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Single Image Tree Reconstruction via Adversarial Network

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2021

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Liu, Zhihao
Wu, Kai
Wang, Yunhai
Cheng, Zhanglin

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Graphical Models. Elsevier. 2021, 117, 101115. ISSN 1524-0703. eISSN 1524-0711. Available under: doi: 10.1016/j.gmod.2021.101115

Zusammenfassung

Realistic 3D tree reconstruction is still a tedious and time-consuming task in the graphics community. In this paper, we propose a simple and efficient method for reconstructing 3D tree models with high fidelity from a single image. The key to single image-based tree reconstruction is to recover 3D shape information of trees via a deep neural network learned from a set of synthetic tree models. We adopted a conditional generative adversarial network (cGAN) to infer the 3D silhouette and skeleton of a tree respectively from edges extracted from the image and simple 2D strokes drawn by the user. Based on the predicted 3D silhouette and skeleton, a realistic tree model that inherits the tree shape in the input image can be generated using a procedural modeling technique. Experiments on varieties of tree examples demonstrate the efficiency and effectiveness of the proposed method in reconstructing realistic 3D tree models from a single image.

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ISO 690LIU, Zhihao, Kai WU, Jianwei GUO, Yunhai WANG, Oliver DEUSSEN, Zhanglin CHENG, 2021. Single Image Tree Reconstruction via Adversarial Network. In: Graphical Models. Elsevier. 2021, 117, 101115. ISSN 1524-0703. eISSN 1524-0711. Available under: doi: 10.1016/j.gmod.2021.101115
BibTex
@article{Liu2021Singl-54269,
  year={2021},
  doi={10.1016/j.gmod.2021.101115},
  title={Single Image Tree Reconstruction via Adversarial Network},
  volume={117},
  issn={1524-0703},
  journal={Graphical Models},
  author={Liu, Zhihao and Wu, Kai and Guo, Jianwei and Wang, Yunhai and Deussen, Oliver and Cheng, Zhanglin},
  note={Article Number: 101115}
}
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