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Robust and Accurate Non-parametric Estimation of Reflectance Using Basis Decomposition and Correction Functions*
Tobias Nöll1,2, Johannes Köhler1,2, and Didier Stricker1,2
1German Research Center for Artificial Intelligence, Kaiserslautern, Germany
Tobias.Noell@dfki.de
Johannes.Koehler@dfki.de
Didier.Stricker@dfki.de
2University of Kaiserslautern, Germany
Abstract. A common approach to non-parametric BRDF estimation is the approximation of the sparsely measured input using basis decomposition. In this paper we greatly improve the fitting accuracy of such methods by iteratively applying a novel correction function to an initial estimate. We also introduce a basis to efficiently represent such a function. Based on this general concept we propose an iterative algorithm that is able to explicitly identify and treat outliers in the input data. Our method is invariant to different error metrics which alleviates the error-prone choice of an appropriate one for the given input. We evaluate our method based on a large set of experiments generated from 100 real-world BRDFs and 16 newly measured materials. The experiments show that our method outperforms other evaluated state-of-the-art basis decomposition methods by an order of magnitude in the perceptual sense for outlier ratios up to 40%.
Keywords: Non-parametric BRDF estimation, reflectance, basis decompostion, correction function, error metric, sparse data, outliers
Electronic Supplementary Material:
LNCS 8690, p. 376 ff.
lncs@springer.com
© Springer International Publishing Switzerland 2014