Browsing by Author "Steinberger, Markus"

Now showing 1 - 5 of 5
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    CUDA and Applications to Task-based Programming
    (The Eurographics Association, 2021) Kenzel, Michael; Kerbl, Bernhard; Winter, Martin; Steinberger, Markus; O'Sullivan, Carol and Schmalstieg, Dieter
    Since its inception, the CUDA programming model has been continuously evolving. Because the CUDA toolkit aims to consistently expose cutting-edge capabilities for general-purpose compute jobs to its users, the added features in each new version reflect the rapid changes that we observe in GPU architectures. Over the years, the changes in hardware, growing scope of built-in functions and libraries, as well as an advancing C++ standard compliance have expanded the design choices when coding for CUDA, and significantly altered the directives to achieve peak performance. In this tutorial, we give a thorough introduction to the CUDA toolkit, demonstrate how a contemporary application can benefit from recently introduced features and how they can be applied to task-based GPU scheduling in particular. For instance, we will provide detailed examples of use cases for independent thread scheduling, cooperative groups, and the CUDA standard library, libcu++, which are certain to become an integral part of clean coding for CUDA in the near future. https://cuda-tutorial.github.io/
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    DONeRF: Towards Real-Time Rendering of Compact Neural Radiance Fields using Depth Oracle Networks
    (The Eurographics Association and John Wiley & Sons Ltd., 2021) Neff, Thomas; Stadlbauer, Pascal; Parger, Mathias; Kurz, Andreas; Mueller, Joerg H.; Chaitanya, Chakravarty R. Alla; Kaplanyan, Anton S.; Steinberger, Markus; Bousseau, Adrien and McGuire, Morgan
    The recent research explosion around implicit neural representations, such as NeRF, shows that there is immense potential for implicitly storing high-quality scene and lighting information in compact neural networks. However, one major limitation preventing the use of NeRF in real-time rendering applications is the prohibitive computational cost of excessive network evaluations along each view ray, requiring dozens of petaFLOPS. In this work, we bring compact neural representations closer to practical rendering of synthetic content in real-time applications, such as games and virtual reality. We show that the number of samples required for each view ray can be significantly reduced when samples are placed around surfaces in the scene without compromising image quality. To this end, we propose a depth oracle network that predicts ray sample locations for each view ray with a single network evaluation. We show that using a classification network around logarithmically discretized and spherically warped depth values is essential to encode surface locations rather than directly estimating depth. The combination of these techniques leads to DONeRF, our compact dual network design with a depth oracle network as its first step and a locally sampled shading network for ray accumulation. With DONeRF, we reduce the inference costs by up to 48x compared to NeRF when conditioning on available ground truth depth information. Compared to concurrent acceleration methods for raymarching-based neural representations, DONeRF does not require additional memory for explicit caching or acceleration structures, and can render interactively (20 frames per second) on a single GPU.
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    Hierarchical Rasterization of Curved Primitives for Vector Graphics Rendering on the GPU
    (The Eurographics Association and John Wiley & Sons Ltd., 2019) Dokter, Mark; Hladký, Jozef; Parger, Mathias; Schmalstieg, Dieter; Seidel, Hans-Peter; Steinberger, Markus; Alliez, Pierre and Pellacini, Fabio
    In this paper, we introduce the CPatch, a curved primitive that can be used to construct arbitrary vector graphics. A CPatch is a generalization of a 2D polygon: Any number of curves up to a cubic degree bound a primitive. We show that a CPatch can be rasterized efficiently in a hierarchical manner on the GPU, locally discarding irrelevant portions of the curves. Our rasterizer is fast and scalable, works on all patches in parallel, and does not require any approximations. We show a parallel implementation of our rasterizer, which naturally supports all kinds of color spaces, blending and super-sampling. Additionally, we show how vector graphics input can efficiently be converted to a CPatch representation, solving challenges like patch self-intersections and false inside-outside classification. Results indicate that our approach is faster than the state-of-the-art, more flexible and could potentially be implemented in hardware.
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    Tessellated Shading Streaming
    (The Eurographics Association and John Wiley & Sons Ltd., 2019) Hladky, Jozef; Seidel, Hans-Peter; Steinberger, Markus; Boubekeur, Tamy and Sen, Pradeep
    Presenting high-fidelity 3D content on compact portable devices with low computational power is challenging. Smartphones, tablets and head-mounted displays (HMDs) suffer from thermal and battery-life constraints and thus cannot match the render quality of desktop PCs and laptops. Streaming rendering enables to show high-quality content but can suffer from potentially high latency. We propose an approach to efficiently capture shading samples in object space and packing them into a texture. Streaming this texture to the client, we support temporal frame up-sampling with high fidelity, low latency and high mobility. We introduce two novel sample distribution strategies and a novel triangle representation in the shading atlas space. Since such a system requires dynamic parallelism, we propose an implementation exploiting the power of hardware-accelerated tessellation stages. Our approach allows fast de-coding and rendering of extrapolated views on a client device by using hardwareaccelerated interpolation between shading samples and a set of potentially visible geometry. A comparison to existing shading methods shows that our sample distributions allow better client shading quality than previous atlas streaming approaches and outperforms image-based methods in all relevant aspects.
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    A Variational Loop Shrinking Analogy for Handle and Tunnel Detection and Reeb Graph Construction on Surfaces
    (The Eurographics Association and John Wiley & Sons Ltd., 2023) Weinrauch, Alexander; Mlakar, Daniel; Seidel, Hans-Peter; Steinberger, Markus; Zayer, Rhaleb; Myszkowski, Karol; Niessner, Matthias
    The humble loop shrinking property played a central role in the inception of modern topology but it has been eclipsed by more abstract algebraic formalisms. This is particularly true in the context of detecting relevant non-contractible loops on surfaces where elaborate homological and/or graph theoretical constructs are favored in algorithmic solutions. In this work, we devise a variational analogy to the loop shrinking property and show that it yields a simple, intuitive, yet powerful solution allowing a streamlined treatment of the problem of handle and tunnel loop detection. Our formalization tracks the evolution of a diffusion front randomly initiated on a single location on the surface. Capitalizing on a diffuse interface representation combined with a set of rules for concurrent front interactions, we develop a dynamic data structure for tracking the evolution on the surface encoded as a sparse matrix which serves for performing both diffusion numerics and loop detection and acts as the workhorse of our fully parallel implementation. The substantiated results suggest our approach outperforms state of the art and robustly copes with highly detailed geometric models. As a byproduct, our approach can be used to construct Reeb graphs by diffusion thus avoiding commonly encountered issues when using Morse functions.