Material appearance acquisition from a single image

The scope of this paper is to present a method of material appearance acquisition(MAA) from a single image. In this paper, material appearance is represented by spatially varying bidirectional reflectance distribution function(SVBRDF). Therefore, MAA can be reduced to the problem of recovery of each pixel’s BRDF parameters from an original input image, which include diffuse coefficient, specular coefficient, normal and glossiness based on the Blinn-Phone model. In our method, the workflow of MAA includes five main phases: highlight removal, estimation of intrinsic images, shape from shading(SFS), initialization of glossiness and refining SVBRDF parameters based on IPOPT. The results indicate that the proposed technique can effectively extract the material appearance from a single image.

[1]  Holly E. Rushmeier,et al.  A Sparse Parametric Mixture Model for BTF Compression, Editing and Rendering , 2011, Comput. Graph. Forum.

[2]  Jaakko Lehtinen,et al.  Two-shot SVBRDF capture for stationary materials , 2015, ACM Trans. Graph..

[3]  Hui-Liang Shen,et al.  Real-time highlight removal using intensity ratio. , 2013, Applied optics.

[4]  Jaakko Lehtinen,et al.  Practical SVBRDF capture in the frequency domain , 2013, ACM Trans. Graph..

[5]  James F. Blinn,et al.  Models of light reflection for computer synthesized pictures , 1977, SIGGRAPH.

[6]  Jirí Filip,et al.  Digital Material Appearance: The Curse of Tera-Bytes , 2012, ERCIM News.

[7]  Jirí Filip,et al.  Rapid Material Appearance Acquisition Using Consumer Hardware , 2014, Sensors.

[8]  Stephen Lin,et al.  Highlight removal by illumination-constrained inpainting , 2003, Proceedings Ninth IEEE International Conference on Computer Vision.

[9]  Alexei A. Efros,et al.  Fast bilateral filtering for the display of high-dynamic-range images , 2002 .

[10]  Sei-Wang Chen,et al.  Physics-based extraction of intrinsic images from a single image , 2004, ICPR 2004.

[11]  Baining Guo,et al.  Material Appearance Modeling: A Data-Coherent Approach , 2013, Springer Berlin Heidelberg.

[12]  Stephen Lin,et al.  A Closed-Form Solution to Retinex with Nonlocal Texture Constraints , 2012, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[13]  Mubarak Shah,et al.  Shape from shading using linear approximation , 1994, Image Vis. Comput..

[14]  Ping-Sing Tsai,et al.  Shape from Shading: A Survey , 1999, IEEE Trans. Pattern Anal. Mach. Intell..

[15]  Baining Guo,et al.  AppGen: interactive material modeling from a single image , 2011, ACM Trans. Graph..

[16]  Jitendra Malik,et al.  Shape, Illumination, and Reflectance from Shading , 2015, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[17]  Tim Weyrich,et al.  Principles of Appearance Acquisition and Representation , 2009, Found. Trends Comput. Graph. Vis..

[18]  Anat Levin,et al.  User Assisted Separation of Reflections from a Single Image Using a Sparsity Prior , 2004, ECCV.

[19]  Honggang Zhang,et al.  Chromaticity-based separation of reflection components in a single image , 2008, Pattern Recognit..

[20]  Christopher Schwartz,et al.  Design and Implementation of Practical Bidirectional Texture Function Measurement Devices Focusing on the Developments at the University of Bonn , 2014, Sensors.

[21]  Edward H. Adelson,et al.  Recovering intrinsic images from a single image , 2005, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[22]  Narendra Ahuja,et al.  Real-Time Specular Highlight Removal Using Bilateral Filtering , 2010, ECCV.

[23]  Tan Ping.  Illumination-Constrained Inpainting for Single Image Highlight Removal , 2004 .

[24]  Jean-Denis Durou,et al.  Numerical methods for shape-from-shading: A new survey with benchmarks , 2008, Comput. Vis. Image Underst..

[25]  Xuelong Li,et al.  Intrinsic images using optimization , 2011, CVPR 2011.