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    • 38-Issue 5
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    Parallel Globally Consistent Normal Orientation of Raw Unorganized Point Clouds

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    Date
    2019
    Author
    Jakob, Johannes ORCID
    Buchenau, Christoph ORCID
    Guthe, Michael ORCID
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    Abstract
    A mandatory component for many point set algorithms is the availability of consistently oriented vertex-normals (e.g. for surface reconstruction, feature detection, visualization). Previous orientation methods on meshes or raw point clouds do not consider a global context, are often based on unrealistic assumptions, or have extremely long computation times, making them unusable on real-world data. We present a novel massively parallelized method to compute globally consistent oriented point normals for raw and unsorted point clouds. Built on the idea of graph-based energy optimization, we create a complete kNN-graph over the entire point cloud. A new weighted similarity criterion encodes the graph-energy. To orient normals in a globally consistent way we perform a highly parallel greedy edge collapse, which merges similar parts of the graph and orients them consistently. We compare our method to current state-of-the-art approaches and achieve speedups of up to two orders of magnitude. The achieved quality of normal orientation is on par or better than existing solutions, especially for real-world noisy 3D scanned data.
    BibTeX
    @article {10.1111:cgf.13797,
    journal = {Computer Graphics Forum},
    title = {{Parallel Globally Consistent Normal Orientation of Raw Unorganized Point Clouds}},
    author = {Jakob, Johannes and Buchenau, Christoph and Guthe, Michael},
    year = {2019},
    publisher = {The Eurographics Association and John Wiley & Sons Ltd.},
    ISSN = {1467-8659},
    DOI = {10.1111/cgf.13797}
    }
    URI
    https://doi.org/10.1111/cgf.13797
    https://diglib.eg.org:443/handle/10.1111/cgf13797
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