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Now showing 1 - 10 of 21
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    Robust Fitting on Poorly Sampled Data for Surface Light Field Rendering and Image Relighting
    (The Eurographics Association and Blackwell Publishing Ltd., 2013) Vanhoey, K.; Sauvage, B.; Génevaux, O.; Larue, F.; Dischler, J.‐M.; Holly Rushmeier and Oliver Deussen
    Two‐dimensional (2D) parametric colour functions are widely used in Image‐Based Rendering and Image Relighting. They make it possible to express the colour of a point depending on a continuous directional parameter: the viewing or the incident light direction. Producing such functions from acquired data is promising but difficult. Indeed, an intensive acquisition process resulting in dense and uniform sampling is not always possible. Conversely, a simpler acquisition process results in sparse, scattered and noisy data on which parametric functions can hardly be fitted without introducing artefacts. Within this context, we present two contributions. The first one is a robust least‐squares‐based method for fitting 2D parametric colour functions on sparse and scattered data. Our method works for any amount and distribution of acquired data, as well as for any function expressed as a linear combination of basis functions. We tested our fitting for both image‐based rendering (surface light fields) and image relighting using polynomials and spherical harmonics. The second one is a statistical analysis to measure the robustness of any fitting method. This measure assesses a trade‐off between precision of the fitting and stability with respect to input sampling conditions. This analysis along with visual results confirm that our fitting method is robust and reduces reconstruction artefacts for poorly sampled data while preserving the precision for a dense and uniform sampling.Generating surface light fields from real acquisition campaigns' data often leads to robustness issues that are due to irregular distribution and sparsity of the photographic sampling. Within this context, we present a robust least‐squares‐based method for fitting 2D parametric colour functions on sparse and scattered data. Moreover, we provide a statistical analysis to measure the robustness of such fitting approaches. The proposed method allows, on one hand, for high‐quality reconstructions in good sampling conditions and, on the other hand, for robust and predictable reconstructions in poor sampling conditions.
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    Full Wave Modelling of Light Propagation and Reflection
    (The Eurographics Association and Blackwell Publishing Ltd., 2013) Musbach, A.; Meyer, G. W.; Reitich, F.; Oh, S. H.; Holly Rushmeier and Oliver Deussen
    The propagation and reflection of electromagnetic waves in a three‐dimensional environment is simulated, and realistic images are produced using the resulting light distributions and reflectance functions. A finite difference time domain method is employed to advance the electric and magnetic fields in a scene. Surfaces containing wavelength scaled structures are created, the interaction of the electromagnetic waves with these nano‐structured materials is calculated, and the sub‐surface interference and diffraction effects are modelled. The result is a reflectance function with wavelength composition and spatial distribution properties that could not have been predicted using classic computer graphic ray tracing approaches. The techniques are employed to reproduce demonstrations of simple interference and diffraction effects, and to create computer‐generated pictures of a Morpho butterfly.The propagation and reflection of electromagnetic waves in a three‐dimensional environment is simulated, and realistic images are produced using the resulting light distributions and reflectance functions. A finite difference time domain method is employed to advance the electric and magnetic fields in a scene. Surfaces containing wavelength scaled structures are created, the interaction of the electromagnetic waves with these nano‐structured materials is calculated, and the sub‐surface interference and diffraction effects are modeled. The result is a reflectance function with wavelength composition and spatial distribution properties that could not have been predicted using classic computer graphic ray tracing approaches.
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    Efficient Non‐linear Optimization via Multi‐scale Gradient Filtering
    (The Eurographics Association and Blackwell Publishing Ltd., 2013) Martin, Tobias; Joshi, Pushkar; Bergou, Miklós; Carr, Nathan; Holly Rushmeier and Oliver Deussen
    We present a method for accelerating the convergence of continuous non‐linear shape optimization algorithms. We start with a general method for constructing gradient vector fields on a manifold, and we analyse this method from a signal processing viewpoint. This analysis reveals that we can construct various filters using the Laplace–Beltrami operator of the shape that can effectively separate the components of the gradient at different scales. We use this idea to adaptively change the scale of features being optimized to arrive at a solution that is optimal across multiple scales. This is in contrast to traditional descent‐based methods, for which the rate of convergence often stalls early once the high frequency components have been optimized. We demonstrate how our method can be easily integrated into existing non‐linear optimization frameworks such as gradient descent, Broyden–Fletcher–Goldfarb–Shanno (BFGS) and the non‐linear conjugate gradient method. We show significant performance improvement for shape optimization in variational shape modelling and parameterization, and we also demonstrate the use of our method for efficient physical simulation.We present a method for accelerating the convergence of continuous nonlinear shape optimization algorithms. We start with a general method for constructing gradient vector fields on a manifold, and we analyze this method from a signal processing viewpoint. This analysis reveals that we can construct various filters using the Laplace‐Beltrami operator of the shape that can effectively separate the components of the gradient at different scales. We use this idea to adaptively change the scale of features being optimized in order to arrive at a solution that is optimal across multiple scales.
