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Parallel Globally Consistent Normal Orientation of Raw Unorganized Point Clouds

Title data

Jakob, Johannes ; Buchenau, Christoph ; Guthe, Michael:
Parallel Globally Consistent Normal Orientation of Raw Unorganized Point Clouds.
In: Computer Graphics Forum. Vol. 38 (2019) Issue 5 . - pp. 163-173.
ISSN 1467-8659
DOI: https://doi.org/10.1111/cgf.13797

Abstract in another language

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.

Further data

Item Type: Article in a journal
Refereed: Yes
Additional notes: Best Paper Award - Honorable
Institutions of the University: Faculties > Faculty of Mathematics, Physics und Computer Science > Department of Computer Science > Professorship Applied Computer Science V > Professorship Applied Computer Science V - Univ.-Prof. Dr. Michael Guthe
Result of work at the UBT: Yes
DDC Subjects: 000 Computer Science, information, general works > 004 Computer science
Date Deposited: 18 Jul 2019 08:42
Last Modified: 18 Jul 2019 08:42
URI: https://eref.uni-bayreuth.de/id/eprint/51540