Abstract
High-quality Monte Carlo image synthesis needs to use realistic bidirectional reflectance distribution functions (BRDFs) based on acquired reflectance data. In this study, we propose a novel model that contributes to solving memory problems and measurement noise problems of BRDFs. The model is a composite associative memory model constructed by integration of Kohonen's Self Organizing Feature Map (SOM), Mass Attraction Network (MAN), and it has been tested on data set acquired by Matusik et al.. Because it is based on table-lookups, it can't be categorized in analytical reflectance models. We show that our approach has acceptable compactness. We also show that it has more accuracy than both analytical BRDF models and Lawrence et al.'s table-based model. In this work, we are able to create images efficiently under global illumination with less memory requirements.
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Index Terms
Representing BRDFs using SOMs and MANs
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