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    Symmetry in 3D Geometry: Extraction and Applications
    (The Eurographics Association and Blackwell Publishing Ltd., 2013) Mitra, Niloy J.; Pauly, Mark; Wand, Michael; Ceylan, Duygu; Holly Rushmeier and Oliver Deussen
    The concept of symmetry has received significant attention in computer graphics and computer vision research in recent years. Numerous methods have been proposed to find, extract, encode and exploit geometric symmetries and high‐level structural information for a wide variety of geometry processing tasks. This report surveys and classifies recent developments in symmetry detection. We focus on elucidating the key similarities and differences between existing methods to gain a better understanding of a fundamental problem in digital geometry processing and shape understanding in general. We discuss a variety of applications in computer graphics and geometry processing that benefit from symmetry information for more effective processing. An analysis of the strengths and limitations of existing algorithms highlights the plenitude of opportunities for future research both in terms of theory and applications.The concept of symmetry has received significant attention in computer graphics and computer vision research in recent years. Numerous methods have been proposed to find, extract, encode, and exploit geometric symmetries and high‐level structural information for a wide variety of geometry processing tasks. This report surveys and classifies recent developments in symmetry detection. We focus on elucidating the key similarities and differences between existing methods to gain a better understanding of a fundamental problem in digital geometry processing and shape understanding in general.
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    Mesh‐Free Discrete Laplace–Beltrami Operator
    (The Eurographics Association and Blackwell Publishing Ltd., 2013) Petronetto, F.; Paiva, A.; Helou, E. S.; Stewart, D. E.; Nonato, L. G.; Holly Rushmeier and Oliver Deussen
    In this work we propose a new discretization method for the Laplace–Beltrami operator defined on point‐based surfaces. In contrast to the existing point‐based discretization techniques, our approach does not rely on any triangle mesh structure, turning out truly mesh‐free. Based on a combination of Smoothed Particle Hydrodynamics and an optimization procedure to estimate area elements, our discretization method results in accurate solutions while still being robust when facing abrupt changes in the density of points. Moreover, the proposed scheme results in numerically stable discrete operators. The effectiveness of the proposed technique is brought to bear in many practical applications. In particular, we use the eigenstructure of the discrete operator for filtering and shape segmentation. Point‐based surface deformation is another application that can be easily carried out from the proposed discretization method.In this work we propose a new discretization method for the Laplace–Beltrami operator defined on point‐based surfaces. In contrast to the existing point‐based discretization techniques, our approach does not rely on any triangle mesh structure, turning out truly meshfree. Based on a combination of Smoothed Particle Hydrodynamics and an optimization procedure to estimate area elements, our discretization method results in accurate solutions while still being robust when facing abrupt changes in the density of points. Moreover, the proposed scheme results in numerically stable discrete operators. The effectiveness of the proposed technique is brought to bear in many practical applications.
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    Analysis and Visualization of Maps Between Shapes
    (The Eurographics Association and Blackwell Publishing Ltd., 2013) Ovsjanikov, M.; Ben-Chen, M.; Chazal, F.; Guibas, L.; Holly Rushmeier and Oliver Deussen
    In this paper we propose a method for analysing and visualizing individual maps between shapes, or collections of such maps. Our method is based on isolating and highlighting areas where the maps induce significant distortion of a given measure in a multi‐scale way. Unlike the majority of prior work, which focuses on discovering maps in the context of shape matching, our main focus is on evaluating, analysing and visualizing a given map, and the distortion(s) it introduces, in an efficient and intuitive way. We are motivated primarily by the fact that most existing metrics for map evaluation are quadratic and expensive to compute in practice, and that current map visualization techniques are suitable primarily for global map understanding, and typically do not highlight areas where the map fails to meet certain quality criteria in a multi‐scale way. We propose to address these challenges in a unified way by considering the functional representation of a map, and performing spectral analysis on this representation. In particular, we propose a simple multi‐scale method for map evaluation and visualization, which provides detailed multi‐scale information about the distortion induced by a map, which can be used alongside existing global visualization techniques.In this paper we propose a method for analyzing and visualizing individual maps between shapes, or collections of such maps. Our method is based on isolating and highlighting areas where the maps induce significant distortion of a given measure in a multi‐scale way. Unlike the majority of prior work which focuses on discovering maps in the context of shape matching, our main focus is on evaluating, analyzing and visualizing a given map, and the distortion(s) it introduces, in an efficient and intuitive way. We are motivated primarily by the fact that most existing metrics for map evaluation are quadratic and expensive to compute in practice, and that current map visualization techniques are suitable primarily for global map understanding, and typically do not highlight areas where the map fails to meet certain quality criteria in a multi‐scale way. We propose to address these challenges in a unified way by considering the functional representation of a map, and performing spectral analysis on this representation. In particular, we propose a simple multi‐scale method for map evaluation and visualization, which provides detailed multi‐scale information about the distortion induced by a map, which can be used alongside existing global visualization techniques.
