Advanced Modeling Techniques For Computer Graphics Engineering Essay

Advanced Modeling Techniques For Computer Graphics Engineering EssayIn the past thirty years, casting techniques in computer graphics save evolved signifi bottomlandtly as the field has matured and attempted to portray the mazyities of nature. polygonal models, patches, points, and lines ar insufficient to represent the complexities of instinctive objects and intricate man-made objects in a manageable and controllable fashion. Higher-level modeling techniques have been developed to rear an bring upion of the model, encode classes of objects, and dispense with high-level control and spec of the model. The design of these ripe(p) modeling techniques is to provide a concise, efficient, flexible, and controllable mechanism for denominateing and invigorate models of complex objects and raw(a) phenomena. Most of these advanced modeling techniques can be considered adjectival modeling techniques code segments or algorithms are utilize to abstract and encode the details of t he model instead of explicitly storing vast poem of low-level primitives. The put on of algorithms unburdens the modeler/animator of low-level control, provides great flexibility, and let ins elaboration of his efforts by parametric control a a couple of(prenominal) parameters to the model event monumental amounts of geometric details (Smith 1984 called this database amplification). This survey examines several types of adjectival techniques, including fractals, grammar- base models, volumetric procedural models, unverbalized surfaces, and particle systems.Fractals Peitgen et al. 1992 have a nice mathematical definition, entirely in computer graphics their definition has been widen to refer generally to models with a large degree of self-similarity subpieces of the object see to be scaled down, possibly translated and rotated versions of the maestro object. Along these lines, Musgrave Ebert et al. 1994 coiffe a fractal as a geometrically complex object, the complex ity of which arises by the repetition of form over some range of scale. Many natural objects exhibit this characteristic, including mountains, coastlines, trees, plants (e.g., cauliflower), water, and clouds. Fractals can generally be classified as settled or non-deterministic (also called random fractals), depending on whether they contain randomness. Random fractals have been employ extensively in computer graphics to model natural objects, most(prenominal) notably terrain. Most fractal terrain-generation algorithms work through recursive subdivision and pseudorandom perturbation. An original surface is defined and divided equally into subparts. New vertices are giveed and pseudorandomly displaced from the original surface, with a displacement magnitude that decreases at each iteration as the frequency increases. Therefore, the first iteration gives the large peaks on the surface, and later subdivisions add small-scale detail. Only the parameters for controlling the random- human body generator, the level of subdivision, and the roughness of the surface are needed to define an extremely complex terrain. Recent work in fractals has included the mask of diffusion-limited aggregation Copyright 1996, CRC Press. ACM deliberation Surveys, Vol. 28, No. 1, bound 1996 (DLA) models and the use of multi-fractals Ebert et al. 1994, which allows different fractal dimensions (degrees of roughness) in the models to simulate natural terrain better.GRAMMAR-BASED MODELSGrammar-based models, in general Lsystems Prusinkiewicz and Lindenmayer 1990, also allow natural complexity to be specified with a few parameters. Grammar- based models have been used by umpteen authors, including Lindenmayer, Prusinkiewicz, and Fowler, to pee remarkably pictorial models and images of trees, plants, and seashells. These models use formal languages, jibe graph grammars called L-systems, to delineate natural structures algorithmically and are closely related to deterministic frac tals in their self-similarity, but fail to meet the precise mathematical definition of a fractal.1 An L-system is a formal language where all the rules are applied in parallel to provide a final sentence describing the object. In the L-system, each remainder symbol represents a part of the object or a directional command to be interpreted by a three-dimensional displace mechanism (turtle graphics). A sentence for a tree would contain voice communication describing each branch, its length, size, and branching angle, when it develops, and its connection in the tree. More complex L-systems, IL-systems, include context- sensitivity, word age information, and probabilistic rule evaluation, which allows each plant to be unique. Recent work in L-systems allows better developmental models, more(prenominal) advanced biologically based growth models, incorporation of more growth parameters, and environmental effects.VOLUMETRIC PROCEDURAL MODELSAnother procedural modeling technique, volum etric procedural modeling (also called hypertextures, volume density functions, and fuzzy blobbies), uses algorithms to define and glorify threedimensional volumetric objects and natural phenomena Ebert et al. 1994. These techniques have been used to model natural phenomena such as fire (Stam and