Title
Computing sparse cones with bounded distortion for conformal parameterizations
Abstract
AbstractWe propose a novel method to generate sparse cone singularities with bounded distortion constraints for conformal parameterizations. It is formulated as minimizing the ℓ0-norm of Gaussian curvature of vertices with hard constraints of bounding the distortion that is measured by the ℓ2-norm of the log conformal factor. We use the reweighted ℓ1-norm to approximate the ℓ0-norm and solve each convex weighted ℓ1 minimization subproblem by the Douglas-Rachford (DR) splitting scheme. To quickly generate sparse cones, we modify DR splitting by weighting the ℓ2-norm of the proximal mapping to force the small Gaussian curvature to quickly approach zero. Accordingly, compared with the conventional DR splitting, the modified method performs one to two orders of magnitude faster. Besides, we perform variable substitution of log conformal factors to simplify the computation process for acceleration. Our algorithm is able to bound distortion to compute sparse cone singularities, so that the resulting conformal parameterizations achieve a favorable tradeoff between the area distortion and the number of cones. We demonstrate its effectiveness and feasibility on a large number of models.
Year
DOI
Venue
2021
10.1145/3478513.3480526
ACM Transactions on Graphics
Keywords
DocType
Volume
conformal parameterizations, cone singularities, bounded distortion, l(0)-norm optimization
Journal
40
Issue
ISSN
Citations 
6
0730-0301
0
PageRank 
References 
Authors
0.34
0
5
Name
Order
Citations
PageRank
Qing Fang101.01
Wenqing Ouyang201.01
Mo Li331.79
Ligang Liu400.34
Xiao-Ming Fu59513.80