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  • 1.
    Cheslack-Postava, E.
    et al.
    Stanford University.
    Wang, R.
    UMass Amherst.
    Akerlund, O.
    Linköping University.
    Pellacini, F.
    Dartmouth College.
    Fast, realistic lighting and material design using nonlinear cut approximation2008In: ACM Transactions on Graphics, ISSN 0730-0301, E-ISSN 1557-7368, Vol. 27, no 5Article in journal (Refereed)
    Abstract [en]

    We present an efficient computational algorithm for functions represented by a nonlinear piecewise constant approximation called cuts. Our main contribution is a single traversal algorithm for merging cuts that allows for arbitrary pointwise computation, such as addition, multiplication, linear interpolation, and multi-product integration. A theoretical error bound of this approach can be proved using a statistical interpretation of cuts. Our algorithm extends naturally to computation with many cuts and maps easily to modern GPUs, leading to significant advantages over existing methods based on wavelet approximation. We apply this technique to the problem of realistic lighting and material design under complex illumination with arbitrary BRDFs. Our system smoothly integrates all-frequency relighting of shadows and reflections with dynamic per-pixel shading effects, such as bump mapping and spatially varying BRDFs. This combination of capabilities is typically missing in current systems. We represent illumination and precomputed visibility as nonlinear sparse vectors, we then use our cut merging algorithm to simultaneously interpolate visibility cuts at each pixel, and compute the triple product integral of the illumination, interpolated visibility, and dynamic BRDF samples. Finally, we present a two-pass, data-driven approach that exploits pilot visibility samples to optimize the construction of the light tree, leading to more efficient cuts and reduced datasets. © 2008 ACM.

  • 2.
    Eilertsen, Gabriel
    et al.
    Linköping University, Department of Science and Technology, Media and Information Technology. Linköping University, Faculty of Science & Engineering.
    Kronander, Joel
    Linköping University, Department of Science and Technology. Linköping University, Faculty of Science & Engineering.
    Denes, Gyorgy
    University of Cambridge, England.
    Mantiuk, Rafal K.
    University of Cambridge, England.
    Unger, Jonas
    Linköping University, Department of Science and Technology, Media and Information Technology. Linköping University, Faculty of Science & Engineering.
    HDR image reconstruction from a single exposure using deep CNNs2017In: ACM Transactions on Graphics, ISSN 0730-0301, E-ISSN 1557-7368, Vol. 36, no 6, article id 178Article in journal (Refereed)
    Abstract [en]

    Camera sensors can only capture a limited range of luminance simultaneously, and in order to create high dynamic range (HDR) images a set of different exposures are typically combined. In this paper we address the problem of predicting information that have been lost in saturated image areas, in order to enable HDR reconstruction from a single exposure. We show that this problem is well-suited for deep learning algorithms, and propose a deep convolutional neural network (CNN) that is specifically designed taking into account the challenges in predicting HDR values. To train the CNN we gather a large dataset of HDR images, which we augment by simulating sensor saturation for a range of cameras. To further boost robustness, we pre-train the CNN on a simulated HDR dataset created from a subset of the MIT Places database. We demonstrate that our approach can reconstruct high-resolution visually convincing HDR results in a wide range of situations, and that it generalizes well to reconstruction of images captured with arbitrary and low-end cameras that use unknown camera response functions and post-processing. Furthermore, we compare to existing methods for HDR expansion, and show high quality results also for image based lighting. Finally, we evaluate the results in a subjective experiment performed on an HDR display. This shows that the reconstructed HDR images are visually convincing, with large improvements as compared to existing methods.

  • 3.
    Eilertsen, Gabriel
    et al.
    Linköping University, Department of Science and Technology, Media and Information Technology. Linköping University, Faculty of Science & Engineering.
    Mantiuk, Rafal
    University of Cambridge.
    Unger, Jonas
    Linköping University, Department of Science and Technology, Media and Information Technology. Linköping University, Faculty of Science & Engineering.
    Real-time noise-aware tone mapping2015In: ACM Transactions on Graphics, ISSN 0730-0301, E-ISSN 1557-7368, ISSN 0730-0301, Vol. 34, no 6, p. 198:1-198:15, article id 198Article in journal (Refereed)
    Abstract [en]

    Real-time high quality video tone mapping is needed for manyapplications, such as digital viewfinders in cameras, displayalgorithms which adapt to ambient light, in-camera processing,rendering engines for video games and video post-processing. We propose a viable solution for these applications by designing a videotone-mapping operator that controls the visibility of the noise,adapts to display and viewing environment, minimizes contrastdistortions, preserves or enhances image details, and can be run inreal-time on an incoming sequence without any preprocessing. To ourknowledge, no existing solution offers all these features. Our novelcontributions are: a fast procedure for computing local display-adaptivetone-curves which minimize contrast distortions, a fast method for detailenhancement free from ringing artifacts, and an integrated videotone-mapping solution combining all the above features.

