A Sparse Non-parametric BRDF Model
Open Access
- 26 October 2022
- journal article
- research article
- Published by Association for Computing Machinery (ACM) in ACM Transactions on Graphics
- Vol. 41 (5), 1-18
- https://doi.org/10.1145/3533427
Abstract
This paper presents a novel sparse non-parametric BRDF model derived using a machine learning approach to represent the space of possible BRDFs using a set of multidimensional sub-spaces, or dictionaries. By training the dictionaries under a sparsity constraint, the model guarantees high quality representations with minimal storage requirements and an inherent clustering of the BDRF-space. The model can be trained once and then reused to represent a wide variety of measured BRDFs. Moreover, the proposed method is flexible to incorporate new unobserved data sets, parameterizations, and transformations. In addition, we show that any two, or more, BRDFs can be smoothly interpolated in the coefficient space of the model rather than the significantly higher-dimensional BRDF space. The proposed sparse BRDF model is evaluated using the MERL, DTU and RGL-EPFL BRDF databases. Experimental results show that the proposed approach results in about 9.75dB higher SNR on average for rendered images as compared to current state-of-the-art models.Keywords
Funding Information
- Knut and Alice Wallenberg Foundation
- Wallenberg Autonomous Systems and Software Program
- Strategic Research Environment ELLIIT
- EU H2020 Research, and Innovation Programme (694122)
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