Single Image Object Modeling Based on BRDF and R-Surfaces Learning

Fabrizio Natola, Valsamis Ntouskos, Fiora Pirri, Marta Sanzari

CVPR 2016

doi:10.1109/CVPR.2016.478 /cvpr/2016/natola2016cvpr-single/

Abstract

A methodology for 3D surface modeling from a single image is proposed. The principal novelty is concave and specular surface modeling without any externally imposed prior. The main idea of the method is to use BRDFs and generated rendered surfaces, to transfer the normal field, computed for the generated samples, to the unknown surface. The transferred information is adequate to blow and sculpt the segmented image mask in to a bas-relief of the object. The object surface is further refined basing on a photo-consistency formulation that relates for error minimization the original image and the modeled object.

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Cite

Text

Natola et al. "Single Image Object Modeling Based on BRDF and R-Surfaces Learning." Conference on Computer Vision and Pattern Recognition, 2016. doi:10.1109/CVPR.2016.478

Markdown

[Natola et al. "Single Image Object Modeling Based on BRDF and R-Surfaces Learning." Conference on Computer Vision and Pattern Recognition, 2016.](https://mlanthology.org/cvpr/2016/natola2016cvpr-single/) doi:10.1109/CVPR.2016.478

BibTeX

@inproceedings{natola2016cvpr-single,
  title     = {{Single Image Object Modeling Based on BRDF and R-Surfaces Learning}},
  author    = {Natola, Fabrizio and Ntouskos, Valsamis and Pirri, Fiora and Sanzari, Marta},
  booktitle = {Conference on Computer Vision and Pattern Recognition},
  year      = {2016},
  doi       = {10.1109/CVPR.2016.478},
  url       = {https://mlanthology.org/cvpr/2016/natola2016cvpr-single/}
}