  • 4.
    Hermosilla, Pedro
    et al.
    Ulm University, Germany.
    Ritschel, Tobias
    University College London, United Kingdom.
    Vazquez, Pere-Pau
    Universitat Politècnica de Catalunya, Spain.
    Vinacua, Àlvar
    Universitat Politècnica de Catalunya, Spain.
    Ropinski, Timo
    Ulm University, Germany.
    Monte Carlo Convolution for Learning on Non-Uniformly Sampled Point Clouds2018In: ACM Transactions on Graphics, ISSN 0730-0301, E-ISSN 1557-7368, Vol. 37, no 6Article in journal (Refereed)
    Abstract [en]

    Deep learning systems extensively use convolution operations to process input data. Though convolution is clearly defined for structured data such as 2D images or 3D volumes, this is not true for other data types such as sparse point clouds. Previous techniques have developed approximations to convolutions for restricted conditions. Unfortunately, their applicability is limited and cannot be used for general point clouds. We propose an efficient and effective method to learn convolutions for non-uniformly sampled point clouds, as they are obtained with modern acquisition techniques. Learning is enabled by four key novelties: first, representing the convolution kernel itself as a multilayer perceptron; second, phrasing convolution as a Monte Carlo integration problem, third, using this notion to combine information from multiple samplings at different levels; and fourth using Poisson disk sampling as a scalable means of hierarchical point cloud learning. The key idea across all these contributions is to guarantee adequate consideration of the underlying non-uniform sample distribution function from a Monte Carlo perspective. To make the proposed concepts applicable to real-world tasks, we furthermore propose an efficient implementation which significantly reduces the GPU memory required during the training process. By employing our method in hierarchical network architectures we can outperform most of the state-of-the-art networks on established point cloud segmentation, classification and normal estimation benchmarks. Furthermore, in contrast to most existing approaches, we also demonstrate the robustness of our method with respect to sampling variations, even when training with uniformly sampled data only. To support the direct application of these concepts, we provide a ready-to-use TensorFlow implementation of these layers at https://github.com/viscom-ulm/MCCNN.

  • 5.
    Houston, Ben
    et al.
    Exocortex Technologies, Frantic Films.
    Nielson, Michael B.
    University of Århus.
    Batty, Christopher
    University of British Columbia, Frantic Films.
    Nilsson, Ola
    Linköping University, Department of Science and Technology. Linköping University, The Institute of Technology.
    Museth, Ken
    Linköping University, Department of Science and Technology, Digital Media. Linköping University, The Institute of Technology.
    Hierarchical RLE level set: A compact and versatile deformable surface representation2006In: ACM Transactions on Graphics, ISSN 0730-0301, E-ISSN 1557-7368, Vol. 25, no 1, p. 151-175Article in journal (Refereed)
    Abstract [en]

    This article introduces the Hierarchical Run-Length Encoded (H-RLE) Level Set data structure. This novel data structure combines the best features of the DT-Grid ( of Nielsen and Museth [ 2004]) and the RLE Sparse Level Set ( of Houston et al. [ 2004]) to provide both optimal efficiency and extreme versatility. In brief, the H- RLE level set employs an RLE in a dimensionally recursive fashion. The RLE scheme allows the compact storage of sequential nonnarrowband regions while the dimensionally recursive encoding along each axis efficiently compacts nonnarrowband planes and volumes. Consequently, this new structure can store and process level sets with effective voxel resolutions exceeding 5000 x 3000 x 3000 ( 45 billion voxels) on commodity PCs with only 1 GB of memory. This article, besides introducing the H- RLE level set data structure and its efficient core algorithms, also describes numerous applications that have benefited from our use of this structure: our unified implicit object representation, efficient and robust mesh to level set conversion, rapid ray tracing, level set metamorphosis, collision detection, and fully sparse fluid simulation ( including RLE vector and matrix representations.) Our comparisons of the popular octree level set and Peng level set structures to the H- RLE level set indicate that the latter is superior in both narrowband sequential access speed and overall memory usage.

