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.
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Wan, S., Ye, T., Li, M., Zhang, H., Li, X. (2012). Efficient Spherical Parametrization Using Progressive Optimization. In: Hu, SM., Martin, R.R. (eds) Computational Visual Media. CVM 2012. Lecture Notes in Computer Science, vol 7633. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-34263-9_22
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DOI: https://doi.org/10.1007/978-3-642-34263-9_22
Publisher Name: Springer, Berlin, Heidelberg
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