Sparse-Data Based 3D Surface Reconstruction for Cartoon and Map

Wu, Bin; Rahman, Talal; Tai, Xue-Cheng
Chapter, Peer reviewed
Accepted version
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https://hdl.handle.net/11250/2734672
Date
2018
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Original version
Wu, B., Rahman, T., & Tai, X.-C. (2018). Sparse-data based 3d surface reconstruction for cartoon and map. In X.-C. Tai, E. Bae, & M. Lysaker (Eds.), Imaging, vision and learning based on optimization and pdes (pp. 47-64). Springer.   10.1007/978-3-319-91274-5_3
Abstract
A model combining the first-order and the second-order variational regularizations for the purpose of 3D surface reconstruction based on 2D sparse data is proposed. The model includes a hybrid fidelity constraint which allows the initial conditions to be switched flexibly between vectors and elevations. A numerical algorithm based on the augmented Lagrangian method is also proposed. The numerical experiments are presented, showing its excellent performance both in designing cartoon characters, as well as in recovering oriented three dimensional maps from contours or points with elevation information.
Publisher
Springer
Copyright
This is an author's accepted manuscript (postprint) of a chapter published by Springer in the Mathematics and Visualization series on 20 November 2018. The final, authenticated version is available at https://doi.org/10.1007/978-3-319-91274-5_3