Towards Globally Optimal Normal Orientations for Large Point Clouds

Various processing algorithms on point set surfaces rely on consistently oriented normals (e.g. Poisson surface reconstruction). While several approaches exist for the calculation of normal directions, in most cases, their orientation has to be determined in a subsequent step. This paper generalizes...

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Veröffentlicht in:	Computer graphics forum Jg. 36; H. 1; S. 197 - 208
Hauptverfasser:	Schertler, Nico, Savchynskyy, Bogdan, Gumhold, Stefan
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	Oxford Blackwell Publishing Ltd 01.01.2017
Schlagworte:	Algorithms computational geometry digital geometry processing Energy conservation Graphs I.3.5 [Computer Graphics]: Computational Geometry and Object Modelling‐Geometric algorithms, languages and systems Mathematical analysis modelling Optimization Orientation Patches (structures) Propagation Reconstruction Solvers Tasks Three dimensional models
ISSN:	0167-7055, 1467-8659
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Abstract	Various processing algorithms on point set surfaces rely on consistently oriented normals (e.g. Poisson surface reconstruction). While several approaches exist for the calculation of normal directions, in most cases, their orientation has to be determined in a subsequent step. This paper generalizes propagation‐based approaches by reformulating the task as a graph‐based energy minimization problem. By applying global solvers, we can achieve more consistent orientations than simple greedy optimizations. Furthermore, we present a streaming‐based framework for orienting large point clouds. This framework orients patches locally and generates a globally consistent patch orientation on a reduced neighbour graph, which achieves similar quality to orienting the full graph. Various processing algorithms on point set surfaces rely on consistently oriented normals (e.g. Poisson surface reconstruction).While several approaches exist for the calculation of normal directions, in most cases, their orientation has to be determined in a subsequent step. This paper generalizes propagation‐based approaches by reformulating the task as a graph‐based energy minimization problem and presents a streaming‐based out‐of‐core implementation.
AbstractList	Various processing algorithms on point set surfaces rely on consistently oriented normals (e.g. Poisson surface reconstruction). While several approaches exist for the calculation of normal directions, in most cases, their orientation has to be determined in a subsequent step. This paper generalizes propagation-based approaches by reformulating the task as a graph-based energy minimization problem. By applying global solvers, we can achieve more consistent orientations than simple greedy optimizations. Furthermore, we present a streaming-based framework for orienting large point clouds. This framework orients patches locally and generates a globally consistent patch orientation on a reduced neighbour graph, which achieves similar quality to orienting the full graph. Various processing algorithms on point set surfaces rely on consistently oriented normals (e.g. Poisson surface reconstruction).While several approaches exist for the calculation of normal directions, in most cases, their orientation has to be determined in a subsequent step. This paper generalizes propagation-based approaches by reformulating the task as a graph-based energy minimization problem and presents a streaming-based out-of-core implementation. Various processing algorithms on point set surfaces rely on consistently oriented normals (e.g. Poisson surface reconstruction). While several approaches exist for the calculation of normal directions, in most cases, their orientation has to be determined in a subsequent step. This paper generalizes propagation‐based approaches by reformulating the task as a graph‐based energy minimization problem. By applying global solvers, we can achieve more consistent orientations than simple greedy optimizations. Furthermore, we present a streaming‐based framework for orienting large point clouds. This framework orients patches locally and generates a globally consistent patch orientation on a reduced neighbour graph, which achieves similar quality to orienting the full graph. Various processing algorithms on point set surfaces rely on consistently oriented normals (e.g. Poisson surface reconstruction).While several approaches exist for the calculation of normal directions, in most cases, their orientation has to be determined in a subsequent step. This paper generalizes propagation‐based approaches by reformulating the task as a graph‐based energy minimization problem and presents a streaming‐based out‐of‐core implementation. Various processing algorithms on point set surfaces rely on consistently oriented normals (e.g. Poisson surface reconstruction). While several approaches exist for the calculation of normal directions, in most cases, their orientation has to be determined in a subsequent step. This paper generalizes propagation-based approaches by reformulating the task as a graph-based energy minimization problem. By applying global solvers, we can achieve more consistent orientations than simple greedy optimizations. Furthermore, we present a streaming-based framework for orienting large point clouds. This framework orients patches locally and generates a globally consistent patch orientation on a reduced neighbour graph, which achieves similar quality to orienting the full graph.
Author	Schertler, Nico Savchynskyy, Bogdan Gumhold, Stefan
Author_xml	– sequence: 1 givenname: Nico surname: Schertler fullname: Schertler, Nico email: nico.schertler@tu-dresden.de organization: TU Dresden – sequence: 2 givenname: Bogdan surname: Savchynskyy fullname: Savchynskyy, Bogdan email: bogdan.savchynskyy@tu-dresden.de organization: TU Dresden – sequence: 3 givenname: Stefan surname: Gumhold fullname: Gumhold, Stefan email: stefan.gumhold@tu-dresden.de organization: TU Dresden
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SubjectTerms	Algorithms computational geometry digital geometry processing Energy conservation Graphs I.3.5 [Computer Graphics]: Computational Geometry and Object Modelling‐Geometric algorithms, languages and systems Mathematical analysis modelling Optimization Orientation Patches (structures) Propagation Reconstruction Solvers Tasks Three dimensional models
Title	Towards Globally Optimal Normal Orientations for Large Point Clouds
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