An Approach to Preprocess and Cluster a BRDF Database

[Display omitted] The Bidirectional Reflectance Distribution Function (BRDF) represents a material through the incoming light on its surface. In this context, material clustering contributes to selecting a basis of representative BRDFs, the reconstruction of BRDFs, the personalization of the appeara...

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Bibliographic Details
Published in:Graphical models 2022-01, Vol.119, p.101123, Article 101123
Main Authors: Nunes, Mislene da Silva, Colaço Júnior, Methanias, Miranda Jr, Gastão Florêncio, Andrade, Beatriz Trinchão
Format: Article
Language:English
Subjects:
clustering
dimensionality reduction
reflectance functions
tabular BRDFs
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Summary:[Display omitted] The Bidirectional Reflectance Distribution Function (BRDF) represents a material through the incoming light on its surface. In this context, material clustering contributes to selecting a basis of representative BRDFs, the reconstruction of BRDFs, the personalization of the appearance of materials, and image-based estimation of material properties. This work presents an approach to cluster a BRDF database according to its reflectance features. We first preprocess a BRDF database by mapping it to an image slice database and then find the best parameters for the LLE method through an empirical analysis, retrieving lower-dimensional databases. We performed a controlled experiment using the k-means, k-medoids, and spectral clustering algorithms applied to the low-dimensional databases. K-means presented the best overall result compared to the other clustering algorithms. For applications that require cluster representatives from the database, we suggest using k-medoids, which presented results close to those of the k-means.
ISSN:1524-0703
1524-0711
DOI:10.1016/j.gmod.2021.101123