  • 6.
    Löw, Joakim
    et al.
    Linköping University, Department of Science and Technology, Media and Information Technology. Linköping University, The Institute of Technology.
    Kronander, Joel
    Linköping University, Department of Science and Technology, Media and Information Technology. Linköping University, The Institute of Technology.
    Ynnerman, Anders
    Linköping University, Department of Science and Technology, Media and Information Technology. Linköping University, The Institute of Technology.
    Unger, Jonas
    Linköping University, Department of Science and Technology, Media and Information Technology. Linköping University, The Institute of Technology.
    BRDF Models for Accurate and Efficient Rendering of Glossy Surfaces2012In: ACM Transactions on Graphics, ISSN 0730-0301, E-ISSN 1557-7368, Vol. 31, no 1Article in journal (Refereed)
    Abstract [en]

    This article presents two new parametric models of the Bidirectional Reflectance Distribution Function (BRDF), one inspired by the Rayleigh-Rice theory for light scattering from optically smooth surfaces, and one inspired by micro-facet theory. The models represent scattering from a wide range of glossy surface types with high accuracy. In particular, they enable representation of types of surface scattering which previous parametric models have had trouble modeling accurately. In a study of the scattering behavior of measured reflectance data, we investigate what key properties are needed for a model to accurately represent scattering from glossy surfaces. We investigate different parametrizations and how well they match the behavior of measured BRDFs. We also examine the scattering curves which are represented in parametric models by different distribution functions. Based on the insights gained from the study, the new models are designed to provide accurate fittings to the measured data. Importance sampling schemes are developed for the new models, enabling direct use in existing production pipelines. In the resulting renderings we show that the visual quality achieved by the models matches that of the measured data.

  • 7.
    Miandji, Ehsan
    et al.
    Linköping University, Department of Science and Technology, Media and Information Technology. Linköping University, Faculty of Science & Engineering.
    Hajisharif, Saghi
    Linköping University, Department of Science and Technology, Media and Information Technology. Linköping University, Faculty of Science & Engineering.
    Unger, Jonas
    Linköping University, Department of Science and Technology, Media and Information Technology. Linköping University, Faculty of Science & Engineering.
    A Unified Framework for Compression and Compressed Sensing of Light Fields and Light Field Videos2019In: ACM Transactions on Graphics, ISSN 0730-0301, E-ISSN 1557-7368, Vol. 38, no 3, p. 1-18, article id 23Article in journal (Refereed)
    Abstract [en]

    In this article we present a novel dictionary learning framework designed for compression and sampling of light fields and light field videos. Unlike previous methods, where a single dictionary with one-dimensional atoms is learned, we propose to train a Multidimensional Dictionary Ensemble (MDE). It is shown that learning an ensemble in the native dimensionality of the data promotes sparsity, hence increasing the compression ratio and sampling efficiency. To make maximum use of correlations within the light field data sets, we also introduce a novel nonlocal pre-clustering approach that constructs an Aggregate MDE (AMDE). The pre-clustering not only improves the image quality but also reduces the training time by an order of magnitude in most cases. The decoding algorithm supports efficient local reconstruction of the compressed data, which enables efficient real-time playback of high-resolution light field videos. Moreover, we discuss the application of AMDE for compressed sensing. A theoretical analysis is presented that indicates the required conditions for exact recovery of point-sampled light fields that are sparse under AMDE. The analysis provides guidelines for designing efficient compressive light field cameras. We use various synthetic and natural light field and light field video data sets to demonstrate the utility of our approach in comparison with the state-of-the-art learning-based dictionaries, as well as established analytical dictionaries.

  • 8.
    Museth, Ken
    et al.
    Linköping University, Department of Science and Technology, Digital Media. Linköping University, The Institute of Technology.
    Breen, David
    Whitaker, Ross
    Barr, A
    Level Set Surface Editing Operators2002In: ACM Transactions on Graphics, ISSN 0730-0301, E-ISSN 1557-7368, Vol. 21, no 3, p. 330-338Article in journal (Refereed)
  • 9.
    Nielsen, Michael B.
    et al.
    University of Århus, Denmark.
    Nilsson, Ola
    Linköping University, Department of Science and Technology. Linköping University, The Institute of Technology.
    Söderström, Andreas
    Linköping University, Department of Science and Technology, Digital Media. Linköping University, The Institute of Technology.
    Museth, Ken
    Linköping University, Department of Science and Technology, Digital Media. Linköping University, The Institute of Technology.
    Out-of-core and compressed level set methods2007In: ACM Transactions on Graphics, ISSN 0730-0301, E-ISSN 1557-7368, Vol. 26, no 4, p. 16-Article in journal (Refereed)
    Abstract [en]

