Robust and Accurate Non-Parametric Estimation of Reflectance using Basis Decomposition and Correction Functions
Tobias Nöll, Johannes Köhler, Didier Stricker
Proceedings of the European Conference on Computer Vision (ECCV) European Conference on Computer Vision (ECCV-14), September 6-12, Zürich, Switzerland

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

Robust and Accurate Non-Parametric Estimation of Reflectance using Basis Decomposition and Correction Functions
(Hrsg.)
Proceedings of the European Conference on Computer Vision (ECCV) European Conference on Computer Vision (ECCV-14), September 6-12, Zürich, Switzerland

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