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    Enhancing Bayesian Estimators for Removing Camera Shake
    (The Eurographics Association and Blackwell Publishing Ltd., 2013) Wang, C.; Yue, Y.; Dong, F.; Tao, Y.; Ma, X.; Clapworthy, G.; Ye, X.; Holly Rushmeier and Oliver Deussen
    The aim of removing camera shake is to estimate a sharp version x from a shaken image y when the blur kernel k is unknown. Recent research on this topic evolved through two paradigms called MAP(k) and MAP(x,k). MAP(k) only solves for k by marginalizing the image prior, while MAP(x,k) recovers both x and k by selecting the mode of the posterior distribution. This paper first systematically analyses the latent limitations of these two estimators through Bayesian analysis. We explain the reason why it is so difficult for image statistics to solve the previously reported MAP(x,k) failure. Then we show that the leading MAP(x,k) methods, which depend on efficient prediction of large step edges, are not robust to natural images due to the diversity of edges. MAP(k), although much more robust to diverse edges, is constrained by two factors: the prior variation over different images, and the ratio between image size and kernel size. To overcome these limitations, we introduce an inter‐scale prior prediction scheme and a principled mechanism for integrating the sharpening filter into MAP(k). Both qualitative results and extensive quantitative comparisons demonstrate that our algorithm outperforms state‐of‐the‐art methods.The aim of removing camera shake is to estimate a sharp version x from a shaken image y when the blur kernel k is unknown. Recent research on this topic evolved through two paradigms called MAP(k) and MAP(x,k). MAP(k) only solves for k by marginalizing the image prior, while MAP(x,k) recovers both x and k by selecting the mode of the posterior distribution. This paper first systematically analyzes the latent limitations of these two estimators through Bayesian analysis. We explain the reason why it is so difficult for image statistics to solve the previously reported MAP(x,k) failure. Then we show that the leading MAP(x,k) methods, which depend on efficient prediction of large step edges, are not robust to natural images due to the diversity of edges. MAP(k), although much more robust to diverse edges, is constrained by two factors: the prior variation over different images, and the ratio between image size and kernel size.
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    34th EUROGRAPHICS General Assembly
    (The Eurographics Association and Blackwell Publishing Ltd., 2013) Holly Rushmeier and Oliver Deussen
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    REPORT OF THE STATUTORY AUDITORS TO THE GENERAL MEETING OF THE MEMBERS OF EUROGRAPHICS ASSOCIATION GENEVA
    (The Eurographics Association and Blackwell Publishing Ltd., 2013) Holly Rushmeier and Oliver Deussen
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    A Collaborative Digital Pathology System for Multi‐Touch Mobile and Desktop Computing Platforms
    (The Eurographics Association and Blackwell Publishing Ltd., 2013) Jeong, W.; Schneider, J.; Hansen, A.; Lee, M.; Turney, S. G.; Faulkner‐Jones, B. E.; Hecht, J. L.; Najarian, R.; Yee, E.; Lichtman, J. W.; Pfister, H.; Holly Rushmeier and Oliver Deussen
    Collaborative slide image viewing systems are becoming increasingly important in pathology applications such as telepathology and E‐learning. Despite rapid advances in computing and imaging technology, current digital pathology systems have limited performance with respect to remote viewing of whole slide images on desktop or mobile computing devices. In this paper we present a novel digital pathology client–server system that supports collaborative viewing of multi‐plane whole slide images over standard networks using multi‐touch‐enabled clients. Our system is built upon a standard HTTP web server and a MySQL database to allow multiple clients to exchange image and metadata concurrently. We introduce a domain‐specific image‐stack compression method that leverages real‐time hardware decoding on mobile devices. It adaptively encodes image stacks in a decorrelated colour space to achieve extremely low bitrates (0.8 bpp) with very low loss of image quality. We evaluate the image quality of our compression method and the performance of our system for diagnosis with an in‐depth user study.Collaborative slide image viewing systems are becoming increasingly important in pathology applications such as telepathology and E‐learning. Despite rapid advances in computing and imaging technology, current digital pathology systems have limited performance with respect to remote viewing of whole slide images on desktop or mobile computing devices. In this paper we present a novel digital pathology client‐server systems that supports collaborative viewing of multi‐plane whole slide images over standard networks using multi‐touch enabled clients. Our system is built upon a standard HTTP web server and a MySQL database to allow multiple clients to exchange image and metadata concurrently.