Browsing by Author "Yan, Lingqi"

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    Adaptive BRDF-Oriented Multiple Importance Sampling of Many Lights
    (The Eurographics Association and John Wiley & Sons Ltd., 2019) Liu, Yifan; Xu, Kun; Yan, Ling-Qi; Boubekeur, Tamy and Sen, Pradeep
    Many-light rendering is becoming more common and important as rendering goes into the next level of complexity. However, to calculate the illumination under many lights, state of the art algorithms are still far from efficient, due to the separate consideration of light sampling and BRDF sampling. To deal with the inefficiency of many-light rendering, we present a novel light sampling method named BRDF-oriented light sampling, which selects lights based on importance values estimated using the BRDF's contributions. Our BRDF-oriented light sampling method works naturally with MIS, and allows us to dynamically determine the number of samples allocated for different sampling techniques. With our method, we can achieve a significantly faster convergence to the ground truth results, both perceptually and numerically, as compared to previous many-light rendering algorithms.
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    A Bayesian Inference Framework for Procedural Material Parameter Estimation
    (The Eurographics Association and John Wiley & Sons Ltd., 2020) Guo, Yu; Hasan, Milos; Yan, Lingqi; Zhao, Shuang; Eisemann, Elmar and Jacobson, Alec and Zhang, Fang-Lue
    Procedural material models have been gaining traction in many applications thanks to their flexibility, compactness, and easy editability. We explore the inverse rendering problem of procedural material parameter estimation from photographs, presenting a unified view of the problem in a Bayesian framework. In addition to computing point estimates of the parameters by optimization, our framework uses a Markov Chain Monte Carlo approach to sample the space of plausible material parameters, providing a collection of plausible matches that a user can choose from, and efficiently handling both discrete and continuous model parameters. To demonstrate the effectiveness of our framework, we fit procedural models of a range of materials-wall plaster, leather, wood, anisotropic brushed metals and layered metallic paints-to both synthetic and real target images.