AppWand: editing measured materials using appearance-driven optimization

We investigate a new approach to editing spatially- and temporally-varying measured materials that adopts a stroke-based workflow. In our system, a user specifies a small number of editing constraints with a 3-D painting interface which are smoothly propagated to the entire dataset through an optimization that enforces similar edits are applied to areas with similar appearance. The sparse nature of this appearance-driven optimization permits the use of efficient solvers, allowing the designer to interactively refine the constraints. We have found this approach supports specifying a wide range of complex edits that would not be easy with existing techniques which present the user with a fixed segmentation of the data. Furthermore, it is independent of the underlying reflectance model and we show edits to both analytic and non-parametric representations in examples from several material databases.

[1]  P. Groenen,et al.  Modern Multidimensional Scaling: Theory and Applications , 1999 .

[2]  S. Marschner,et al.  Measuring and modeling the appearance of finished wood , 2005, SIGGRAPH 2005.

[3]  Frédo Durand,et al.  Image-driven navigation of analytical BRDF models , 2006, EGSR '06.

[4]  Gary W. Meyer,et al.  Applying appearance standards to light reflection models , 2001, SIGGRAPH.

[5]  Sumanta N. Pattanaik,et al.  BRDF-Shop: creating physically correct bidirectional reflectance distribution functions , 2006, IEEE Computer Graphics and Applications.

[6]  J. Tenenbaum,et al.  A global geometric framework for nonlinear dimensionality reduction. , 2000, Science.

[7]  Matthew Brand,et al.  Charting a Manifold , 2002, NIPS.

[8]  Wojciech Matusik,et al.  Inverse shade trees for non-parametric material representation and editing , 2006, SIGGRAPH 2006.

[9]  Yousef Saad,et al.  Iterative methods for sparse linear systems , 2003 .

[10]  Dani Lischinski,et al.  Colorization by example , 2005, EGSR '05.

[11]  Dani Lischinski,et al.  Colorization using optimization , 2004, SIGGRAPH 2004.

[12]  Shree K. Nayar,et al.  Time-varying surface appearance , 2006, SIGGRAPH 2006.

[13]  Karl vom Berge,et al.  A compact factored representation of heterogeneous subsurface scattering , 2006, SIGGRAPH 2006.

[14]  S T Roweis,et al.  Nonlinear dimensionality reduction by locally linear embedding. , 2000, Science.

[15]  Christian P. Robert,et al.  Monte Carlo Statistical Methods , 2005, Springer Texts in Statistics.

[16]  Bui Tuong Phong.  Illumination for computer generated pictures , 1975, Commun. ACM.

[17]  Hans-Peter Seidel,et al.  Image-based reconstruction of spatial appearance and geometric detail , 2003, TOGS.

[18]  Bui Tuong Phong.  Illuminat~on for computer generated images , 1973 .

[19]  Gregory J. Ward,et al.  Measuring and modeling anisotropic reflection , 1992, SIGGRAPH.

[20]  F. E. Nicodemus,et al.  Geometrical considerations and nomenclature for reflectance , 1977 .

[21]  Robert L Cook,et al.  A reflectance model for computer graphics , 1981, SIGGRAPH '81.

[22]  Donald P. Greenberg,et al.  Toward a psychophysically-based light reflection model for image synthesis , 2000, SIGGRAPH.

[23]  Zeev Farbman,et al.  Interactive local adjustment of tonal values , 2006, SIGGRAPH 2006.

[24]  H. Shum,et al.  Appearance manifolds for modeling time-variant appearance of materials , 2006, SIGGRAPH 2006.

[25]  Donald P. Greenberg,et al.  Non-linear approximation of reflectance functions , 1997, SIGGRAPH.

[26]  Hugues Hoppe,et al.  Appearance-space texture synthesis , 2006, SIGGRAPH 2006.

[27]  Steven M. Seitz,et al.  Shape and Spatially-Varying BRDFs from Photometric Stereo , 2005, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[28]  Christian P. Robert,et al.  Monte Carlo Statistical Methods (Springer Texts in Statistics) , 2005 .

[29]  Anil K. Jain,et al.  Data clustering: a review , 1999, CSUR.

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