We describe a system that computes radiosity solutions for polygonal environments much larger than can be stored in main memory. The solution is stored in and retrieved from a database as the computation proceeds. Our system is based on two ideas: the use of visibility oracles to find source and blocker surfaces potentially visible to a receiving surface; and the use of hierarchical techniques to represent interactions between large surfaces efficiently, and to represent the computed radiosity solution compactly. Visibility information allows the environment to be partitioned into subsets, each containing all the information necessary to transfer light to a cluster of receiving polygons. Since the largest subset needed for any particular cluster is much smaller than the total size of the environment, these subset computations can be performed in much less memory than can classical or hierarchical radiosity. The computation is then ordered for further efficiency. Careful ordering of energy transfers minimizes the number of database reads and writes. We report results from large solutions of unfurnished and furnished buildings, and show that our implementation's observed running time scales nearly linearly with both local and global model complexity.

@inproceedings{Teller:1994:PAO,
   author = "Seth Teller and Celeste Fowler and Thomas Funkhouser and Pat Hanrahan",
   title = "Partitioning and Ordering Large Radiosity Computations",
   booktitle = "Proceedings of SIGGRAPH 94",
   year = "1994",
   month = jul,
   pages = "443--450"
}