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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Vydáno v:Computer graphics forum Ročník 36; číslo 1; s. 197 - 208
Hlavní autoři: Schertler, Nico, Savchynskyy, Bogdan, Gumhold, Stefan
Médium: Journal Article
Jazyk:angličtina
Vydáno: Oxford Blackwell Publishing Ltd 01.01.2017
Témata:
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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Shrnutí: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.
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ISSN:0167-7055
1467-8659
DOI:10.1111/cgf.12795