DOCSLIB.ORG

A Fast Algorithm for Texture Synthesis Based on Perlin Noise

Total Page：16

File Type：pdf, Size：1020Kb

Download full-text PDF Read full-text
A Fast Algorithm for Texture Synthesis Based on Perlin Noise I.J. Image, Graphics and Signal Processing, 2011, 3, 33-40 Published Online April 2011 in MECS (http://www.mecs-press.org/) Wavelet and Blend maps for texture synthesis Du Jin-Lian Wang Song Meng Xianhai Beijing University of Technology Beijing China [email protected] Abstract─ blending is now a popular technology for large method. realtime texture synthesis .Nevertheless, creating blend map during rendering is time and computation consuming work. II. RELATED WORK In this paper, we exploited a method to create a kind of blend tile which can be tile together seamlessly. Note that In recent years, a great deal of works on texture blend map is in fact a kind of image, which is Markov synthesis based on texture tiles and blending technology Random Field, contains multiresolution signals, while has been proposed. Here we simply review some wavelet is a powerful way to process multiresolution signals, we use wavelet to process the traditional blend tile. After up-to-date and typical approaches. our processing steps, the result blend tile become smooth Texture splatting[3] is the typical technology which and suitable for tiling, with no important features lost. use linear blending to smooth the transition between Using this kind blend tile, many computation resources for textures tiles and it was used widely in game terrain computing blend map during texture synthesizing is saved. rendering. The limit of this method is that it needs large The experimental results shows that our method may alpha maps for blending and linear blending often successfully process many traditional blend tiles. produced undesirable artifacts. To improve the quality of synthesis texture, many Index Terms─ texture synthesis; wavelet; Multiresolution researchers have contributed their wisdom and effort. analysis; blend map [Malik et al.1999][4] proposed conception of texton channel to specify the features of a texture map and the texton map was applied successfully to control I. INTRODUCTION procedural texture synthesis by [Zhang et al.2003;Wu Textures are extensively used to increase the realism and Yu 2004][5,6]. The work of Lai et al.[7] introduced a of rendered objects. It is especially important for the novel method to blend textures. Their algorithm present realtime rendering of terrain. In this case, the use of large identifies the features of an image and provides a textures often results low performance during rendering. visually pleasing border between tiled textures on the As alternatives, a number of techniques including terrain. The technology is limited by the number of [Ashikhmin's 2001, Wei and Levoy 2000][1,2], have been textures that are precomputed, thus the visual richness of developed for procedural texture synthesis. Given a terrain may be reduced. The textures that blend between sample texture, these techniques can create a similar regions also require extra texture memory, over and texture that fits the target surface naturally and above the tiles already in use. Alexandre hardy[8] seamlessly. To increase the visual richness of the introduced blend maps to increase the control and synthesis texture, blend maps and blending algorithms observed richness of tiled textures. The technology are used during synthesizing. However, create large construct blend map by identifying important features in blending tiles will consume computation resources and a texture for these features are often highlighted under also consume extra texture memory which is very certain lighting conditions. The resulting texture precious now. This paper introduces a method to produce produced by the technology appears more realistic in small blend tile which can be tiled together with no many cases. However, there are too many coefficients seams along the boundaries of two tiles. So the blend tile need to be define in this method so it is not easy for user. could be used repeatedly to produce large texture with no In our opinion, if the blend map can be tiled need to blend the sharp boundaries. The blend tile is seamlessly, the computing work for blending textures produced by wavelet analysis, so the processing steps will be reduced largely, because there is no need to and mathematics of our approach are discussed firstly. synthesizing blend map or feature map. So our work is Advantages of using small blend tiles and its impact on try to produce blend map which can tile together —blend performance are illustrated in the paper. tile with Wavelet analysis. Related works about texturing terrain or other Our approach is based on the observation that many surfaces using texture tiles and blending technology are blend maps, which are Markov Random Field, contains discussed in next section, followed by discussion of our multiresolution signals, that means the map is refinable. While wavelet analysis is a powerful way to analyze and Supported by Scientific Research Common Program of Beijing Municipal construct functions[9,10,11]. It is therefore natural to use Commission of Education under Grant Nos. KM200710005002。 