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

Bin Wu; Talal Rahman; Xue-Cheng TAI

doi:10.1007/978-3-319-91274-5_3

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

Bin Wu^*, Talal Rahman, Xue-Cheng TAI

^*Corresponding author for this work

Department of Mathematics

Research output: Contribution to journal › Conference article › peer-review

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.

Original language	English
Pages (from-to)	47-64
Number of pages	18
Journal	Mathematics and Visualization
Volume	0
Issue number	221219
DOIs	https://doi.org/10.1007/978-3-319-91274-5_3
Publication status	Published - 2018
Event	International conference on Imaging, Vision and Learning Based on Optimization and PDEs, IVLOPDE 2016 - Bergen, Norway Duration: 29 Aug 2016 → 2 Sept 2016

Scopus Subject Areas

Modelling and Simulation
Geometry and Topology
Computer Graphics and Computer-Aided Design
Applied Mathematics

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10.1007/978-3-319-91274-5_3

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title = "Sparse-Data Based 3D Surface Reconstruction for Cartoon and Map",

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.",

author = "Bin Wu and Talal Rahman and Xue-Cheng TAI",

note = "Funding Information: Acknowledgements XC Tai acknowledges the support through ISP-Matematikk (Project no. 239033/F20). The providing us example strokes.; International conference on Imaging, Vision and Learning Based on Optimization and PDEs, IVLOPDE 2016 ; Conference date: 29-08-2016 Through 02-09-2016",

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AU - Wu, Bin

AU - Rahman, Talal

AU - TAI, Xue-Cheng

N1 - Funding Information: Acknowledgements XC Tai acknowledges the support through ISP-Matematikk (Project no. 239033/F20). The providing us example strokes.

PY - 2018

Y1 - 2018

N2 - 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.

AB - 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.

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U2 - 10.1007/978-3-319-91274-5_3

DO - 10.1007/978-3-319-91274-5_3

M3 - Conference article

AN - SCOPUS:85057492426

SN - 1612-3786

VL - 0

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JO - Mathematics and Visualization

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IS - 221219

T2 - International conference on Imaging, Vision and Learning Based on Optimization and PDEs, IVLOPDE 2016

Y2 - 29 August 2016 through 2 September 2016

ER -

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

Abstract

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