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• W3128252107 abstract "Recent 3-D scanning techniques can produce various kinds of digitized 3-D data. Most of these scanned data are in a format of unstructured point clouds. Such low-level representation of 3-D data usually contains only geometric properties (point positions), while lacking higher level structure cues, for example, feature lines. Feature lines can be defined as a visually prominent characteristic of the shape, including edges, ridges, and valley lines in multiple scales, which can support a lot of downstream applications, such as shape reconstruction and analysis. We present a two-phase algorithm for extracting line-type features on point clouds. To extract both large-scale and shallow feature lines, we first define a statistical metric to detect all potential feature points while immune to the noise to some extent. Then, for correctly reconstructing the feature lines from these identified coarse feature points, we introduce an anisotropic contracting scheme to force feature points lying on the underlying real feature lines. To illustrate the reliability of our method, various experiments have been conducted on both synthetic and raw data. Both visual and quantitative comparisons show that our method is robust to noise and can correctly extract multiscale feature lines. In addition, our method is generally applicable to robotic picking. <italic xmlns:mml=http://www.w3.org/1998/Math/MathML xmlns:xlink=http://www.w3.org/1999/xlink>Note to Practitioners</i> —This article was motivated by the problem of the feature line extraction for real scanned point clouds. Feature lines, as one kind of the most important structure information, depict the basic shape of the real object in our life. Extracting this kind of shape features from the unstructured point clouds can facilitate a variety of downstream practical applications, such as product design, workpiece manufacturing, and robotic grasping. Existing approaches to detect features either heavily rely on differential quantities, which are sensitive to the noise, or need an elaborately designed local descriptor but fail to recognize small-scale features. These challenges motivate us to design a new approach aiming at extracting multiscale feature lines while keeping robustness to heavy noise. The technique developed in this work can produce high-quality feature points and feature lines, which would serve as higher level structural information and facilitate many applications. Additional applications in 6-degree-of-freedom (6-DoF) pose estimation demonstrate the potential of our method for robotic picking." @default.
• W3128252107 created "2021-02-15" @default.
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• W3128252107 date "2022-04-01" @default.
• W3128252107 modified "2023-10-17" @default.
• W3128252107 title "Multiscale Feature Line Extraction From Raw Point Clouds Based on Local Surface Variation and Anisotropic Contraction" @default.
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• W3128252107 doi "https://doi.org/10.1109/tase.2021.3053006" @default.
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