Volume 40, Issue 6 pp. 288-303
Article

Neural Modelling of Flower Bas-relief from 2D Line Drawing

Yu-Wei Zhang

Corresponding Author

Yu-Wei Zhang

School of Mechanical and Automotive Engineering, Qilu University of Technology (Shandong Academy of Sciences), Jinan, China

[email protected]

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Jinlei Wang

Jinlei Wang

School of Mechanical and Automotive Engineering, Qilu University of Technology (Shandong Academy of Sciences), Jinan, China

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Wenping Wang

Wenping Wang

Department of Computer Science, the University of Hong Kong, Hong Kong, China

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Yanzhao Chen

Yanzhao Chen

School of Mechanical and Automotive Engineering, Qilu University of Technology (Shandong Academy of Sciences), Jinan, China

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Hui Liu

Hui Liu

School of Computer Science and Technology, Shandong University of Finance and Economics, Jinan, China

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Zhongping Ji

Zhongping Ji

Institute of Graphics and Image, Hangzhou Dianzi University, Hangzhou, China

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Caiming Zhang

Caiming Zhang

School of Computer Science and Technology, Shandong University, Jinan, China

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First published: 24 May 2021

Abstract

Different from other types of bas-reliefs, a flower bas-relief contains a large number of depth-discontinuity edges. Most existing line-based methods reconstruct free-form surfaces by ignoring the depth-discontinuities, thus are less efficient in modeling flower bas-reliefs. This paper presents a neural-based solution which benefits from the recent advances in CNN. Specially, we use line gradients to encode the depth orderings at leaf edges. Given a line drawing, a heuristic method is first proposed to compute 2D gradients at lines. Line gradients and dense curvatures interpolated from sparse user inputs are then fed into a neural network, which outputs depths and normals of the final bas-relief. In addition, we introduce an object-based method to generate flower bas-reliefs and line drawings for network training. Extensive experiments show that our method is effective in modelling bas-reliefs with depth-discontinuity edges. User evaluation also shows that our method is intuitive and accessible to common users.

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