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dc.contributor.authorJakob, Johannesen_US
dc.contributor.authorBuchenau, Christophen_US
dc.contributor.authorGuthe, Michaelen_US
dc.contributor.editorBommes, David and Huang, Huien_US
dc.description.abstractA 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.en_US
dc.publisherThe Eurographics Association and John Wiley & Sons Ltd.en_US
dc.subjectComputing methodologies
dc.subjectShape analysis
dc.subjectTheory of computation
dc.subjectComputational geometry
dc.subjectMassively parallel algorithms
dc.titleParallel Globally Consistent Normal Orientation of Raw Unorganized Point Cloudsen_US
dc.description.seriesinformationComputer Graphics Forum
dc.description.sectionheaders2D and 3D Reconstruction

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  • 38-Issue 5
    Geometry Processing 2019 - Symposium Proceedings

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