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Summary: A Clustering Algorithm for Radiosity in Complex Environments
Brian Smits James Arvo Donald Greenberg
Program of Computer Graphics \Lambda
Cornell University
Abstract
We present an approach for accelerating hierarchical radiosity by
clustering objects. Previous approaches constructed effective hier
archies by subdividing surfaces, but could not exploit a hierarchical
grouping on existing surfaces. This limitation resulted in an exces
sive number of initial links in complex environments. Initial linking
is potentially the most expensive portion of hierarchical radiosity
algorithms, and constrains the complexity of the environments that
can be simulated. The clustering algorithm presented here operates
by estimating energy transfers between collections of objects while
maintaining reliable error bounds on each transfer. Two methods
of bounding the transfers are employed with different tradeoffs be
tween accuracy and time. In contrast with the O(s 2 ) time and space
complexity of the initial linking in previous hierarchical radiosity
algorithms, the new methods have complexities of O(s log s) and
O(s) for both time and space. Using these methods we have ob
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