Feature line extraction from unorganized noisy point clouds using truncated Fourier series

Altantsetseg, Enkhbayar; Muraki, Yuta; Matsuyama, Katsutsugu; Konno, Kouichi

doi:10.1007/s00371-013-0800-x

Cited by 32 publications

(14 citation statements)

References 18 publications

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“…Some approaches [14,[36][37][38][39][40] employ surface meshes or point-based surfaces, which can detect 3D edges or extract feature lines from some irregular objects and more complex surfaces. However, these methods are only applied to a small-scale 3D-point cloud with a single object.…”

Section: Overviewmentioning

confidence: 99%

Edge Detection and Feature Line Tracing in 3D-Point Clouds by Analyzing Geometric Properties of Neighborhoods

et al. 2016

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This paper presents an automated and effective method for detecting 3D edges and tracing feature lines from 3D-point clouds. This method is named Analysis of Geometric Properties of Neighborhoods (AGPN), and it includes two main steps: edge detection and feature line tracing. In the edge detection step, AGPN analyzes geometric properties of each query point's neighborhood, and then combines RANdom SAmple Consensus (RANSAC) and angular gap metric to detect edges. In the feature line tracing step, feature lines are traced by a hybrid method based on region growing and model fitting in the detected edges. Our approach is experimentally validated on complex man-made objects and large-scale urban scenes with millions of points. Comparative studies with state-of-the-art methods demonstrate that our method obtains a promising, reliable, and high performance in detecting edges and tracing feature lines in 3D-point clouds. Moreover, AGPN is insensitive to the point density of the input data.

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Section: Overviewmentioning

confidence: 99%

Edge Detection and Feature Line Tracing in 3D-Point Clouds by Analyzing Geometric Properties of Neighborhoods

et al. 2016

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“…Continuous feature lines are then formed by graph‐based methods, such as constructing and pruning a minimal spanning tree. Beside PCA, local shape information can also be learned using the geometry of Voronoi cells [MOG09], clustering estimated point normals [WHH10] or fitting truncated Fourier series [AMMK13]. While these methods use existing points as candidates of feature points, others (like us) create new feature locations.…”

Section: Related Workmentioning

confidence: 99%

Extracting Sharp Features from RGB‐D Images

Cao

et al. 2016

Computer Graphics Forum

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Sharp edges are important shape features and their extraction has been extensively studied both on point clouds and surfaces. We consider the problem of extracting sharp edges from a sparse set of colour‐and‐depth (RGB‐D) images. The noise‐ridden depth measurements are challenging for existing feature extraction methods that work solely in the geometric domain (e.g. points or meshes). By utilizing both colour and depth information, we propose a novel feature extraction method that produces much cleaner and more coherent feature lines. We make two technical contributions. First, we show that intensity edges can augment the depth map to improve normal estimation and feature localization from a single RGB‐D image. Second, we designed a novel algorithm for consolidating feature points obtained from multiple RGB‐D images. By utilizing normals and ridge/valley types associated with the feature points, our algorithm is effective in suppressing noise without smearing nearby features.

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“…For curvature estimation of point cloud, several algorithms are proposed [18,19]. In [20], a method to calculate the curvature of the point Figure 2: Flowchart of our algorithm cloud is proposed. Specifically, the method represents shape features of the valley and the ridge lines by a frequency-domain.…”

Section: Curvature Calculation Of Point Cloudmentioning

confidence: 99%

Feature-Preserving Simplification of Point Cloud by Using Clustering Approach Based on Mean Curvature

Yang

Matsuyama

Konno

et al. 2015

The Journal of the Society for Art and Science

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For point cloud data obtained from 3D scanning devices, excessively large storage and long postprocessing time are required. Due to this, it is very important to simplify the point cloud to reduce calculation cost. In this paper, we propose a new point cloud simplification method that can maintain the characteristics of surface shape for unstructured point clouds. In our method, a segmentation range based on mean curvature of point cloud can be controlled. The simplification process is completed by maintaining the position of the representative point and removing the represented points using the range. Our method can simplify results with highly simplified rate with preserving the form feature. Applying the proposed method to 3D stone tool models, the method is evaluated precisely and effectively.

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Feature line extraction from unorganized noisy point clouds using truncated Fourier series

Cited by 32 publications

References 18 publications

Edge Detection and Feature Line Tracing in 3D-Point Clouds by Analyzing Geometric Properties of Neighborhoods

Edge Detection and Feature Line Tracing in 3D-Point Clouds by Analyzing Geometric Properties of Neighborhoods

Extracting Sharp Features from RGB‐D Images

Feature-Preserving Simplification of Point Cloud by Using Clustering Approach Based on Mean Curvature

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