Inverse shade trees for non-parametric material representation and editing

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

doi:10.1145/1141911.1141949

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 multi-gigabyte measured datasets of the Spatially-Varying Bidirectional Reflectance Distribution Function (SVBRDF) into a compact representation that may be edited in real time.

Highlights

The use of measured surface reflectance has the potential to bring new levels of photorealism to renderings of complex materials
The shade tree framework could in principle represent many types of appearance data, including Bidirectional Texture Functions (BTFs), BSSRDFs, light fields, and time-varying textures, this paper focuses on Spatially-Varying Bidirectional Reflectance Distribution Function (SVBRDF)
The supplementary video shows further real-time editing results. While most of these edits are straightforward given our intuitive shade tree representation, they are to our knowledge the first demonstration of non-parametric editing of spatially-varying measured materials, and would not be easy with alternative matrix factorization methods, which do not provide a meaningful separation of materials or individual BRDFs

Summary

Introduction

The use of measured surface reflectance has the potential to bring new levels of photorealism to renderings of complex materials Such datasets are becoming common, with recent work on acquiring dense measurements of both individual materials [Marschner et al 1999; Matusik et al 2003] and spatially-dependent reflectance [Dana et al 1999; McAllister 2002; Lensch et al 2003; Han and Perlin 2003; Marschner et al 2005]. The availability of such data, has highlighted the difficulty of representing complex materials accurately using conventional analytic reflectance models [Ngan et al 2005]. This paper proposes a compact tree-based representation (Figure 1) that provides the intuitive editability of parametric models while retaining the accuracy and flexibility of general linear decomposition methods

Methods

Results

Conclusion