

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A Curvature Based Adaptive Neighborhood for Individual Point Cloud Classification

Cited by 11 publications

References 18 publications

Effective Selection of Variable Point Neighbourhood for Feature Point Extraction from Aerial Building Point Cloud Data

Effective Selection of Variable Point Neighbourhood for Feature Point Extraction from Aerial Building Point Cloud Data

Opacity-Based Edge Highlighting for Transparent Visualization of 3d Scanned Point Clouds

Segmentation of LiDAR point cloud data in urban areas using adaptive neighborhood selection technique

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