Browsing by Author "Xu, Kun"

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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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    An Improved Geometric Approach for Palette-based Image Decomposition and Recoloring
    (The Eurographics Association and John Wiley & Sons Ltd., 2019) Wang, Yili; Liu, Yifan; Xu, Kun; Lee, Jehee and Theobalt, Christian and Wetzstein, Gordon
    Palette-based image decomposition has attracted increasing attention in recent years. A specific class of approaches have been proposed basing on the RGB-space geometry, which manage to construct convex hulls whose vertices act as palette colors. However, such palettes do not guarantee to have the representative colors which actually appear in the image, thus making it less intuitive and less predictable when editing palette colors to perform recoloring. Hence, we proposed an improved geometric approach to address this issue. We use a polyhedron, but not necessarily a convex hull, in the RGB space to represent the color palette. We then formulate the task of palette extraction as an optimization problem which could be solved in a few seconds. Our palette has a higher degree of representativeness and maintains a relatively similar level of accuracy compared with previous methods. For layer decomposition, we compute layer opacities via simple mean value coordinates, which could achieve instant feedbacks without precomputations. We have demonstrated our method for image recoloring on a variety of examples. In comparison with state-of-the-art works, our approach is generally more intuitive and efficient with fewer artifacts.