Efficient spherical parametrization using progressive optimization

Authors:
Shenghua Wan;Tengfei Ye;Maoqing Li;Hongchao Zhang;Xin Li
Affiliations:
School of Electrical Engineering and Computer Science, Louisiana State University (LSU), Baton Rouge, LA;Department of Automation, Xiamen University, Xiamen, China;Department of Automation, Xiamen University, Xiamen, China;Department of Mathematics, LSU, Baton Rouge, LA;School of Electrical Engineering and Computer Science, Louisiana State University (LSU), Baton Rouge, LA
Venue:
CVM'12 Proceedings of the First international conference on Computational Visual Media
Year:
2012

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Abstract

Spherical mapping is a key enabling technology in modeling and processing genus-0 close surfaces. A closed genus-0 surface can be seamless parameterized onto a unit sphere. We develop an effective progressive optimization scheme to compute such a parametrization, minimizing a nonlinear energy balancing angle and area distortions. Among all existing state-of-the-art spherical mapping methods, the main advantage of our spherical mapping are two-folded: (1) the algorithm converges very efficiently, therefore it is suitable for handling huge geometric models, and (2) it generates bijective and lowly distorted mapping results.