wavelet analysis to process the blend map, remove high Copyright © 2011 MECS I.J. Image, Graphics and Signal Processing, 2011, 3, 33-40 34 Wavelet and Blend maps for texture synthesis frequencies from the map, smooth the remainder. Then F()() x=∑ fiφ x − i (1) the result noise is suitable for tiling and will not cause i aliasing and discontinuities. In the following section we present the overview for Where φ()x is the basis function and φ()x− i is a processing steps of our approach and the mathematics for translated version of φ()x by integer amount i. where deriving this approach is presented in section 4. Practical implementation and results are discussed in section 5. the fi are the coefficients of the representation. I. OVERVIEW OF OUR ALGORITHM If we call the vector space formed by φ()x and it’s To provide a context for the remainder of the paper translated versions the resolution 0 space and denote by and to simplify the algorithm, we present a high-level S 0 : overview. The essence of our algorithm consists of the following three steps illustrated in Fig.1: 0 ⎧ ⎫ Create the blend map R filled with important features S:⎨ F ( x ) | F ( x )=∑φ ( x − i )⎬ (2) of the textures which suitable for your ⎩ i ⎭ application.(fig.1a) 1 Downsample R to create the half-size image A larger resolution 1 space S of function can be Rd.(fig.1b) represented by scaling down the width of φ()x by Upsample Rd to a full size image Rn(fig.1c). factor of 2. Function G() x in S1 take the following Tile Rn to construct large blend map for your application. form: Rn is created by downsample R to get the low 1 S( x ) : G ( x )= gφ (2 x− i ) (3) frequencies and refine it to a full size. Thus Rn contains ∑ i smoothed low-frequency part of R. i In wavelet analysis idea, all the functions in S0 are also in S1, that means SS0⊂ 1 . This guarantees that downsample S1 enriches the space S 0 . and φ()x can be written in terms of φ(2x− k ) : (b) upsample (a) φ(x )= ∑ hkφ (2 x− k ) (4) k Thus given the coefficients fi that represents 0 1 F() x in S , there exist coefficients f i that represents that function exactly in S1 : 1 F( x )= fi φ (2 x− i ) (5) (c) ∑ i ． Figure1 (a).Blend map R of a texture..(b).Half size image Rd. (c).Full size Using the equation 4 and equation 5, it is image Rn. straightforward to show that: f1 = h f (6) III． THEORY i ∑ i−2 k k k Wavelet analysis, known as multiresolution analysis, The equation 6 represents an upsampling filter, which has emerged a powerful way to extract the different is in fact used in step 3 of our algorithm. frequencies information from original signal and enrich them at the same time. We use this feature of wavelet Although the upsampling filter established above can analysis to process the blend map, and provide the guarantee that every member of S 0 can be represented mathematical underpinnings for our algorithm. First, we 1 exactly as a member of S . The converse is not true: work in one dimension, then extend to 2 dimensions. not every function G(x) in S1 can be represented A. Upsampling and Downsampling exactly in the lower resolution space S 0 , there is some In the multiresolution opinion, much function can be lost detail. But the loss of detail can be minimized in a represented as a weighted sum of basis function, as least squares sense by wavelet analysis. Assume following; G0 () x is the least squares best approximation to Copyright © 2011 MECS I.J. Image, Graphics and Signal Processing, 2011, 3, 33-40 Wavelet and Blend maps for texture synthesis 35 G() x in S 0 , then given the coefficients g for G() x , R= {... r d ...} i Then we get d i, j . 0 the coefficients gi can be determined using standard R R Compute n by upsampling d result from wavelet analysis: Rd 0 By using equation 12, we get the containing low gi = ∑ ak−2 i g k (7) 0 k frequencies signal represented in S . However, R ak is the downsampling filter used in step 2 of our d looks still rough, not smooth enough. Thus we algorithm. R smoothen d by upsam p ling it to achieve the ta rget When extend above equation to two dimensions, the n n R 1 R = {...r ...} r 1 blend image n in S . n i, j ,and i, j can be upsampling filter f i represented in equation 6 and 0 written as following: downsampling filter gi should be written in the n d ri, j = hi−2 kx , j − 2 ky r kx , ky following form respectively: ∑ kx, ky (13) 1 In this step, n o important detail is lost, because all the fi, j = ∑ hi−2 kx , j − 2 ky f kx , ky (8) R kx, ky memb ers i n d is also represented exactly as members 0 Rn gi, j = ∑ akx−2 i , ky − 2 j g kx , ky (9) in .
