Browsing by Author "Salvi, Marco"

Now showing 1 - 3 of 3
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    Neural Temporal Adaptive Sampling and Denoising
    (The Eurographics Association and John Wiley & Sons Ltd., 2020) Hasselgren, Jon; Munkberg, Jacob; Salvi, Marco; Patney, Anjul; Lefohn, Aaron; Panozzo, Daniele and Assarsson, Ulf
    Despite recent advances in Monte Carlo path tracing at interactive rates, denoised image sequences generated with few samples per-pixel often yield temporally unstable results and loss of high-frequency details. We present a novel adaptive rendering method that increases temporal stability and image fidelity of low sample count path tracing by distributing samples via spatio-temporal joint optimization of sampling and denoising. Adding temporal optimization to the sample predictor enables it to learn spatio-temporal sampling strategies such as placing more samples in disoccluded regions, tracking specular highlights, etc; adding temporal feedback to the denoiser boosts the effective input sample count and increases temporal stability. The temporal approach also allows us to remove the initial uniform sampling step typically present in adaptive sampling algorithms. The sample predictor and denoiser are deep neural networks that we co-train end-to-end over multiple consecutive frames. Our approach is scalable, allowing trade-off between quality and performance, and runs at near real-time rates while achieving significantly better image quality and temporal stability than previous methods.
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    A Survey of Temporal Antialiasing Techniques
    (The Eurographics Association and John Wiley & Sons Ltd., 2020) Yang, Lei; Liu, Shiqiu; Salvi, Marco; Mantiuk, Rafal and Sundstedt, Veronica
    Temporal Antialiasing (TAA), formally defined as temporally-amortized supersampling, is the most widely used antialiasing technique in today's real-time renderers and game engines. This survey provides a systematic overview of this technique. We first review the history of TAA, its development path and related work. We then identify the two main sub-components of TAA, sample accumulation and history validation, and discuss algorithmic and implementation options. As temporal upsampling is becoming increasingly relevant to today's game engines, we propose an extension of our TAA formulation to cover a variety of temporal upsampling techniques. Despite the popularity of TAA, there are still significant unresolved technical challenges that affect image quality in many scenarios. We provide an in-depth analysis of these challenges, and review existing techniques for improvements. Finally, we summarize popular algorithms and topics that are closely related to TAA. We believe the rapid advances in those areas may either benefit from or feedback into TAA research and development.
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    Temporally Dense Ray Tracing
    (The Eurographics Association, 2019) Andersson, Pontus; Nilsson, Jim; Salvi, Marco; Spjut, Josef; Akenine-Möller, Tomas; Steinberger, Markus and Foley, Tim
    We present a technique for real-time ray tracing with the goal of reaching 240 frames per second or more. The core idea is to trade spatial resolution for faster temporal updates in such a way that the display and human visual system aid in integrating high-quality images. We use a combination of frameless and interleaved rendering concepts together with ideas from temporal antialiasing algorithms and novel building blocks-the major one being adaptive selection of pixel orderings within tiles, which reduces spatiotemporal aliasing significantly. The image quality is verified with a user study. Our work can be used for esports or any kind of rendering where higher frame rates are needed.