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Now showing 1 - 4 of 4
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    Scalable Remote Rendering with Depth and Motion-flow Augmented Streaming
    (The Eurographics Association and Blackwell Publishing Ltd., 2011) Paja, Dawid; Herzog, Robert; Eisemann, Elmar; Myszkowski, Karol; Seidel, Hans-Peter; M. Chen and O. Deussen
    In this paper, we focus on efficient compression and streaming of frames rendered from a dynamic 3D model. Remote rendering and on-the-fly streaming become increasingly attractive for interactive applications. Data is kept confidential and only images are sent to the client. Even if the client's hardware resources are modest, the user can interact with state-of-the-art rendering applications executed on the server. Our solution focuses on augmented video information, e.g., by depth, which is key to increase robustness with respect to data loss, image reconstruction, and is an important feature for stereo vision and other client-side applications. Two major challenges arise in such a setup. First, the server workload has to be controlled to support many clients, second the data transfer needs to be efficient. Consequently, our contributions are twofold. First, we reduce the server-based computations by making use of sparse sampling and temporal consistency to avoid expensive pixel evaluations. Second, our data-transfer solution takes limited bandwidths into account, is robust to information loss, and compression and decompression are efficient enough to support real-time interaction. Our key insight is to tailor our method explicitly for rendered 3D content and shift some computations on client GPUs, to better balance the server/client workload. Our framework is progressive, scalable, and allows us to stream augmented high-resolution (e.g., HDready) frames with small bandwidth on standard hardware.
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    A Survey on Temporal Coherence Methods in Real-Time Rendering
    (The Eurographics Association, 2011) Scherzer, Daniel; Yang, Lei; Mattausch, Oliver; Nehab, Diego; Sander, Pedro V.; Wimmer, Michael; Eisemann, Elmar; N. John and B. Wyvill
    Nowadays, there is a strong trend towards rendering to higher-resolution displays and at high frame rates. This development aims at delivering more detail and better accuracy, but it also comes at a significant cost. Although graphics cards continue to evolve with an ever-increasing amount of computational power, the processing gain is counteracted to a high degree by increasingly complex and sophisticated pixel computations. For real-time applications, the direct consequence is that image resolution and temporal resolution are often the first candidates to bow to the performance constraints (e.g., although full HD is possible, PS3 and XBox often render at lower resolutions). In order to achieve high-quality rendering at a lower cost, one can exploit temporal coherence (TC). The underlying observation is that a higher resolution and frame rate do not necessarily imply a much higher workload, but a larger amount of redundancy and a higher potential for amortizing rendering over several frames. In this state-of-the-art report, we investigate methods that make use of this principle and provide practical and theoretical advice on how to exploit temporal coherence for performance optimization. These methods not only allow incorporating more computationally intensive shading effects into many existing applications, but also offer exciting opportunities for extending high-end graphics applications to lower-spec consumer-level hardware. To this end, we first introduce the notion and main concepts of TC, including an overview of historical methods. We then describe a key data structure, the so-called reprojection cache, with several supporting algorithms that facilitate reusing shading information from previous frames, and finally illustrated its usefulness in various applications.
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    Learning Line Features in 3D Geometry
    (The Eurographics Association and Blackwell Publishing Ltd., 2011) Sunkel, Martin; Jansen, Silke; Wand, Michael; Eisemann, Elmar; Seidel, Hans-Peter; M. Chen and O. Deussen
    Feature detection in geometric datasets is a fundamental tool for solving shape matching problems such as partial symmetry detection. Traditional techniques usually employ a priori models such as crease lines that are unspecific to the actual application. Our paper examines the idea of learning geometric features. We introduce a formal model for a class of linear feature constellations based on a Markov chain model and propose a novel, efficient algorithm for detecting a large number of features simultaneously. After a short user-guided training stage, in which one or a few example lines are sketched directly onto the input data, our algorithm automatically finds all pieces of geometry similar to the marked areas. In particular, the algorithm is able recognize larger classes of semantically similar but geometrically varying features, which is very difficult using unsupervised techniques. In a number of experiments, we apply our technique to point cloud data from 3D scanners. The algorithm is able to detect features with very low rates of false positives and negatives and to recognize broader classes of similar geometry (such as "windows" in a building scan) even from few training examples, thereby significantly improving over previous unsupervised techniques.
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    Separable Approximation of Ambient Occlusion
    (The Eurographics Association, 2011) Huang, Jing; Boubekeur, Tamy; Ritschel, Tobias; Holländer, Matthias; Eisemann, Elmar; N. Avis and S. Lefebvre
    Ambient occlusion (AO) provides an effective approximation to global illumination that enjoys widespread use amongst practitioners. In this paper, we present a fast easy-to-implement separable approximation to screen space ambient occlusion. Computing occlusion first along a single direction and then transporting this occlusion into a second pass that is stochastically evaluating the final shading based on the AO estimates proves extremely efficient. Combined with interleaved sampling and geometry-aware blur, visually convincing results close to a non-separable occlusion can be obtained at much higher performance.