Parallel Globally Consistent Normal Orientation of Raw Unorganized Point Clouds
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Date
2019
Journal Title
Journal ISSN
Volume Title
Publisher
The Eurographics Association and John Wiley & Sons Ltd.
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.
Description
@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}
}