A clustering algorithm for radiosity in complex environments

  • Authors:

  • Brian Smits;James Arvo;Donald Greenberg

  • Affiliations:

  • Program of Computer Graphics, Cornell University, 580 ETC Building, Cornell University, Ithaca, NY;Program of Computer Graphics, Cornell University, 580 ETC Building, Cornell University, Ithaca, NY;Program of Computer Graphics, Cornell University, 580 ETC Building, Cornell University, Ithaca, NY

  • Venue:
  • SIGGRAPH '94 Proceedings of the 21st annual conference on Computer graphics and interactive techniques
  • Year:
  • 1994

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Abstract

We present an approach for accelerating hierarchical radiosity by clustering objects. Previous approaches constructed effective hierarchies by subdividing surfaces, but could not exploit a hierarchical grouping on existing surfaces. This limitation resulted in an excessive 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 transfer between collections of objects while maintaining reliable error bounds on each transfer. Two methods of bounding the transfers are employed with different tradeoffs between accuracy and time. In contrast with the O(s2) time and space complexity of the initial linking in previous hierarchical radiosity algorithms, the new methods have complexities of O(slogs) and O(s) for both time and space. Using these methods we have obtained speedups of two orders of magnitude for environments of moderate complexity while maintaining comparable accuracy.