Browsing by Author "Chen, Yanyun"

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    Instant Stippling on 3D Scenes
    (The Eurographics Association and John Wiley & Sons Ltd., 2018) Ma, Lei; Guo, Jianwei; Yan, Dong-Ming; Sun, Hanqiu; Chen, Yanyun; Fu, Hongbo and Ghosh, Abhijeet and Kopf, Johannes
    In this paper, we present a novel real-time approach to generate high-quality stippling on 3D scenes. The proposed method is built on a precomputed 2D sample sequence called incremental Voronoi set with blue-noise properties. A rejection sampling scheme is then applied to achieve tone reproduction, by thresholding the sample indices proportional to the inverse target tonal value to produce a suitable stipple density. Our approach is suitable for stippling large-scale or even dynamic scenes because the thresholding of individual stipples is trivially parallelizable. In addition, the static nature of the underlying sequence benefits the frame-to-frame coherence of the stippling. Finally, we propose an extension that supports stipples of varying sizes and tonal values, leading to smoother spatial and temporal transitions. Experimental results reveal that the temporal coherence and real-time performance of our approach are superior to those of previous approaches.
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    Learning Scene Illumination by Pairwise Photos from Rear and Front Mobile Cameras
    (The Eurographics Association and John Wiley & Sons Ltd., 2018) Cheng, Dachuan; Shi, Jian; Chen, Yanyun; Deng, Xiaoming; Zhang, Xiaopeng; Fu, Hongbo and Ghosh, Abhijeet and Kopf, Johannes
    Illumination estimation is an essential problem in computer vision, graphics and augmented reality. In this paper, we propose a learning based method to recover low-frequency scene illumination represented as spherical harmonic (SH) functions by pairwise photos from rear and front cameras on mobile devices. An end-to-end deep convolutional neural network (CNN) structure is designed to process images on symmetric views and predict SH coefficients. We introduce a novel Render Loss to improve the rendering quality of the predicted illumination. A high quality high dynamic range (HDR) panoramic image dataset was developed for training and evaluation. Experiments show that our model produces visually and quantitatively superior results compared to the state-of-the-arts. Moreover, our method is practical for mobile-based applications.