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

Fabrizio Natola, Valsamis Ntouskos, Fiora Pirri, Marta Sanzari; Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2016, pp. 4414-4423

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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[bibtex]
@InProceedings{Natola_2016_CVPR,
author = {Natola, Fabrizio and Ntouskos, Valsamis and Pirri, Fiora and Sanzari, Marta},
title = {Single Image Object Modeling Based on BRDF and R-Surfaces Learning},
booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},
month = {June},
year = {2016}
}