A clustering algorithm for radiosity in complex environments

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 transfers 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(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 obtained speedups of two orders of magnitude for environments of moderate complexity while maintaining comparable accuracy.

A Clustering Algorithm for Radiance Calculation In General Environments

“…It is interesting to note the benefit of storing the radiant intensity in the form of a directional distribution, since the transfer estimate does not require the interrogation of the cluster contents. This represents a potential gain over previous hierarchical clustering algorithms [6,10].…”

“…We assume here that the reader is familiar with hierarchical radiosity and clustering algorithms [3,10,6]. In these methods, a hierarchical subdivision structure of 3D space is used to collect surfaces into clusters.…”

“…The main advantage of this approach is that the amount of work performed for each link in the gathering phase is fixed, and does not depend on the clusters' complexity. This "constant-time" transfer computation, combined with the linear number of links with respect to the total number of surfaces [3,10], results in a clustering algorithm with linear asymptotic complexity.…”

Interactive Common Illumination for Computer Augmented Reality

“…Recent advances in global illumination technology allow us to calculate the lighting efficiently, using hierarchical radiosity [16], clustering [24,25] and incremental update methods [8]. To initialise the system, the calculation of certain basic parameters is required.…”

“…This method allows us to model the real scene to the level of detail required, and to extract camera parameters simply and automatically. We use fast hierarchical [16,24,25] and incremental update [8] techniques for radiosity, permitting interaction with virtual objects in the CAR environment. Interactive update rates (a few seconds per frame) of the mixed real/synthetic environment, including common illumination is achieved by using a texturebased rendering approach on suitable hardware.…”