Load more

Recommended publications
  • Perlin Textures in Real Time Using Opengl Antoine Miné, Fabrice Neyret
    Perlin Textures in Real Time using OpenGL Antoine Miné, Fabrice Neyret To cite this version: Antoine Miné, Fabrice Neyret. Perlin Textures in Real Time using OpenGL. [Research Report] RR- 3713, INRIA. 1999, pp.18. inria-00072955 HAL Id: inria-00072955 https://hal.inria.fr/inria-00072955 Submitted on 24 May 2006 HAL is a multi-disciplinary open access L’archive ouverte pluridisciplinaire HAL, est archive for the deposit and dissemination of sci- destinée au dépôt et à la diffusion de documents entific research documents, whether they are pub- scientifiques de niveau recherche, publiés ou non, lished or not. The documents may come from émanant des établissements d’enseignement et de teaching and research institutions in France or recherche français ou étrangers, des laboratoires abroad, or from public or private research centers. publics ou privés. INSTITUT NATIONAL DE RECHERCHE EN INFORMATIQUE ET EN AUTOMATIQUE Perlin Textures in Real Time using OpenGL Antoine Mine´ Fabrice Neyret iMAGIS-IMAG, bat C BP 53, 38041 Grenoble Cedex 9, FRANCE [email protected] http://www-imagis.imag.fr/Membres/Fabrice.Neyret/ No 3713 juin 1999 THEME` 3 apport de recherche ISSN 0249-6399 Perlin Textures in Real Time using OpenGL Antoine Miné Fabrice Neyret iMAGIS-IMAG, bat C BP 53, 38041 Grenoble Cedex 9, FRANCE [email protected] http://www-imagis.imag.fr/Membres/Fabrice.Neyret/ Thème 3 — Interaction homme-machine, images, données, connaissances Projet iMAGIS Rapport de recherche n˚3713 — juin 1999 — 18 pages Abstract: Perlin’s procedural solid textures provide for high quality rendering of surface appearance like marble, wood or rock.
    [Show full text]
  • Fast, High Quality Noise
    The Importance of Being Noisy: Fast, High Quality Noise Natalya Tatarchuk 3D Application Research Group AMD Graphics Products Group Outline Introduction: procedural techniques and noise Properties of ideal noise primitive Lattice Noise Types Noise Summation Techniques Reducing artifacts General strategies Antialiasing Snow accumulation and terrain generation Conclusion Outline Introduction: procedural techniques and noise Properties of ideal noise primitive Noise in real-time using Direct3D API Lattice Noise Types Noise Summation Techniques Reducing artifacts General strategies Antialiasing Snow accumulation and terrain generation Conclusion The Importance of Being Noisy Almost all procedural generation uses some form of noise If image is food, then noise is salt – adds distinct “flavor” Break the monotony of patterns!! Natural scenes and textures Terrain / Clouds / fire / marble / wood / fluids Noise is often used for not-so-obvious textures to vary the resulting image Even for such structured textures as bricks, we often add noise to make the patterns less distinguishable Ех: ToyShop brick walls and cobblestones Why Do We Care About Procedural Generation? Recent and upcoming games display giant, rich, complex worlds Varied art assets (images and geometry) are difficult and time-consuming to generate Procedural generation allows creation of many such assets with subtle tweaks of parameters Memory-limited systems can benefit greatly from procedural texturing Smaller distribution size Lots of variation
    [Show full text]
  • Synthetic Data Generation for Deep Learning Models
    32. DfX-Symposium 2021 Synthetic Data Generation for Deep Learning Models Christoph Petroll 1 , 2 , Martin Denk 2 , Jens Holtmannspötter 1 ,2, Kristin Paetzold 3 , Philipp Höfer 2 1 The Bundeswehr Research Institute for Materials, Fuels and Lubricants (WIWeB) 2 Universität der Bundeswehr München (UniBwM) 3 Technische Universität Dresden * Korrespondierender Autor: Christoph Petroll Institutsweg 1 85435 Erding Germany Telephone: 08122/9590 3313 Mail: [email protected] Abstract The design freedom and functional integration of additive manufacturing is increasingly being implemented in existing products. One of the biggest challenges are competing optimization goals and functions. This leads to multidisciplinary optimization problems which needs to be solved in parallel. To solve this problem, the authors require a synthetic data set to train a deep learning metamodel. The research presented shows how to create a data set with the right quality and quantity. It is discussed what are the requirements for solving an MDO problem with a metamodel taking into account functional and production-specific boundary conditions. A data set of generic designs is then generated and validated. The generation of the generic design proposals is accompanied by a specific product development example of a drone combustion engine. Keywords Multidisciplinary Optimization Problem, Synthetic Data, Deep Learning © 2021 die Autoren | DOI: https://doi.org/10.35199/dfx2021.11 1. Introduction and Idea of This Research Due to its great design freedom, additive manufacturing (AM) shows a high potential of functional integration and part consolidation [1]-[3]. For this purpose, functions and optimization goals that are usually fulfilled by individual components must be considered in parallel.
