Robust Fitting on Poorly Sampled Data for Surface Light Field Rendering and Image Relighting

• Published in: Computer graphics forum Vol. 32; no. 6; pp. 101 - 112
• Main Authors: Vanhoey, K., Sauvage, B., Génevaux, O., Larue, F., Dischler, J.-M.
• Format: Journal Article
• Language: English
• Published: Oxford Blackwell Publishing Ltd 01.09.2013; Wiley
• Subjects: 3D data acquisition; Analysis; aspect reconstruction; Color; Colour; Computer graphics; Computer Science; Fittings; I.3.3 [Computer Graphics]: Picture/Image Generation-Digitizing and scanning; I.4.1 [Computer Graphics]: Digitization and Image Capture-Reflectance; image-based relighting; image-based rendering; Mathematical analysis; Mathematical models; Polynomials; Reconstruction; Sampling; Studies; surface light fields; Two dimensional; aspect reconstruction; surface light fields; 3D data acquisition; image-based rendering; image-based relighting
• ISSN: 0167-7055; 1467-8659
• DOI: 10.1111/cgf.12073

Two‐dimensional (2D) parametric colour functions are widely used in Image‐Based Rendering and Image Relighting. They make it possible to express the colour of a point depending on a continuous directional parameter: the viewing or the incident light direction. Producing such functions from acquired data is promising but difficult. Indeed, an intensive acquisition process resulting in dense and uniform sampling is not always possible. Conversely, a simpler acquisition process results in sparse, scattered and noisy data on which parametric functions can hardly be fitted without introducing artefacts. Within this context, we present two contributions. The first one is a robust least‐squares‐based method for fitting 2D parametric colour functions on sparse and scattered data. Our method works for any amount and distribution of acquired data, as well as for any function expressed as a linear combination of basis functions. We tested our fitting for both image‐based rendering (surface light fields) and image relighting using polynomials and spherical harmonics. The second one is a statistical analysis to measure the robustness of any fitting method. This measure assesses a trade‐off between precision of the fitting and stability with respect to input sampling conditions. This analysis along with visual results confirm that our fitting method is robust and reduces reconstruction artefacts for poorly sampled data while preserving the precision for a dense and uniform sampling. Generating surface light fields from real acquisition campaigns' data often leads to robustness issues that are due to irregular distribution and sparsity of the photographic sampling. Within this context, we present a robust least‐squares‐based method for fitting 2D parametric colour functions on sparse and scattered data. Moreover, we provide a statistical analysis to measure the robustness of such fitting approaches. The proposed method allows, on one hand, for high‐quality reconstructions in good sampling conditions and, on the other hand, for robust and predictable reconstructions in poor sampling conditions.