Title
Single Image Object Modeling Based On Brdf And R-Surfaces Learning
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.
Year
DOI
Venue
2016
10.1109/CVPR.2016.478
2016 IEEE CONFERENCE ON COMPUTER VISION AND PATTERN RECOGNITION (CVPR)
Field
DocType
Volume
Bidirectional reflectance distribution function,Computer vision,Computer science,Specular reflection,Object model,Minification,Artificial intelligence,Novelty
Conference
2016
Issue
ISSN
Citations 
1
1063-6919
0
PageRank 
References 
Authors
0.34
19
4
Name
Order
Citations
PageRank
Fabrizio Natola100.34
Valsamis Ntouskos2125.42
Fiora Pirri368494.09
Marta Sanzari420.70