Eurographics Partner Events

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    Estimating Uncertainty in Appearance Acquisition
    (The Eurographics Association, 2024) Zhou, Zhiqian; Zhang, Cheng; Dong, Zhao; Marshall, Carl; Zhao, Shuang; Haines, Eric; Garces, Elena
    The inference of material reflectance from physical observations (e.g., photographs) is usually under-constrained, causing point estimates to suffer from ambiguity and, thus, generalize poorly to novel configurations. Conventional methods address this problem by using dense observations or introducing priors. In this paper, we tackle this problem from a different angle by introducing a method to quantify uncertainties. Based on a Bayesian formulation, our method can quantitatively analyze how under-constrained a material inference problem is (given the observations and priors), by sampling the entire posterior distribution of material parameters rather than optimizing a single point estimate as given by most inverse rendering methods. Further, we present a method to guide acquisition processes by recommending viewing/lighting configurations for making additional observations. We demonstrate the usefulness of our technique using several synthetic and one real example.
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    An Implementation Algorithm of 2D Sobol Sequence Fast, Elegant, and Compact
    (The Eurographics Association, 2024) Ahmed, Abdalla G. M.; Haines, Eric; Garces, Elena
    We present a novel algorithm to evaluate 2D Sobol samples, bringing the time complexity for m-bit resolution to O(log(m)) instead of O(m), thus gaining tangible performance boost. We take advantage of the geometric structure of the underlying Pascal matrix to factor it into diagonally-running matrices that are efficient to implement using bit-wise operations. We extend the method to inversion in global Sobol sampling. The algorithms form a flexible framework, able to generate several wellknown sample sequences as special cases. We compare the speed performance and memory footprint of our algorithms to state of the art implementations.
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    Visualizing Riemannian data with Rie-SNE
    (The Eurographics Association, 2024) Bergsson, Andri; Hauberg, Søren; Archambault, Daniel; Nabney, Ian; Peltonen, Jaakko
    Faithful visualizations of data residing on manifolds must take the underlying geometry into account when producing a flat planar view of the data. In this paper, we extend the stochastic neighbor embedding (SNE) algorithm to data on general Riemannian manifolds. We replace standard Gaussian assumptions with Riemannian diffusion counterparts and propose an efficient approximation that only requires access to calculations of Riemannian distances and volumes. We demonstrate that the approach also allows for mapping data from one manifold to another, e.g. from a high-dimensional sphere to a low-dimensional one.