In this work, we investigate the visual appearance of real-world surfaces and the dependence of appearance on scale, viewing direction and illumination direction. At ne scale, surface variations cause local intensity variation or image texture. The appearance of this texture depends on both illumination and viewing direction and can be characterized by the BTF (bidirectional texture function). At su ciently coarse scale, local image texture is not resolvable and local image intensity is uniform. The dependence of this image intensity on illumination and viewing direction is described by the BRDF (bidirectional re ectance distribution function). We simultaneously measure the BTF and BRDF of over 60 di erent rough surfaces, each observed with over 200 di erent combinations of viewing and illumination direction. The resulting BTF database is comprised of over 12,000 image textures. To enable convenient use of the BRDF measurements, we t the measurements to two recent models and obtain a BRDF parameter database. These parameters can be used directly in image analysis and synthesis of a wide variety of surfaces. The BTF, BRDF, and BRDF parameter databases have important implications for computer vision and computer graphics and and each is made publicly available.

This paper describes a method for synthesizing images that match the texture appearanceof a given digitized sample. This synthesis is completely automatic and requires only the "target" texture as input. It allows generation of as much texture as desired so that any object can be covered. It can be used to produce solid textures for creating textured 3-d objects without the distortions inherent in texture mapping. It can also be used to synthesize texture mixtures, images that look a bit like each of several digitized samples. The approach is based on a model of human texture perception, and has potential to be a practically useful tool for graphics applications. 1 Introduction Computer renderings of objects with surface texture are more interesting and realistic than those without texture. Texture mapping [15] is a technique for adding the appearance of surface detail by wrapping or projecting a digitized texture image ontoa surface. Digitized textures can be obtained from a variety ...

In this paper, we propose a new approach to numerical smoke simulation for computer graphics applications. The method proposed here exploits physics unique to smoke in order to design a numerical method that is both fast and efficient on the relatively coarse grids traditionally used in computer graphics applications (as compared to the much finer grids used in the computational fluid dynamics literature). We use the inviscid Euler equations in our model, since they are usually more appropriate for gas modeling and less computationally intensive than the viscous NavierStokes equations used by others. In addition, we introduce a physically consistent vorticity confinement term to model the small scale rolling features characteristic of smoke that are absent on most coarse grid simulations. Our model also correctly handles the interaction of smoke with moving objects. Keywords: Smoke, computational fluid dynamics, Navier-Stokes equations, Euler equations, Semi-Lagrangian methods, stable fluids, vorticity confinement, participating media 1

This tutorial survey paper reviews several different models for light interaction with volume densities of absorbing, glowing, reflecting, and/or scattering material. They are, in order of increasing realism, absorption only, emission only, emission and absorption combined, single scattering of external illumination without shadows, single scattering with shadows, and multiple scattering. For each model I give the physical assumptions, describe the applications for which it is appropriate, derive the differential or integral equations for light transport, present calculations methods for solving them, and show output images for a data set representing a cloud. Special attention is given to calculation methods for the multiple scattering model.

This paper describes a biologically motivated method of texture synthesis called reaction-diffusion and demonstrates how these textures can be generated in a manner that directly matches the geometry of a given surface. Reaction-diffusion is a process in which two or more chemicals diffuse at unequal rates over a surface and react with one another to form stable patterns such as spots and stripes. Biologists and mathematicians have explored the patterns made by several reaction-diffusion systems. We extend the range of textures that have previously been generated by using a cascade of multiple reaction-diffusion systems in which one system lays down an initial pattern and then one or more later systems refine the pattern. Examples of patterns generated by such a cascade process include the clusters of spots on leopards known as rosettes and the web-like patterns found on giraffes. In addition, this paper introduces a method by which reaction-diffusion textures are created to match the geometry of an arbitrary polyhedral surface. This is accomplished by creating a mesh over a given surface and then simulating the reactiondiffusion process directly on this mesh. This avoids the often difficult task of assigning texture coordinates to a complex surface. A mesh is generated by evenly distributing points over the model using relaxation and then determining which points are adjacent by constructing their Voronoi regions. Textures are rendered directly from the mesh by using a weighted sum of mesh values to compute surface color at a given position. Such textures can also be used as bump maps.

This paper describes how evolutionary techniques of variation and selection can be used to create complex simulated structures, textures, and motions for use in computer graphics and animation. Interactive selection, based on visual perception of procedurally generated results, allows the user to direct simulated evolutions in preferred directions. Several examples using these methods have been implemented and are described. 3D plant structures are grown using fixed sets of genetic parameters. Images, solid textures, and animations are created using mutating symbolic lisp expressions. Genotjps consisting of symbolic expressions are presented as an attempt to surpass the limitations of fixed-length genotypes with predefine expression rules. his proposed that artificial evolution has potential as a powerful tool for achieving flexible complexity with a minimum of user input and knowledge of details. 2

We present a simple texture synthesis algorithm that is well-suited for a specific class of naturally occurring textures. This class includes quasi-repeating patterns consisting of small objects of familiar but irregular size, such as flower fields, pebbles, forest undergrowth, bushes and tree branches. The algorithm starts from a sample image and generates a new image of arbitrary size the appearance of which is similar to that of the original image. This new image does not change the basic spatial frequencies the original image; instead it creates an image that is a visually similar, and is of a size set by the user. This method is fast and its implementation is straightforward. We extend the algorithm to allow direct user input for interactive control over the texture synthesis process. This allows the user to indicate large-scale properties of the texture appearance using a standard painting-style interface, and to choose among various candidate textures the algorithm can create by performing different number of iterations.

We propose a new rendering technique that produces 3-D images with enhanced visual comprehensibility. Shape fea-tures can be readily understood if certain geometric proper-ties are enhanced. To achieve this, we develop drawing algo-rithms for discontinuities, edges, contour lines, and curved hatching. All of them are realized with 2-D image process-ing operations instead of line tracking processes, so that they can be efficiently combined with conventional surface rendering algorithms. Data about the geometric properties of the surfaces are preserved as Geometric Buffers (G-buffers). Each G-buffer contains one geometric property such as the depth or the normal vector of each pixel. By using G-buffers as interme-diate results, artificial enhancement processes are separated from geometric processes (projection and hidden surface re-moval) and physical processes (shading and texture map-ping), and performed as postprocesses. This permits a user to rapidly examine various combinations of enhancement techniques without excessive recompntation, and easily ob-tain the most comprehensible image. Our method can be widely applied for various purposes. Several of these, edge enhancement, line drawing illustra-tions, topographical maps, medical imaging, and surface analysis, are presented in this paper.

We present a method for rendering scenes with fine detail via an object called a texel, a rendering primitive inspired by volume den-sities mixed with anisotropic lighting models. This technique solves a long outstanding problem in image synthesis: the rendering of furry surfaces.

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