Parallel Globally Consistent Normal Orientation of Raw Unorganized Point Clouds

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 unreali...

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Vydané v:Computer graphics forum Ročník 38; číslo 5; s. 163 - 173
Hlavní autori: Jakob, J., Buchenau, C., Guthe, M.
Médium: Journal Article
Jazyk:English
Vydavateľské údaje: Oxford Blackwell Publishing Ltd 01.08.2019
Predmet:
Algorithms
CCS Concepts
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Computing methodologies → Shape analysis
Optimization
Orientation
Parallel processing
Theory of computation → Computational geometry; Massively parallel algorithms
ISSN:0167-7055, 1467-8659
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Popis
Shrnutí: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.
Bibliografia:ObjectType-Article-1
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content type line 14
ISSN:0167-7055
1467-8659
DOI:10.1111/cgf.13797