    [Show full text]
  • Generating Realistic City Boundaries Using Two-Dimensional Perlin Noise
    Generating realistic city boundaries using two-dimensional Perlin noise Graduation thesis for the Doctoraal program, Computer Science Steven Wijgerse student number 9706496 February 12, 2007 Graduation Committee dr. J. Zwiers dr. M. Poel prof.dr.ir. A. Nijholt ir. F. Kuijper (TNO Defence, Security and Safety) University of Twente Cluster: Human Media Interaction (HMI) Department of Electrical Engineering, Mathematics and Computer Science (EEMCS) Generating realistic city boundaries using two-dimensional Perlin noise Graduation thesis for the Doctoraal program, Computer Science by Steven Wijgerse, student number 9706496 February 12, 2007 Graduation Committee dr. J. Zwiers dr. M. Poel prof.dr.ir. A. Nijholt ir. F. Kuijper (TNO Defence, Security and Safety) University of Twente Cluster: Human Media Interaction (HMI) Department of Electrical Engineering, Mathematics and Computer Science (EEMCS) Abstract Currently, during the creation of a simulator that uses Virtual Reality, 3D content creation is by far the most time consuming step, of which a large part is done by hand. This is no different for the creation of virtual urban environments. In order to speed up this process, city generation systems are used. At re-lion, an overall design was specified in order to implement such a system, of which the first step is to automatically create realistic city boundaries. Within the scope of a research project for the University of Twente, an algorithm is proposed for this first step. This algorithm makes use of two-dimensional Perlin noise to fill a grid with random values. After applying a transformation function, to ensure a minimum amount of clustering, and a threshold mech- anism to the grid, the hull of the resulting shape is converted to a vector representation.
    [Show full text]
  • CMSC 425: Lecture 14 Procedural Generation: Perlin Noise
    CMSC 425 Dave Mount CMSC 425: Lecture 14 Procedural Generation: Perlin Noise Reading: The material on Perlin Noise based in part by the notes Perlin Noise, by Hugo Elias. (The link to his materials seems to have been lost.) This is not exactly the same as Perlin noise, but the principles are the same. Procedural Generation: Big game companies can hire armies of artists to create the immense content that make up the game's virtual world. If you are designing a game without such extensive resources, an attractive alternative for certain natural phenomena (such as terrains, trees, and atmospheric effects) is through the use of procedural generation. With the aid of a random number generator, a high quality procedural generation system can produce remarkably realistic models. Examples of such systems include terragen (see Fig. 1(a)) and speedtree (see Fig. 1(b)). terragen speedtree (a) (b) Fig. 1: (a) A terrain generated by terragen and (b) a scene with trees generated by speedtree. Before discussing methods for generating such interesting structures, we need to begin with a background, which is interesting in its own right. The question is how to construct random noise that has nice structural properties. In the 1980's, Ken Perlin came up with a powerful and general method for doing this (for which he won an Academy Award!). The technique is now widely referred to as Perlin Noise (see Fig. 2(a)). A terrain resulting from applying this is shown in Fig. 2(b). (The terragen software uses randomized methods like Perlin noise as a starting point for generating a terrain and then employs additional operations such as simulated erosion, to achieve heightened realism.) Perlin Noise: Natural phenomena derive their richness from random variations.
    [Show full text]
  • State of the Art in Procedural Noise Functions
    EUROGRAPHICS 2010 / Helwig Hauser and Erik Reinhard STAR – State of The Art Report State of the Art in Procedural Noise Functions A. Lagae1,2 S. Lefebvre2,3 R. Cook4 T. DeRose4 G. Drettakis2 D.S. Ebert5 J.P. Lewis6 K. Perlin7 M. Zwicker8 1Katholieke Universiteit Leuven 2REVES/INRIA Sophia-Antipolis 3ALICE/INRIA Nancy Grand-Est / Loria 4Pixar Animation Studios 5Purdue University 6Weta Digital 7New York University 8University of Bern Abstract Procedural noise functions are widely used in Computer Graphics, from off-line rendering in movie production to interactive video games. The ability to add complex and intricate details at low memory and authoring cost is one of its main attractions. This state-of-the-art report is motivated by the inherent importance of noise in graphics, the widespread use of noise in industry, and the fact that many recent research developments justify the need for an up-to-date survey. Our goal is to provide both a valuable entry point into the field of procedural noise functions, as well as a comprehensive view of the field to the informed reader. In this report, we cover procedural noise functions in all their aspects. We outline recent advances in research on this topic, discussing and comparing recent and well established methods. We first formally define procedural noise functions based on stochastic processes and then classify and review existing procedural noise functions. We discuss how procedural noise functions are used for modeling and how they are applied on surfaces. We then introduce analysis tools and apply them to evaluate and compare the major approaches to noise generation.
