Abstract
A novel method for approximating the radiosity kernel by a discrete set of basis functions is presented. The algorithm is characterized by selecting samples from the geometry definition and iteratively creates a functional model instantiated by a set of Gaussian basis functions. These are supported over the whole environment and thus, surfaces are not considered separately. Together with the implicit clustering algorithm provided by the applied learning scheme, the algorithm accounts ideally for coherence in the global kernel function.
On one hand, this leads to a very sparse representation of the kernel. On the other hand, by avoiding the creation of initial basis functions for separate pairs of surfaces, the method is capable of calculating even huge geometries to a desired accuracy with a proportional amount of computing resources.
Recent results from the field of artificial neural networks (the Growing Cell Structures) are extended for the presented learning algorithm. This work is done in Flatland, but there are no methodical constraints which bound the application to two dimensions.
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Bohn, CA. (1996). Efficiently Representing the Radiosity Kernel through Learning. In: Pueyo, X., Schröder, P. (eds) Rendering Techniques ’96. EGSR 1996. Eurographics. Springer, Vienna. https://doi.org/10.1007/978-3-7091-7484-5_13
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DOI: https://doi.org/10.1007/978-3-7091-7484-5_13
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