Curvature estimation for scattered point cloud data

Zhang, Fan; Kang, Baosheng; Jian-dong, Zhao; Li, Juan

doi:10.3724/sp.j.1087.2013.01662

Cited by 4 publications

(5 citation statements)

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“…This article uses the quadratic surface fitting method to estimate curvature [6] . The specific steps of the algorithm are as follows:…”

Section: Estimation Of Point Cloud Curvaturementioning

confidence: 99%

An improved bilateral filtering based on curvature feature for point cloud denoising in road surface detection

Chen

2024

MIPPR 2023: Remote Sensing Image Processing, Geographic Information Systems, and Other Applications

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To effectively remove noise from the point clouds obtained for road surface defect detection while preserving the feature information, a bilateral filtering algorithm based on curvature features is proposed for denoising road surface defect point clouds. First, divide the noise points in the point cloud into several regions based on their distribution characteristics and curvature features. Then use statistical filtering to remove noise points in the obvious noise area and the flat area. Finally, an improved bilateral filtering algorithm is applied to smooth the noise points in the complex detail area.

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“…This article uses the quadratic surface fitting method to estimate curvature [6] . The specific steps of the algorithm are as follows:…”

Section: Estimation Of Point Cloud Curvaturementioning

confidence: 99%

An improved bilateral filtering based on curvature feature for point cloud denoising in road surface detection

Chen

2024

MIPPR 2023: Remote Sensing Image Processing, Geographic Information Systems, and Other Applications

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“…Based on the above method, the curvature histograms of the segmented subintervals P x and Q y can be obtained. And the similarity between the curvature histograms of P x and Q y can be obtained by using the Bhattacharyya distance [24].The formula for calculating the Bhattacharyya distance is shown in equation (5).…”

Section: ) Histogram Similaritymentioning

confidence: 99%

Fast and High Accuracy 3D Point Cloud Registration for Automatic Reconstruction From Laser Scanning Data

Rao

Fan

et al. 2023

IEEE Access

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Point cloud registration from laser scanning data is a technique to establish the mapping relationship between source and target point clouds, which has been widely used in automatic 3D reconstruction, pose estimation, localization, and navigation. While algorithms like Super4PCS and MSSF-4PCS can achieve registration without initial poses, they are relatively slow, less accurate, and require iterations. To address these issues, we propose a 3D point cloud registration algorithm based on interval segmentation and multi-dimensional feature. Firstly, the source and target point clouds are segmented internally and the point cloud curvature is designed to narrow down the search range for the registration between the segmented point clouds. Secondly, the corresponding four-point sets in the segmented areas of the source and target point clouds are determined using affine invariance constraints. Finally, a multidimensional feature vector based on curvature features and fast point feature histogram is established to determine the unique corresponding four-point set pairs, and the rigid body transformation matrix is solved accordingly. Our algorithm is tested on publicly available 3D point cloud data models Bunny, Dino, Dragon, and Horse from Stanford University. Results showed that our algorithm improved registration accuracy by 24.39% and registration efficiency by 46.21% compared to the MSSF-4PCS point cloud registration algorithm. Multiple sets of experimental results confirmed the effectiveness of our algorithm. The proposed 3D point cloud registration is proved to be fast with high accuracy, which can be utilized for automatic segmentation, reconstruction, and modelling from Laser Scanning Data.

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“…A popular discretization scheme for computing the Gaussian curvature takes the form (2π − P i )/G i , where G i is the sum of the neighbourhood triangular areas. G 1 is the sum of the areas of all the triangles, G 2 is the sum of the inner Borrelli [36] 0.6773 0.6920 0.6498 0.5712 Zhang [37] 0.0681 0.0883 0.0752 0.0543 Kalogerakis [38] 0.0354 0.0679 0.0547 0.0332 Boix (G2) [36] 0.0233 0.0565 0.0375 0.0275 Meyer(G4) [36] 0.0233 0.0565 0.0375 0.0275 Our 0.0234 0.0111 0.0061 0.0045 quadrilateral region composed of the orthocentric, and G 4 is the area of the Meyer-Voronoi region. The threshold range of qualified curvature is 0.5-1.5.…”

Section: Discrete Normal and Discrete Curvature Calculation Accuracymentioning

confidence: 99%

Feature extraction from point clouds for rigid aircraft part inspection using an improved Harris algorithm

Li¹,

Huang²,

Li³

et al. 2018

Meas. Sci. Technol.

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In the process of feature extraction, geometric information about feature points is based on the nearest 1-ring field points, which are usually the edge points of the face adjacent to the feature line. The high structural complexity of rigid aircraft components leads to recognition of false feature points. Therefore, this paper proposes an improved Harris algorithm designed to improve the accuracy of feature extraction. It combines the discrete curvature and the normal vector in a way that is suitable for use with point clouds. Key to this approach is the use of changes in the gradient rather than changes in the curvature to identify corner, plane, and crease points. Our method accurately eliminates pseudo-feature points that are too close to actual feature points in order to refine potential feature points. We demonstrate the efficiency and robustness of the proposed method by validating it against standard models and actual scan data and by comparing it with other feature detection algorithms.

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Curvature estimation for scattered point cloud data

Cited by 4 publications

References 0 publications

An improved bilateral filtering based on curvature feature for point cloud denoising in road surface detection

An improved bilateral filtering based on curvature feature for point cloud denoising in road surface detection

Fast and High Accuracy 3D Point Cloud Registration for Automatic Reconstruction From Laser Scanning Data

Feature extraction from point clouds for rigid aircraft part inspection using an improved Harris algorithm

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