Reproducing oil paint gloss in print for the purpose of creating reproductions of Old Masters

In the field of Fine Art reproduction, 3D scanning plus 3D printing, combined with dedicated software, now allows to capture and reproduce the color and texture of oil paintings. However, for life-like reproduction of the material appearance of such paintings, the typical gloss and translucency must also be included, which is currently not the case. The aim of this paper is to elaborate on the challenges and results of capturing and reproducing oil paint gloss (next to texture and color) using a scanning and printing system. A sample was hand-made using oil paint and acrylic varnish, and its gloss was then reproduced. A gloss map of the painted sample was acquired using a high end DLSR camera and a simple acquisition protocol. Next, Océ High Resolution 3D printing technology was used to create samples with spatially varying gloss. For this, two different strategies were combined: (1) multilevel half-toning of the colors was used to reproduce matte color layers, and (2) varnish was half-toned on top in increasing coverage to recreate increasing gloss levels. This paper presents an overview of the state-of-the-art literature in gloss reproduction and perception, our process of reproduction as well as the visual evaluation of the quality of the created reproduction.

[1]  Hans Brettel,et al.  Printing gloss effects in a 2.5D system , 2014, Electronic Imaging.

[2]  Shoji Tominaga,et al.  Spectral image acquisition, analysis, and rendering for art paintings , 2008, J. Electronic Imaging.

[3]  Donald P. Greenberg,et al.  Psychophysically based model of surface gloss perception , 2001, IS&T/SPIE Electronic Imaging.

[4]  Philip Dutré,et al.  Design of an instrument for measuring the spectral bidirectional scatter distribution function. , 2008, Applied optics.

[5]  Philipp Urban Spectral-based Image Reproduction Workflow - From Capture to Print , 2009 .

[6]  Steve Marschner,et al.  Building volumetric appearance models of fabric using micro CT imaging , 2014, Commun. ACM.

[7]  John M. Snyder,et al.  Manifold bootstrapping for SVBRDF capture , 2010, ACM Trans. Graph..

[8]  Andrew Gardner,et al.  Linear light source reflectometry , 2003, ACM Trans. Graph..

[9]  Bo Sun,et al.  Time-Varying BRDFs , 2006, NPH.

[10]  Jirí Filip,et al.  Bidirectional Texture Function Modeling: A State of the Art Survey , 2009, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[11]  P. Jonker,et al.  Simultaneous capture of the color and topography of paintings using fringe encoded stereo vision , 2014, Heritage Science.

[12]  Hembo Pagi,et al.  Reflectance Transformation Imaging Systems for Ancient Documentary Artefacts , 2011, EVA.

[13]  Michael Pointer,et al.  Measurement of appearance , 2002, Other Conferences.

[14]  Baining Guo,et al.  Pocket reflectometry , 2011, SIGGRAPH 2011.

[15]  Michael R. Pointer,et al.  Toward the soft metrology of surface gloss: A review , 2014 .

[16]  Ying Chen,et al.  Model Evaluation for Computer Graphics Renderings of Artist Paint Surfaces , 2007, Color Imaging Conference.

[17]  Pieter Jonker,et al.  Topographical scanning and reproduction of near-planar surfaces of paintings , 2014, Electronic Imaging.

[18]  John Dakin,et al.  Gloss as an aspect of the measurement of appearance. , 2006, Journal of the Optical Society of America. A, Optics, image science, and vision.

[19]  Jitendra Malik,et al.  Recovering high dynamic range radiance maps from photographs , 1997, SIGGRAPH.

[20]  Wojciech Matusik,et al.  A data-driven reflectance model , 2003, ACM Trans. Graph..

[21]  J. Redman The Simultaneous Capture of Spectral and Textural Information , 2007 .

[22]  Carlos Ureña,et al.  An Overview of BRDF Models , 2012 .

[23]  Gary W. Meyer,et al.  A BRDF Database Employing the Beard-Maxwell Reflection Model , 2002, Graphics Interface.

[24]  Frédéric Leloup New Methods and Models Improving the Prediction of Visual Gloss Perception (Nieuwe methodes en modellen ter bevordering van de voorspelling van visuele glanswaarneming) , 2012 .

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

[26]  G. Obein,et al.  Difference scaling of gloss: nonlinearity, binocularity, and constancy. , 2004, Journal of vision.

[27]  Ewald Snel,et al.  Acquisition, Fitting and Rendering of Paintings , 2007 .

[28]  Hans Brettel,et al.  Towards gloss control in fine art reproduction , 2015, Electronic Imaging.

[29]  Fred W. Billmeyer,et al.  Psychometric Scaling of Gloss , 1986 .

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

[31]  F. Billmeyer,et al.  Visual gloss scaling and multidimensional scaling analysis of painted specimens , 1987 .

[32]  Norimichi Tsumura,et al.  Photometric approach to surface reconstruction of artist paintings , 2011, J. Electronic Imaging.

[33]  Yue Dong,et al.  Bi-scale appearance fabrication , 2013, ACM Trans. Graph..

[34]  James A. Ferwerda,et al.  The tangiBook: A Tangible Display System for Direct Interaction with Virtual Surfaces , 2009, CIC.

[35]  Mikael Lindstrand,et al.  Instrumental Gloss Characterization – In the Light of Visual Evaluation: A Review , 2005, Journal of Imaging Science and Technology.

[36]  David J. Kriegman,et al.  Toward a perceptual space for gloss , 2009, TOGS.

[37]  Wojciech Matusik,et al.  Printing spatially-varying reflectance , 2009, SIGGRAPH 2009.