Inverse Shade Trees for Non-Parametric Material Representation and Editing
SIGGRAPH 2006

Jason Lawrence, Aner Ben-Artzi, Christopher DeCoro,
Wojciech Matusik, Hanspeter Pfister, Ravi Ramamoorthi,
Szymon Rusinkiewicz

Abstract

Recent progress in the measurement of surface reflectance has created a demand for non-parametric appearance representations that are accurate, compact, and easy to use for rendering. Another crucial goal, which has so far received little attention, is editability: for practical use, we must be able to change both the directional and spatial behavior of surface reflectance (e.g., making one material shinier, another more anisotropic, and changing the spatial "texture maps" indicating where each material appears). We introduce an Inverse Shade Tree framework that provides a general approach to estimating the "leaves" of a user-specified shade tree from high-dimensional measured datasets of appearance. These leaves are sampled 1- and 2-dimensional functions that capture both the directional behavior of individual materials and their spatial mixing patterns. In order to compute these shade trees automatically, we map the problem to matrix factorization and introduce a flexible new algorithm that allows for constraints such as non-negativity, sparsity, and energy conservation. Although we cannot infer every type of shade tree, we demonstrate the ability to reduce multigigabyte measured datasets of the Spatially-Varying Bidirectional Reflectance Distribution Function (SVBRDF) into a compact representation that may be edited in real time.

Paper
  High-resolution (22MB): PDF file
  Low-resolution (1 MB): PDF file

Video
  5-minute MPEG4 video (21MB): AVI file

Links
  Project page (contains data and source code)
  IST publication website at Princeton (may be more up to date)