Editable Parametric Dense Foliage from 3D Capture

Abstract

We present an algorithm to compute parametric models of dense foliage. The guiding principles of our work are automatic reconstruction and compact artist friendly representation. We use Bezier patches to model leaf surface, which we compute from images and point clouds of dense foliage. We present an algorithm to segment individual leaves from colour and depth data. We then reconstruct the Bezier representation from segmented leaf points clouds using non-linear optimisation. Unlike previous work, we do not require laboratory scanned exemplars or user intervention. We also demonstrate intuitive manipulators to edit the reconstructed parametric models. We believe our work is a step towards making captured data more accessible to artists for foliage modelling.

Cite

Text

Chaurasia and Beardsley. "Editable Parametric Dense Foliage from 3D Capture." International Conference on Computer Vision, 2017. doi:10.1109/ICCV.2017.567

Markdown

[Chaurasia and Beardsley. "Editable Parametric Dense Foliage from 3D Capture." International Conference on Computer Vision, 2017.](https://mlanthology.org/iccv/2017/chaurasia2017iccv-editable/) doi:10.1109/ICCV.2017.567

BibTeX

@inproceedings{chaurasia2017iccv-editable,
  title     = {{Editable Parametric Dense Foliage from 3D Capture}},
  author    = {Chaurasia, Gaurav and Beardsley, Paul},
  booktitle = {International Conference on Computer Vision},
  year      = {2017},
  doi       = {10.1109/ICCV.2017.567},
  url       = {https://mlanthology.org/iccv/2017/chaurasia2017iccv-editable/}
}