Browsing by Author "Zwicker, Matthias"

Now showing 1 - 5 of 5
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    EUROGRAPHICS 2017: State of the Art Reports Frontmatter
    (The Eurographics Association and John Wiley & Sons Ltd., 2017) Ostromoukov, Victor; Zwicker, Matthias;
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    Eurographics Symposium on Rendering 2017 - Experimental Ideas and Implementations: Frontmatter
    (Eurographics Association, 2017) Zwicker, Matthias; Sander, Pedro;
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    Eurographics Symposium on Rendering 2017: Frontmatter
    (Eurographics Association, 2017) Zwicker, Matthias; Sander, Pedro;
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    Real-time Monte Carlo Denoising with the Neural Bilateral Grid
    (The Eurographics Association, 2020) Meng, Xiaoxu; Zheng, Quan; Varshney, Amitabh; Singh, Gurprit; Zwicker, Matthias; Dachsbacher, Carsten and Pharr, Matt
    Real-time denoising for Monte Carlo rendering remains a critical challenge with regard to the demanding requirements of both high fidelity and low computation time. In this paper, we propose a novel and practical deep learning approach to robustly denoise Monte Carlo images rendered at sampling rates as low as a single sample per pixel (1-spp). This causes severe noise, and previous techniques strongly compromise final quality to maintain real-time denoising speed. We develop an efficient convolutional neural network architecture to learn to denoise noisy inputs in a data-dependent bilateral space. Our neural network learns to generate a guide image for first splatting noisy data into the grid, and then slicing it to read out the denoised data. To seamlessly integrate bilateral grids into our trainable denoising pipeline, we leverage a differentiable bilateral grid, called neural bilateral grid, which enables end-to-end training. In addition, we also show how we can further improve denoising quality using a hierarchy of multi-scale bilateral grids. Our experimental results demonstrate that this approach can robustly denoise 1-spp noisy input images at real-time frame rates (a few milliseconds per frame). At such low sampling rates, our approach outperforms state-of-the-art techniques based on kernel prediction networks both in terms of quality and speed, and it leads to significantly improved quality compared to the state-of-the-art feature regression technique.
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    A Survey on Gradient-Domain Rendering
    (The Eurographics Association and John Wiley & Sons Ltd., 2019) Hua, Binh-Son; Gruson, Adrien; Petitjean, Victor; Zwicker, Matthias; Nowrouzezahrai, Derek; Eisemann, Elmar; Hachisuka, Toshiya; Giachetti, Andrea and Rushmeyer, Holly
    Monte Carlo methods for physically-based light transport simulation are broadly adopted in the feature film production, animation and visual effects industries. These methods, however, often result in noisy images and have slow convergence. As such, improving the convergence of Monte Carlo rendering remains an important open problem. Gradient-domain light transport is a recent family of techniques that can accelerate Monte Carlo rendering by up to an order of magnitude, leveraging a gradient-based estimation and a reformulation of the rendering problem as an image reconstruction. This state of the art report comprehensively frames the fundamentals of gradient-domain rendering, as well as the pragmatic details behind practical gradient-domain uniand bidirectional path tracing and photon density estimation algorithms. Moreover, we discuss the various image reconstruction schemes that are crucial to accurate and stable gradient-domain rendering. Finally, we benchmark various gradient-domain techniques against the state-of-the-art in denoising methods before discussing open problems.