    [Show full text]
  • Tile-Based Method for Procedural Content Generation
    Tile-based Method for Procedural Content Generation Dissertation Presented in Partial Fulfillment of the Requirements for the Degree Doctor of Philosophy in the Graduate School of The Ohio State University By David Maung Graduate Program in Computer Science and Engineering The Ohio State University 2016 Dissertation Committee: Roger Crawfis, Advisor; Srinivasan Parthasarathy; Kannan Srinivasan; Ken Supowit Copyright by David Maung 2016 Abstract Procedural content generation for video games (PCGG) is a growing field due to its benefits of reducing development costs and adding replayability. While there are many different approaches to PCGG, I developed a body of research around tile-based approaches. Tiles are versatile and can be used for materials, 2D game content, or 3D game content. They may be seamless such that a game player cannot perceive that game content was created with tiles. Tile-based approaches allow localized content and semantics while being able to generate infinite worlds. Using techniques such as aperiodic tiling and spatially varying tiling, we can guarantee these infinite worlds are rich playable experiences. My research into tile-based PCGG has led to results in four areas: 1) development of a tile-based framework for PCGG, 2) development of tile-based bandwidth limited noise, 3) development of a complete tile-based game, and 4) application of formal languages to generation and evaluation models in PCGG. ii Vita 2009................................................................B.S. Computer Science, San Diego State
    [Show full text]
  • A Cellular Texture Basis Function
    A Cellular Texture Basis Function Steven Worley 1 ABSTRACT In this paper, we propose a new set of related texture basis func- tions. They are based on scattering ªfeature pointsº throughout 3 Solid texturing is a powerful way to add detail to the surface of < and building a scalar function based on the distribution of the rendered objects. Perlin's ªnoiseº is a 3D basis function used in local points. The use of distributed points in space for texturing some of the most dramatic and useful surface texture algorithms. is not new; ªbombingº is a technique which places geometric fea- We present a new basis function which complements Perlin noise, tures such as spheres throughout space, which generates patterns based on a partitioning of space into a random array of cells. We on surfaces that cut through the volume of these features, forming have used this new basis function to produce textured surfaces re- polkadots, for example. [9, 5] This technique is not a basis func- sembling ¯agstone-like tiled areas, organic crusty skin, crumpled tion, and is signi®cantly less useful than noise. Lewis also used paper, ice, rock, mountain ranges, and craters. The new basis func- points scattered throughout space for texturing. His method forms a tion can be computed ef®ciently without the need for precalculation basis function, but it is better described as an alternative method of or table storage. generating a noise basis than a new basis function with a different appearance.[3] In this paper, we introduce a new texture basis function that has INTRODUCTION interesting behavior, and can be evaluated ef®ciently without any precomputation.
    [Show full text]
  • Parameterized Melody Generation with Autoencoders and Temporally-Consistent Noise
    Parameterized Melody Generation with Autoencoders and Temporally-Consistent Noise Aline Weber, Lucas N. Alegre Jim Torresen Bruno C. da Silva Institute of Informatics Department of Informatics; RITMO Institute of Informatics Federal Univ. of Rio Grande do Sul University of Oslo Federal Univ. of Rio Grande do Sul Porto Alegre, Brazil Oslo, Norway Porto Alegre, Brazil {aweber, lnalegre}@inf.ufrgs.br jimtoer@ifi.uio.no [email protected] ABSTRACT of possibilities when composing|for instance, by suggesting We introduce a machine learning technique to autonomously a few possible novel variations of a base melody informed generate novel melodies that are variations of an arbitrary by the musician. In this paper, we propose and evaluate a base melody. These are produced by a neural network that machine learning technique to generate new melodies based ensures that (with high probability) the melodic and rhyth- on a given arbitrary base melody, while ensuring that it mic structure of the new melody is consistent with a given preserves both properties of the original melody and gen- set of sample songs. We train a Variational Autoencoder eral melodic and rhythmic characteristics of a given set of network to identify a low-dimensional set of variables that sample songs. allows for the compression and representation of sample Previous works have attempted to achieve this goal by songs. By perturbing these variables with Perlin Noise| deploying different types of machine learning techniques. a temporally-consistent parameterized noise function|it is Methods exist, e.g., that interpolate pairs of melodies pro- possible to generate smoothly-changing novel melodies.
