Texture-map computations can be made tractable through use of precalculated tables which allow computational costs independent of the texture density. The first example of this technique, the “mip” map, uses a set of tables containing successively lower-resolution representations filtered down from the discrete texture function. An alternative method using a single table of values representing the integral over the texture function rather than the function itself may yield superior results at similar cost. The necessary algorithms to support the new technique are explained. Finally, the cost and performance of the new technique is compared to previous techniques.
Appel [3] and then Bouknight and Kelley [5] have demonstrated solutions to the shadow problem which are discussed in this paper in the context of a classification scheme for shadow algorithms. Three classes of solution are currently identifiable (there may be further undiscovered classes). Appel, Bouknight and Kelley have shown solutions of one class and algorithms suggesting the other two classes have been proposed but not yet implemented.The first class of algorithm, demonstrated by Appel, Bouknight and Kelley, detects shadow boundaries as the image is produced by a raster-scan. The edges of cast shadows are found by projecting potential shadowing polygon edges onto the surface being scanned. Shadow edges thus formed are then projected onto the image plane. Upon crossing a shadow edge, the color of a scan segment is changed appropriately.A second class of algorithm involves two passes through a hidden-surface algorithm, or perhaps a single pass through each of two differing algorithms. The first pass distinguishes shadowed and unshadowed surfaces and divides partially shadowed surfaces by determining hidden surfaces from a viewpoint coincident with the light source. The colors of shadowed surfaces are then modified and a second pass operates on the augmented data from the observer's viewpoint.The third class of shadow algorithm involves calculating the surface enclosing the volume of space swept out by the shadow of an object, its umbra. The umbra surface is then added to the data and treated as an invisible surface which, when pierced, causes a transition into or out of an object shadow.A more complete explanation of the three classes follows with suggested implementations in each class. These will be preceded by remarks on modeling of the light source and followed by an attempted comparison of the practical difficulties in implementing the three approaches. MODELING THE LIGHT SOURCELight sources are generally modeled as either points or directions. However, an actual light ABSTRACT Shadows are advocated for improved comprehension and enhanced realism in computer-synthesized images. A classification of shadow algorithms delineates three approaches: shadow computation during scanout; division of object surfaces into shadowed and unshadowed areas prior to removal of hidden surfaces; and inclusion of shadow volumes in the object data. The classes are related to existing shadow algorithms and implementations within each class are sketched. A brief comparison of the three approaches suggests that the last approach has the most appealing characteristics.
Certain defects, such as jagged edges and disappearing detail, have long been an annoyance in digitally generated shaded images. Although increasing the resolution or defocusing the display can attenuate them, an understanding of these defects leads to more effective methods. This paper explains the observed defects in terms of the aliasing phenomenon inherent in sampled signals and discusses prefiltering as a recognized cure. A method for evaluating filters is presented, the application of prefiltering to hidden-surface algorithms is discussed, and an implementation of a filtering tiler is shown accompanied by examples of its effectiveness.
Appel [3] and then Bouknight and Kelley [5] have demonstrated solutions to the shadow problem which are discussed in this paper in the context of a classification scheme for shadow algorithms. Three classes of solution are currently identifiable (there may be further undiscovered classes). Appel, Bouknight and Kelley have shown solutions of one class and algorithms suggesting the other two classes have been proposed but not yet implemented.The first class of algorithm, demonstrated by Appel, Bouknight and Kelley, detects shadow boundaries as the image is produced by a raster-scan. The edges of cast shadows are found by projecting potential shadowing polygon edges onto the surface being scanned. Shadow edges thus formed are then projected onto the image plane. Upon crossing a shadow edge, the color of a scan segment is changed appropriately.A second class of algorithm involves two passes through a hidden-surface algorithm, or perhaps a single pass through each of two differing algorithms. The first pass distinguishes shadowed and unshadowed surfaces and divides partially shadowed surfaces by determining hidden surfaces from a viewpoint coincident with the light source. The colors of shadowed surfaces are then modified and a second pass operates on the augmented data from the observer's viewpoint.The third class of shadow algorithm involves calculating the surface enclosing the volume of space swept out by the shadow of an object, its umbra. The umbra surface is then added to the data and treated as an invisible surface which, when pierced, causes a transition into or out of an object shadow.A more complete explanation of the three classes follows with suggested implementations in each class. These will be preceded by remarks on modeling of the light source and followed by an attempted comparison of the practical difficulties in implementing the three approaches. MODELING THE LIGHT SOURCELight sources are generally modeled as either points or directions. However, an actual light ABSTRACT Shadows are advocated for improved comprehension and enhanced realism in computer-synthesized images. A classification of shadow algorithms delineates three approaches: shadow computation during scanout; division of object surfaces into shadowed and unshadowed areas prior to removal of hidden surfaces; and inclusion of shadow volumes in the object data. The classes are related to existing shadow algorithms and implementations within each class are sketched. A brief comparison of the three approaches suggests that the last approach has the most appealing characteristics.
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