An edge-sensitive simplification method for scanned point clouds

Liu, Shifan; Liang, Jin; Ren, Ming; He, JingBin; Gong, Chunyuan; Wang, Lu; Ze-hua, Miao

doi:10.1088/1361-6501/ab5e00

Cited by 13 publications

(8 citation statements)

References 31 publications

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“…Mérigot et al [10] replace traditional covariance as voronoi covariance to further investigate the relationship between the sharp feature and covariance matrix. Besides, some researchers [11][12][13] attempt to convert point-wise selection into a clustering problem, and extract the edges by identifying those points at the intersection among those clusters. Those clustering attributes of each point are usually associated with the covariance matrix.…”

Section: Related Workmentioning

confidence: 99%

Edge detection for 3D point clouds via locally max-angular gaps descriptor

Ma,

Zhang,

Chen

et al. 2023

Meas. Sci. Technol.

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Numerous point-cloud-based applications, including surface reconstruction and completion, greatly benefit from the utilization of edge features, which play a crucial role in structuring the target shape. Nevertheless, the current limitations of point cloud edge detection techniques in effectiveness and efficiency have motivated us to develop a more robust 3D edge detection method. In this paper, we introduce a simple yet effective descriptor for evaluating the edge level of 3D point clouds. This descriptor is defined as the variance of the max-angular gaps between the target point and its k nearest neighboring points. Leveraging this descriptor, we further propose a multiscale feature fusion strategy that well detect the edge features for the target point cloud. Experimental results demonstrate the superiority of our method over other comparison methods in terms of effectiveness and stability in preserving point cloud edges. Moreover, we investigate the applications of our method in point cloud simplification and registration to provide additional insights into its potential use in some point-cloud-based downstream tasks.

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Section: Related Workmentioning

confidence: 99%

Edge detection for 3D point clouds via locally max-angular gaps descriptor

Ma,

Zhang,

Chen

et al. 2023

Meas. Sci. Technol.

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“…The method proposes using a metric based on entropy estimation for clustering the point cloud. Liu et al [ 13 ] presented an edge-sensitive feature detail preserving algorithm; they used two clustering schemas to split the point cloud into the geometric and spatial domains. These methods can preserve global structures of the point clouds, and some of them preserve sharp features; however, because of the clustering process, they are computational time-consuming.…”

Section: Related Workmentioning

confidence: 99%

A Saliency-Based Sparse Representation Method for Point Cloud Simplification

Leal

Sánchez-Torres

Bedoya

et al. 2021

Sensors

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High-resolution 3D scanning devices produce high-density point clouds, which require a large capacity of storage and time-consuming processing algorithms. In order to reduce both needs, it is common to apply surface simplification algorithms as a preprocessing stage. The goal of point cloud simplification algorithms is to reduce the volume of data while preserving the most relevant features of the original point cloud. In this paper, we present a new point cloud feature-preserving simplification algorithm. We use a global approach to detect saliencies on a given point cloud. Our method estimates a feature vector for each point in the cloud. The components of the feature vector are the normal vector coordinates, the point coordinates, and the surface curvature at each point. Feature vectors are used as basis signals to carry out a dictionary learning process, producing a trained dictionary. We perform the corresponding sparse coding process to produce a sparse matrix. To detect the saliencies, the proposed method uses two measures, the first of which takes into account the quantity of nonzero elements in each column vector of the sparse matrix and the second the reconstruction error of each signal. These measures are then combined to produce the final saliency value for each point in the cloud. Next, we proceed with the simplification of the point cloud, guided by the detected saliency and using the saliency values of each point as a dynamic clusterization radius. We validate the proposed method by comparing it with a set of state-of-the-art methods, demonstrating the effectiveness of the simplification method.

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“…Abdul Rahman El Sayed et al [15] proposed a simplified algorithm based on weighted graph representation, which first used the significant characteristics of each shape vertex to identify the geometric region and identify the feature points in the region. Other methods have been proposed [16], [17], [18], [19] based on feature point retention to simplify the point cloud, in which the feature point retention optimization has been limited in some aspects. Numerous works [20], [21], [22], [23], [24], [25], [26], [27] have also used the clustering method to simplify point clouds from different aspects, which was found to have an effect on simplification, but the number of calculations remained large.…”

Section: Introductionmentioning

confidence: 99%

A New Simplification Algorithm for Point Cloud Based on the Vertical Plane Constraint and Moving Window

Liu

Peng

2022

IEEE Access

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Using three-dimensional spatial information, this method constructs the detection condition, compares the conditions to be detected, formed by the points in the file to be simplified, then determines the redundancy of the points. Moving windows are used to promote the operation of the algorithm and generate many tiny approximate vertical planes, from which the simplified points will be generated. By selecting experimental data on rabbit and horse and comparing the methods based on mesh and curvature, the proposed method increased the simplification rate of rabbit and horse from 3.022% and 11.123% (mesh method), and 5.704% and 15.316% (curvature method), to 83.387% and 84.296%, respectively, while the standard deviation was reduced from 0.01051 and 0.0157 (mesh method), and 0.0817 and 0.0013 (curvature method), to 0.02179 and 0.01507, respectively. For the simplification of multiple objects, the proposed method increased the simplification rate from 89.113% and 91.826% (mesh method), and 84.79% and 88.91% (curvature method), to 93.458% and 96.916%, respectively, reducing the time by approximately 20 s.

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An edge-sensitive simplification method for scanned point clouds

Cited by 13 publications

References 31 publications

Edge detection for 3D point clouds via locally max-angular gaps descriptor

Edge detection for 3D point clouds via locally max-angular gaps descriptor

A Saliency-Based Sparse Representation Method for Point Cloud Simplification

A New Simplification Algorithm for Point Cloud Based on the Vertical Plane Constraint and Moving Window

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