    This article presents a generic framework for the representation and deformation of level set surfaces at extreme resolutions. The framework is composed of two modules that each utilize optimized and application specific algorithms: 1) A fast out-of-core data management scheme that allows for resolutions of the deforming geometry limited only by the available disk space as opposed to memory, and 2) compact and fast compression strategies that reduce both offline storage requirements and online memory footprints during simulation. Out-of-core and compression techniques have been applied to a wide range of computer graphics problems in recent years, but this article is the first to apply it in the context of level set and fluid simulations. Our framework is generic and flexible in the sense that the two modules can transparently be integrated, separately or in any combination, into existing level set and fluid simulation software based on recently proposed narrow band data structures like the DT-Grid of Nielsen and Museth [2006] and the H-RLE of Houston et al. [2006]. The framework can be applied to narrow band signed distances, fluid velocities, scalar fields, particle properties as well as standard graphics attributes like colors, texture coordinates, normals, displacements etc. In fact, our framework is applicable to a large body of computer graphics problems that involve sequential or random access to very large co-dimension one (level set) and zero (e.g. fluid) data sets. We demonstrate this with several applications, including fluid simulations interacting with large boundaries (? 15003), surface deformations (? 20483), the solution of partial differential equations on large surfaces (˜40963) and mesh-to-level set scan conversions of resolutions up to ? 350003 (7 billion voxels in the narrow band). Our out-of-core framework is shown to be several times faster than current state-of-the-art level set data structures relying on OS paging. In particular we show sustained throughput (grid points/sec) for gigabyte sized level sets as high as 65% of state-of-the-art throughput for in-core simulations. We also demonstrate that our compression techniques out-perform state-of-the-art compression algorithms for narrow bands. © 2007 ACM.

  • 10.
    Söderström, Andreas
    et al.
    Linköping University, Department of Science and Technology. Linköping University, The Institute of Technology.
    Karlsson, Matts
    Linköping University, Department of Management and Engineering, Applied Thermodynamics and Fluid Mechanics . Linköping University, The Institute of Technology.
    Museth, Ken
    Linköping University, Department of Science and Technology. Linköping University, The Institute of Technology.
    A PML Based Non-Reflective Boundary for Free Surface Fluid Animation2010In: ACM Transactions on Graphics, ISSN 0730-0301, E-ISSN 1557-7368, Vol. 29, no 5, p. 136-Article in journal (Refereed)
    Abstract [en]

    This article presents a novel non-reflective boundary condition for the free surface incompressible Euler and Navier-Stokes equations. Boundaries of this type are very useful when, for example, simulating water flow around a ship moving over a wide ocean. Normally waves generated by the ship will reflect off of the boundaries of the simulation domain and as these reflected waves returns towards the ship they will cause undesired interference patterns.By employing a Perfectly Matched Layer (PML) approach we have derived a boundary condition that absorbs incoming waves and thus efficiently prevents these undesired wave reflections. To solve the resulting boundary equations we present a fast and stable algorithm based on the Stable Fluids approach. Through numerical experiments we then show that our boundaries are significantly more effective than simpler reflection preventing techniques. We also provide a thorough analysis of the parameters involved in our boundary formulation and show how they effect wave absorption efficiency.

  • 11.
    Wenger, Andreas
    et al.
    University of Southern California Institute for Creative Technologies.
    Gardner, Andrew
    University of Southern California Institute for Creative Technologies.
    Tchou, Chris
    University of Southern California Institute for Creative Technologies.
    Unger, Jonas
    University of Southern California .
    Hawkins, Tim
    University of Southern California Institute for Creative Technologies.
    Debevec, Paul
    University of Southern California Institute for Creative Technologies.
    Performance Relighting and Reflectance Transformation with Time-Multiplexed Illumination2005In: ACM Transactions on Graphics, ISSN 0730-0301, E-ISSN 1557-7368, Vol. 24, no 3Article in journal (Refereed)
    Abstract [en]

    We present a technique for capturing an actor’s live-action performance in such a way that the lighting and reflectance of the actor can be designed and modified in postproduction. Our approach is to illuminate the subject with a sequence of time-multiplexed basis lighting conditions, and to record these conditions with a highspeed video camera so that many conditions are recorded in the span of the desired output frame interval. We investigate several lighting bases for representing the sphere of incident illumination using a set of discrete LED light sources, and we estimate and compensate for subject motion using optical flow and image warping based on a set of tracking frames inserted into the lighting basis. To composite the illuminated performance into a new background, we include a time-multiplexed matte within the basis. We also show that the acquired data enables time-varying surface normals, albedo, and ambient occlusion to be estimated, which can be used to transform the actor’s reflectance to produce both subtle and stylistic effects.

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