A Photometric Sampling Strategy for Reflectance Characterization and Transference

Authors

  • Mario Castelán CINVESTAV, Unidad Saltillo

DOI:

https://doi.org/10.13053/cys-19-2-1944

Keywords:

Reflectance transference, singular value decomposition, random Markov fields.

Abstract

Rendering 3D models with real world reflectance properties is an open research problem with significant applications in the field of computer graphics and image understanding. In this paper, our interest lies on the characterization and transference of appearance from a source object onto a target 3D shape. To this end, a three-step strategy is proposed. In the first step, reflectance is sampled by rotating a light source in concentric circles around the source object. Singular value decomposition is then used for describing, in a pixel-wise manner, appearance features such as color, texture and specular regions. The second step introduces a Markov random field transference method based on surface normal correspondence between the source object and a synthetic sphere. The aim of this step is to generate a sphere whose appearance emulates that of the source material. In the third step, final transference of properties is performed from the surface normals of the generated sphere to the surface normals of the target 3D model. Experimental evaluation validates the suitability of the proposed strategy for transferring appearance from a variety of materials between diverse shapes.

Author Biography

Mario Castelán, CINVESTAV, Unidad Saltillo

Mario Castelán obtained his BSc from the University of Veracruz (UV) in 1999 and his MSc in Artificial Intelligence from the University of Veracruz and the National Laboratory of Advanced Informatics (LANIA) in 2002. He obtained his D. Phil. in Computer Science from the University of York, U.K., in 2006. Currently, he is a full- time researcher at the Robotics and Advanced Manufacturing Research Group of CINVESTAV-Saltillo. His research interests are focused on 3D shape analysis and statistical learning for computer vision and robotics applications.

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Published

2015-06-01

Issue

Vol. 19 No. 2 (2015): 19(2) April-June 2015

Section

Articles

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