An appearance-driven space to create new BRDFs

• Mislene da Silva Nunes UFS
• Gastão Florêncio Miranda Junior UFS
• Beatriz Trinchão Andrade UFS

Resumo

The search for more realism in renderings led to increased interest in tabular bidirectional reflectance distribution function (BRDF) samples captured from real-world materials. This representation provides a high degree of realism at the cost of a long acquisition time and high storage space. Because of those drawbacks, the literature presents few BRDF databases captured from real materials, and the available ones present a limited number of samples. Aiming to create new and realistic-looking materials, this work proposes an approach to generate new BRDFs from a tabular BRDF database. Our goal is to provide a pipeline where the user expresses appearance necessities by selecting materials from a BRDF database. We generate an appearance-driven space shaped by the user’s interests, in which it is feasible to navigate and retrieve new tabular BRDFs. This new space should enable smooth navigation and, to provide visual richness and realism, not rely only on the materials selected by the user. To this end, we first preprocess a tabular BRDF database by keeping BRDF samples with relevant reflectance features. A low-dimensional representation of this preprocessed database is obtained by applying a dimensionality reduction method, and a clustering algorithm is applied to obtain clusters of materials with similar features. Based on the materials selected by the user, we define one or more clusters of materials to build the appearance-driven space. Any point in this lower-dimensional space can be mapped to the original BRDFs space. Therefore, new BRDFs that present similar properties to the ones which the user selected are created. We compare the performance of linear (Multidimensional Scaling - MDS) and nonlinear (Isometric Feature Mapping - ISOMAP) dimensionality reduction methods in this task and analyze the obtained materials.

Palavras-chave: Tabular BRDFs, Dimensionality reduction, Clustering