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 state-of-the-art report (STAR) will present the techniques for the main tasks involved in producing photo-realistic renderings using measured BTFs in details.

No abstract

One of the most important, still unsolved problems in computer graphics is the generation of predictive imagery, i.e., images that represent perfect renditions of reality. Such perfect images are required in application areas like Virtual Prototyping for making reliable decisions in the costly design development of novel products like cars and airplanes. Recently, measured material properties received significant attention since they enable generation of highly accurate images that appear to be predictive at a first glance.In this work we investigate the degree of realism that can be achieved using measured bidirectional texture functions (BTFs) by comparing photographs and rendered images at two scales. To analyze the realism of rendered images at a coarse scale, we compare the light distribution resulting from standard materials to the one from measured BTFs by automatic procedures. At a fine scale, accurate reproduction of material structures is checked by a psychophysical study. Our results show that measured BTFs lead to much more accurate results than standard materials at both scales.

Abstract

Recently, the special kind of near-regular texture has drawn significant attention from researchers in the field of texture synthesis. Near-regular textures contain global regular structures that pose significant problems to the popular sample-based approaches, and irregular stochastic structures that can not be reproduced by simple tiling. Existing work tries to overcome this problem by user assisted modeling of the regular structures and then relies on regular tiling. In this paper we use the concept of fractional Fourier analysis to perform a fully automatic separation of the global regular structure from the irregular structure. The actual synthesis is performed by generating a fractional Fourier texture mask from the extracted global regular structure which is used to guide the synthesis of irregular texture details. Our new method allows for automatic and efficient synthesis of a wide range of near-regular textures preserving their regular structures and faithfully reproducing their stochastic elements.