Browsing by Author "Mitchell, Kenny"

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    Image Based Proximate Shadow Retargeting
    (The Eurographics Association, 2018) Casas, Llogari; Fauconneau, Matthias; Kosek, Maggie; Mclister, Kieran; Mitchell, Kenny; {Tam, Gary K. L. and Vidal, Franck
    We introduce Shadow Retargeting which maps real shadow appearance to virtual shadows given a corresponding deformation of scene geometry, such that appearance is seamlessly maintained. By performing virtual shadow reconstruction from un-occluded real shadow samples observed in the camera frame, we recover the deformed shadow appearance efficiently. Our method uses geometry priors for the shadow casting object and a planar receiver surface. Inspired by image retargeting approaches [VTP10] we describe a novel local search strategy, steered by importance based deformed shadow estimation. Results are presented on a range of objects, deformations and illumination conditions in real-time Augmented Reality (AR) on a mobile device. We demonstrate the practical application of the method in generating otherwise laborious in-betweening frames for 3D printed stop motion animation.
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    Noise Reduction on G‐Buffers for Monte Carlo Filtering
    (© 2017 The Eurographics Association and John Wiley & Sons Ltd., 2017) Moon, Bochang; Iglesias‐Guitian, Jose A.; McDonagh, Steven; Mitchell, Kenny; Chen, Min and Zhang, Hao (Richard)
    We propose a novel pre‐filtering method that reduces the noise introduced by depth‐of‐field and motion blur effects in geometric buffers (G‐buffers) such as texture, normal and depth images. Our pre‐filtering uses world positions and their variances to effectively remove high‐frequency noise while carefully preserving high‐frequency edges in the G‐buffers. We design a new anisotropic filter based on a per‐pixel covariance matrix of world position samples. A general error estimator, Stein's unbiased risk estimator, is then applied to estimate the optimal trade‐off between the bias and variance of pre‐filtered results. We have demonstrated that our pre‐filtering improves the results of existing filtering methods numerically and visually for challenging scenes where depth‐of‐field and motion blurring introduce a significant amount of noise in the G‐buffers.We propose a novel pre‐filtering method that reduces the noise introduced by depth‐of‐field and motion blur effects in geometric buffers (G‐buffers) such as texture, normal and depth images. Our pre‐filtering uses world positions and their variances to effectively remove high‐frequency noise while carefully preserving high‐frequency edges in the G‐buffers. We design a new anisotropic filter based on a per‐pixel covariance matrix of world position samples. A general error estimator, Stein's unbiased risk estimator, is then applied to estimate the optimal trade‐off between the bias and variance of pre‐filtered results.