    [Show full text]
  • Communicating Simulated Emotional States of Robots by Expressive Movements
    Middlesex University Research Repository An open access repository of Middlesex University research http://eprints.mdx.ac.uk Sial, Sara Baber (2013) Communicating simulated emotional states of robots by expressive movements. Masters thesis, Middlesex University. [Thesis] Final accepted version (with author’s formatting) This version is available at: https://eprints.mdx.ac.uk/12312/ Copyright: Middlesex University Research Repository makes the University’s research available electronically. Copyright and moral rights to this work are retained by the author and/or other copyright owners unless otherwise stated. The work is supplied on the understanding that any use for commercial gain is strictly forbidden. A copy may be downloaded for personal, non-commercial, research or study without prior permission and without charge. Works, including theses and research projects, may not be reproduced in any format or medium, or extensive quotations taken from them, or their content changed in any way, without first obtaining permission in writing from the copyright holder(s). They may not be sold or exploited commercially in any format or medium without the prior written permission of the copyright holder(s). Full bibliographic details must be given when referring to, or quoting from full items including the author’s name, the title of the work, publication details where relevant (place, publisher, date), pag- ination, and for theses or dissertations the awarding institution, the degree type awarded, and the date of the award. If you believe that any material held in the repository infringes copyright law, please contact the Repository Team at Middlesex University via the following email address: [email protected] The item will be removed from the repository while any claim is being investigated.
    [Show full text]
  • CALIFORNIA STATE UNIVERSITY, NORTHRIDGE Procedural
    CALIFORNIA STATE UNIVERSITY, NORTHRIDGE Procedural Generation of Planetary-Scale Terrains in Virtual Reality A thesis submitted in partial fulfillment of the requirements For the degree of Master of Science in Software Engineering By Ryan J. Vitacion December 2018 The thesis of Ryan J. Vitacion is approved: __________________________________________ _______________ Professor Kyle Dewey Date __________________________________________ _______________ Professor Taehyung Wang Date __________________________________________ _______________ Professor Li Liu, Chair Date California State University, Northridge ii Acknowledgment I would like to express my sincere gratitude to my committee chair, Professor Li Liu, whose guidance and enthusiasm were invaluable in the development of this thesis. I would also like to thank Professor George Wang and Professor Kyle Dewey for agreeing to be a part of my committee and for their support throughout this research endeavor. iii Dedication This work is dedicated to my parents, Alex and Eppie, whose endless love and support made this thesis possible. I would also like to dedicate this work to my sister Jessica, for her constant encouragement and advice along the way. I love all of you very dearly, and I simply wouldn’t be where I am today without you by my side. iv Table of Contents Signature Page.....................................................................................................................ii Acknowledgment................................................................................................................iii
    [Show full text]
  • A Crash Course on Texturing
    A Crash Course on Texturing Li-Yi Wei Microsoft Research problems. The first problem is that it can be tedious and labor in- tensive to acquire the detailed geometric and BRDF data, either by manual drawing or by measuring real materials. The second prob- lem is that, such a detailed model, even if it can be acquired, may take significant time, memory, and computation resources to render. Fortunately, for Computer Graphics applications, we seldom need to do this. If we are simulating a physical for a more seri- ous purpose of say, designing an aircraft, an artificial heart, or a nuke head, then it is crucial that we get everything right without omitting any detail. However, for graphics, all we need is some- thing that looks right, for both appearance and motion. As research in psychology and psychophysics shows, the human visual system likes to take shortcuts in processing information (and it is proba- bly why it appears to be so fast, even for slow-witted people), and not textured this allows us to take shortcuts in image rendering as well. To avoid annoying some conservative computer scientists (which can jeopar- dize my academic career), I will use the term approximation instead of shortcut hereafter. Texturing is an approximation for detailed surface geometry and material properties. For example, assume you want to model and render a brick wall. One method is to model the detailed geome- try and material properties of all the individual bricks and mortar layers, and send this information for rendering, either via ray tracer or graphics hardware.
    [Show full text]