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Efficient antialiased rendering of 3-D linear fractals
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Source International Conference on Computer Graphics and Interactive Techniques archive
Proceedings of the 18th annual conference on Computer graphics and interactive techniques table of contents
Pages: 91 - 100  
Year of Publication: 1991
ISBN:0-89791-436-8
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Authors
John C. Hart  Electronic Visualization Laboratory, University of Illinois at Chicago
Thomas A. DeFanti  Electronic Visualization Laboratory, University of Illinois at Chicago
Sponsor
SIGGRAPH: ACM Special Interest Group on Computer Graphics and Interactive Techniques
Publisher
ACM  New York, NY, USA
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Downloads (6 Weeks): 8,   Downloads (12 Months): 41,   Citation Count: 5
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ABSTRACT

Object instancing is the efficient method of representing an hierarchical object with a directed graph instead of a tree. If this graph contains a cycle then the object it represents is a linear fractal. Linear fractals are difficult to render for three specific reasons: (1) ray-fractal intersection is not trivial, (2) surface normals are undefined and (3) the object aliases at all sampling resolutions.Ray-fractal intersections are efficiently approximated to sub-pixel accuracy using procedural bounding volumes and a careful determination of the size of a pixel, giving the perception that the surface is infinitely detailed. Furthermore, a surface normal for these non-differentiable surfaces is defined and analyzed. Finally, the concept of antialiasing "covers" is adapted and used to solve the problem of sampling fractal surfaces.An initial bounding volume estimation method is also described, allowing a linear fractal to be rendered given only its iterated, function system. A parallel implementation of these methods is described and applications of these results to the rendering of other fractal models are given.


REFERENCES

Note: OCR errors may be found in this Reference List extracted from the full text article. ACM has opted to expose the complete List rather than only correct and linked references.

 
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Collaborative Colleagues:
John C. Hart: colleagues
Thomas A. DeFanti: colleagues