Browsing by Author "Parger, Mathias"

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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.