Segmentation-based Multi-scale Edge Extraction to Measure the Persistence of Features in Unorganized Point Clouds

Bazazian, Dena; Casas, Josep R.; Ruiz-Hidalgo, Javier

doi:10.5220/0006092503170325

Cited by 6 publications

(5 citation statements)

References 29 publications

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“…In view of this, some scholars carry out plane segmentation of three-dimensional building point clouds to obtain multiple plane point clouds, then convert the plane point clouds into images [7], and finally the image boundary extraction method is used to obtain the building outline [8], such as with the Line Segment Detector (LSD) method [9] and the Canny algorithm [10]. Bazazian et al [11] uses the region growth combined with geodesic distance for the region segmentation of point clouds, and defines a multi-scale operator to determine which feature points are continuous. By the same token, the normal vector difference of adjacent points can be used as a multi-scale operator to detect the feature points [12].…”

Section: Related Workmentioning

confidence: 99%

Contour Extraction of UAV Point Cloud Based on Neighborhood Geometric Features of Multi-Level Growth Plane

Chen,

An,

Zhao

et al. 2024

Drones

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The extraction of UAV building point cloud contour points is the basis for the expression of a three-dimensional lightweight building outline. Previous unmanned aerial vehicle (UAV) building point cloud contour extraction methods have mainly focused on the expression of the roof contour, but did not extract the wall contour. In view of this, an algorithm based on the geometric features of the neighborhood points of the region-growing clustering fusion surface is proposed to extract the boundary points of the UAV building point cloud. Firstly, the region growth plane is fused to obtain a more accurate segmentation plane. Then, the neighboring points are projected onto the neighborhood plane and a vector between the object point and neighborhood point is constructed. Finally, the azimuth of each vector is calculated, and the boundary points of each segmented plane are extracted according to the difference in adjacent azimuths. Experiment results show that the best boundary points can be extracted when the number of adjacent points is 24 and the difference in adjacent azimuths is 120. The proposed method is superior to other methods in the contour extraction of UAV buildings point clouds. Moreover, it can extract not only the building roof contour points, but also the wall contour points, including the window contour points.

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Section: Related Workmentioning

confidence: 99%

Contour Extraction of UAV Point Cloud Based on Neighborhood Geometric Features of Multi-Level Growth Plane

Chen,

An,

Zhao

et al. 2024

Drones

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show abstract

“…To overcome this issue, computing the difference of eigenvalues was proposed to extract edges [12]. The same idea was extended based on a segmentation [16] approach. Furthermore, discriminative learning algorithms are employed to extract edges from unorganized point clouds through a point classifier based on the edge versus non-edge points [2].…”

Section: Edge Detectionmentioning

confidence: 99%

“…However, applying CNNs for the edge detection problem in point clouds is still challenging. The majority of edge detection techniques are based on signal processing and local geometry properties [1][2][3][12][13][14][15][16], although recently there was some research that applied deep learning techniques for edge detection from point clouds [17][18][19][20][21][22]. In this paper, we introduce a novel approach for edge detection from point clouds based on the main concepts of capsule networks [23].…”

Section: Introductionmentioning

confidence: 99%

EDC-Net: Edge Detection Capsule Network for 3D Point Clouds

Bazazian

Parés

2021

Applied Sciences

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Edge features in point clouds are prominent due to the capability of describing an abstract shape of a set of points. Point clouds obtained by 3D scanner devices are often immense in terms of size. Edges are essential features in large scale point clouds since they are capable of describing the shapes in down-sampled point clouds while maintaining the principal information. In this paper, we tackle challenges of edge detection tasks in 3D point clouds. To this end, we propose a novel technique to detect edges of point clouds based on a capsule network architecture. In this approach, we define the edge detection task of point clouds as a semantic segmentation problem. We built a classifier through the capsules to predict edge and non-edge points in 3D point clouds. We applied a weakly-supervised learning approach in order to improve the performance of our proposed method and built in the capability of testing the technique in wider range of shapes. We provide several quantitative and qualitative experimental results to demonstrate the robustness of our proposed EDC-Net for edge detection in 3D point clouds. We performed a statistical analysis over the ABC and ShapeNet datasets. Our numerical results demonstrate the robust and efficient performance of EDC-Net.

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“…, Change of curvature (γ 7 i ) as explained in [36], [37] and Sharp Edges (γ 8 i ) as identified in [18], [38].…”

Section: Input Featuresmentioning

confidence: 99%

DCG-Net: Dynamic Capsule Graph Convolutional Network for Point Clouds

Bazazian

Nahata

2020

IEEE Access

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This paper introduces DCG-Net (Dynamic Capsule Graph Network) to analyze point clouds for the tasks of classification and segmentation. DCG-Net aggregates point cloud features to build and update the graphs based on the dynamic routing mechanism of capsule networks at each layer of a convolutional network. The first layer of DGC-Net exploits the geometrical attributes of the point cloud to build a graph by neighborhood aggregation while the deeper layers of the network dynamically update the graph based on the feature space of convolutions. We conduct extensive experiments on public datasets, ModelNet40, ShapeNet-Part. Our experimental results demonstrate that DCG-Net achieves state-of-theart performance on public datasets, 93.4% accuracy on ModelNet40, and 85.4% instance mIoU (mean Intersection over Union) on ShapeNet-Part.

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Segmentation-based Multi-scale Edge Extraction to Measure the Persistence of Features in Unorganized Point Clouds

Cited by 6 publications

References 29 publications

Contour Extraction of UAV Point Cloud Based on Neighborhood Geometric Features of Multi-Level Growth Plane

Contour Extraction of UAV Point Cloud Based on Neighborhood Geometric Features of Multi-Level Growth Plane

EDC-Net: Edge Detection Capsule Network for 3D Point Clouds

DCG-Net: Dynamic Capsule Graph Convolutional Network for Point Clouds

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