Inakage), gases such as smoke, clouds, and fog (Ebert, Perlin, Sakas, Stam), and water (Ebert, Perlin). The volumetric procedures direct as input a point location in space, a time parameter, and parameters that describe the object being modeled, and return the density and comment of the object for that location in space. Complex volumetric phenomena can, therefore, be exposit with a few parameters. Perlin has successfully used this technique to create realistic rock arches, woven fabric, smoke, and fur Ebert et al. 1994, basing his procedures on a statistical simulation of turbulence and random noise to give natural-looking complexity to the objects. Ebert et al. 1994 have used similar functions to model and animate steam, fog, smoke, clouds, and solid marble. These procedural techniques allow the use of simple simulations of natural complexity (noise, turbulence) to speed computation, but also allow the incorporation of physically based parameters, where appropriate and feasible. This flexibility is one of the many advantages of procedural techniques.IMPLICIT SURFACESWhile previously discussed techniques have been used primarily for modeling the complexities of nature, implicit surfaces Wyvill et al. 1986 Wyvill and Gascuel 1995 (also called blobby molecules, metaballs, and soft objects) have mainly been used for modeling organic shapes, complex man-made shapes, and soft objects that are difficult to animate and describe using more traditional techniques. Implicit surfaces are a more concise representation than parametric surfaces and provide flexibility in modeling and animating soft objects. Im- 1Some authors consider L-systems to be deterministic fractals. 154 David S. Ebert ACM Computing Surveys, Vol. 28, No. 1, March 1996 plicit surfaces are iso-valued surfaces created from blending primitives (skeletal elements) represented by implicit equations of the form F(x, y, z) 5 0. Each primitive is a procedure that returns a functional value for the field defined by the implicit equation. A key feature of implicit surfaces is the procedural, smooth, often volume-preserving blending of primitives to form quite complex surfaces from simple primitives. Objects are defined as offsets (isosurfaces) from a serial of blended skeletal elements (points, lines, polygons, spheres, ellipsoids, and so on). Modeling and animation of implicit surfaces is achieved by controlling the skeletal elements and blending functions, which provide complex models and animations from a few parameters (another example of data amplification). Recent work in implicit surfaces Wyvill and Gascuel 995 has increase their use to character modeling and animation, human figur e modeling, and representing rigid objects through the addition of CSG (constructive solid geometry) operators.PARTICLE SYSTEMS element systems differ from the previous foursome techniques in that their abstraction is in control of the animation and specification of the object. Particle systems do use a large database of geometric primitives to represent natural objects (fuzzy objects), but the animation, location, birth, and death of the particles representing the object are controlled algorithmically. Particle systems are most commonly used to represent natural phenomena such as fire, water, clouds, snow, rain, grass, and trees Reeves and Blau 1985. A particle- system object is represented by a large collection (cloud) of very simple geometric particles that change stochastically over time. The procedural aspect and main power of particle systems allow the specification and control of this extremely large cloud of geometric particles with very few parameters. Besides the geometri c particles, a particle system has controllable stochastic particleanimation procedures that govern the creation, movement, and death of the particles. These animation procedures often include physically based forces to simulate effects such as gravity, vorticity, conservation of momentum, and energy. Particle systems shake up special interpretation problems because of the large number of primitives, but specialized rendering techniques, including probabilistic rendering algorithms, have been developed to render particle systems Reeves and Blau 1985. incoming DIRECTIONSAdvanced modeling techniques leave alone continue to play an important situation in computer graphics. As computers become more powerful, the complexity that can be rendered will increase however, the ability of humans to specify more geometric complexity (millions of primitives) will not. Therefore, procedural techniques, with their ability to amplify the substance abusers specification and control, are the only viable alternative. The ability of these techniques to specify and control incredibly realistic and detailed models with a small number of user-specified parameters will evolve. More work will be done to allow high-level control and specification of models in user-understandable terms, while more complex algorithms and improved physically based simulations will be incorporated into these procedures. Finally, autoloading(prenominal) generation of the procedural models through artificial evolution techniques, similar to those of Sims 1994, will greatly enhance the capabilities and uses of these advanced modeling techniques.