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The bidirectional texture function (BTF) is a 6D function that can describe textures arising from both spatially-variant surface reflectance and surface mesostructures. In this paper, we present an algorithm for synthesizing the BTF on an arbitrary surface from a sample BTF. A main challenge in surface BTF synthesis is the requirement of a consistent mesostructure on the surface, and to achieve that we must handle the large amount of data in a BTF sample. Our algorithm performs BTF synthesis based on surface textons, which extract essential information from the sample BTF to facilitate the synthesis. We also describe a general search strategy, called the �-coherent search, for fast BTF synthesis using surface textons. A BTF synthesized using our algorithm not only looks similar to the BTF sample in all viewing/lighthing conditions but also exhibits a consistent mesostructure when viewing and lighting directions change. Moreover, the synthesized BTF fits the target surface naturally and seamlessly. We demonstrate the effectiveness of our algorithm with sample BTFs from various sources, including those measured from real-world textures.

This paper introduces a tensor framework for image-based rendering. In particular, we develop an algorithm called TensorTextures that learns a parsimonious model of the bidirectional texture function (BTF) from observational data. Given an ensemble of images of a textured surface, our nonlinear, generative model explicitly represents the multifactor interaction implicit in the detailed appearance of the surface under varying photometric angles, including local (per-texel) reflectance, complex mesostructural self-occlusion, interreflection and self-shadowing, and other BTF-relevant phenomena. Mathematically, TensorTextures is based on multilinear algebra, the algebra of higher-order tensors, hence its name. It is computed through a decomposition known as the N-mode SVD, an extension to tensors of the conventional matrix singular value decomposition (SVD). We demonstrate the application of TensorTextures to the image-based rendering of natural and synthetic textured surfaces under continuously varying viewpoint and illumination conditions.

One of the main challenges in computer graphics is still the realistic rendering of complex materials such as fabric or skin. The difficulty arises from the complex meso structure and reflectance behavior defining the unique look-and-feel of a material. A wide class of such realistic materials can be described as 2D-texture under varying light- and view direction namely the Bidirectional Texture Function (BTF). Since an easy and general method for modeling BTFs is not available, current research concentrates on image-based methods which rely on measured BTFs (acquired real-world data) in combination with appropriate synthesis methods. Recent results have shown that this approach greatly improves the visual quality of rendered surfaces and therefore the quality of applications such as virtual prototyping. This STAR will present in detail the state-of-the-art techniques for the main tasks involved in producing photo-realistic renderings using measured BTFs

We describe a simple and robust method for surface mesostructure acquisition. Our method builds on the observation that specular reflection is a reliable visual cue for surface mesostructure perception. In contrast to most photometric stereo methods, which take specularities as outliers and discard them, we propose a progressive acquisition system that captures a dense specularity field as the only information for mesostructure reconstruction. Our method can efficiently recover surfaces with fine-scale geometric details from complex real-world objects with a wide variety of reflection properties, including translucent, low albedo, and highly specular objects. We show results for a variety of objects including human skin, dried apricot, orange, jelly candy, black leather and dark chocolate.

Radiance transfer represents how generic source lighting is shadowed and scattered by an object to produce view-dependent appearance. We generalize by rendering transfer at two scales. A macro-scale is coarsely sampled over an object’s surface, providing global effects like shadows cast from an arm onto a body. A meso-scale is finely sampled over a small patch to provide local texture. Low-order (25D) spherical harmonics represent lowfrequency lighting dependence for both scales. To render, a coefficient vector representing distant source lighting is first transformed at the macro-scale by a matrix at each vertex of a coarse mesh. The resulting vectors represent a spatially-varying hemisphere of lighting incident to the meso-scale. A 4D function, called a radiance transfer texture (RTT), then specifies the surface’s meso-scale response to each lighting basis component, as a function of a spatial index and a view direction. Finally, a 25D dot product of the macro-scale result vector with the vector looked up from the RTT performs the correct shading integral. We use an id map to place RTT samples from a small patch over the entire object; only two scalars are specified at high spatial resolution. Results show that bi-scale decomposition makes preprocessing practical and efficiently renders self-shadowing and interreflection effects from dynamic, low-frequency light sources at both scales.

Abstract — Real world surfaces such as tree bark, moss, sponge, and fur often have complicated geometry that leads to effects such as self-shadowing, masking, specularity, and interreflection as the lighting or viewpoint in a scene changes. We use image based techniques to analyze and represent bidirectional texture functions, or BTFs, with correct geometric and lighting effects. A basis for the apparent BRDF of points on the surface is determined and used to compress the texture datasets, as well as provide a space for comparison of texture elements across all lights and views. The compression method reduces the approximately 10,000 images in each 6-D lighting, viewpoint, and spatial variation texture dataset to under 2 MB. I.

Abstract—The bidirectional texture function (BTF) is a 6D function that describes the appearance of a real-world surface as a function of lighting and viewing directions. The BTF can model the fine-scale shadows, occlusions, and specularities caused by surface mesostructures. In this paper, we present algorithms for efficient synthesis of BTFs on arbitrary surfaces and for hardware-accelerated rendering. For both synthesis and rendering, a main challenge is handling the large amount of data in a BTF sample. To addresses this challenge, we approximate the BTF sample by a small number of 4D point appearance functions (PAFs) multiplied by 2D geometry maps. The geometry maps and PAFs lead to efficient synthesis and fast rendering of BTFs on arbitrary surfaces. For synthesis, a surface BTF can be generated by applying a texton-based sysnthesis algorithm to a small set of 2D geometry maps while leaving the companion 4D PAFs untouched. As for rendering, a surface BTF synthesized using geometry maps is well-suited for leveraging the programmable vertex and pixel shaders on the graphics hardware. We present a real-time BTF rendering algorithm that runs at the speed of about 30 frames/second on a mid-level PC with an ATI Radeon 8500 graphics card. We demonstrate the effectiveness of our synthesis and rendering algorithms using both real and synthetic BTF samples. Index Terms—Bidirectional texture function, reflectance and shading models, texture synthesis, mesh parameterization, texture mapping, surfaces. 1

This paper presents and compares six novel approaches for capturing, synthesising and relighting real 3D surface textures. Unlike 2D texture synthesis these techniques allow the captured textures to be relit using illumination conditions, and viewing angles, that differ from those of original. Our approaches each comprise two stages: synthesis and relighting. Synthesis can be applied either before or after relighting. The relighting stage is implemented in three different ways: using image-based, gradientbased, and height-based approaches. Thus there are a total of six different ways in which we may combine these functions. We present a representative set of results selected from our experiments with 30 textures. The best images are obtained when image-based or gradient-based relighting is used after synthesis.

Tensor approximation is necessary to obtain compact multilinear models for multi-dimensional visual datasets. Traditionally, each multi-dimensional data item is represented as a vector. Such a scheme flattens the data and partially destroys the internal structures established throughout the multiple dimensions. In this paper, we retain the original dimensionality of the data items to more effectively exploit existing spatial redundancy and allow more efficient computation. Since the size of visual datasets can easily exceed the memory capacity of a single machine, we also present an outof -core algorithm for higher-order tensor approximation. The basic idea is to partition a tensor into smaller blocks and perform tensorrelated operations blockwise. We have successfully applied our techniques to three graphics-related data-driven models, including 6D bidirectional texture functions, 7D dynamic BTFs and 4D volume simulation sequences. Experimental results indicate that our techniques can not only process out-of-core data, but also achieve higher compression ratios and quality than previous methods.