Browsing by Author "Mellado, Nicolas"

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    Recursive Analytic Spherical Harmonics Gradient for Spherical Lights
    (The Eurographics Association and John Wiley & Sons Ltd., 2022) Mézières, Pierre; Mellado, Nicolas; Barthe, Loïc; Paulin, Mathias; Chaine, Raphaëlle; Kim, Min H.
    When rendering images using Spherical Harmonics (SH), the projection of a spherical function on the SH basis remains a computational challenge both for high-frequency functions and for emission functions from complex light sources. Recent works investigate efficient SH projection of the light field coming from polygonal and spherical lights. To further reduce the rendering time, instead of computing the SH coefficients at each vertex of a mesh or at each fragment on an image, it has been shown, for polygonal area light, that computing both the SH coefficients and their spatial gradients on a grid covering the scene allows the efficient and accurate interpolation of these coefficients at each shaded point. In this paper, we develop analytical recursive formulae to compute the spatial gradients of SH coefficients for spherical light. This requires the efficient computation of the spatial gradients of the SH basis function that we also derive. Compared to existing method for polygonal light, our method is faster, requires less memory and scales better with respect to the SH band limit. We also show how to approximate polygonal lights using spherical lights to benefit from our derivations. To demonstrate the effectiveness of our proposal, we integrate our algorithm in a shading system able to render fully dynamic scenes with several hundreds of spherical lights in real time.
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    Stable and Efficient Differential Estimators on Oriented Point Clouds
    (The Eurographics Association and John Wiley & Sons Ltd., 2021) Lejemble, Thibault; Coeurjolly, David; Barthe, Loïc; Mellado, Nicolas; Digne, Julie and Crane, Keenan
    Point clouds are now ubiquitous in computer graphics and computer vision. Differential properties of the point-sampled surface, such as principal curvatures, are important to estimate in order to locally characterize the scanned shape. To approximate the surface from unstructured points equipped with normal vectors, we rely on the Algebraic Point Set Surfaces (APSS) [GG07] for which we provide convergence and stability proofs for the mean curvature estimator. Using an integral invariant viewpoint, this first contribution links the algebraic sphere regression involved in the APSS algorithm to several surface derivatives of different orders. As a second contribution, we propose an analytic method to compute the shape operator and its principal curvatures from the fitted algebraic sphere. We compare our method to the state-of-the-art with several convergence and robustness tests performed on a synthetic sampled surface. Experiments show that our curvature estimations are more accurate and stable while being faster to compute compared to previous methods. Our differential estimators are easy to implement with little memory footprint and only require a unique range neighbors query per estimation. Its highly parallelizable nature makes it appropriate for processing large acquired data, as we show in several real-world experiments.