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JustineGiroux committed Dec 7, 2023
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<title>Towards a Perceptual Evaluation Framework for Lighting Estimation</title>
<meta name="description" content="This paper proposes a new lighting estimation focused IQA metric based on the human perception of lighting for multiuple tasks, materials, and domaines.">
<meta name="description" content="Progress in lighting estimation is tracked by computing existing image quality assessment (IQA) metrics on images from standard datasets. While this may appear to be a reasonable approach, we demonstrate that doing so does not correlate to human preference when the estimated lighting is used to relight a virtual scene into a real photograph. To study this, we design a controlled psychophysical experiment where human observers must choose their preference amongst rendered scenes lit using a set of lighting estimation algorithms selected from the recent literature, and use it to analyse how these algorithms perform according to human perception. Then, we demonstrate that none of the most popular IQA metrics from the literature, taken individually, correctly represent human perception. Finally, we show that by learning a combination of existing IQA metrics, we can more accurately represent human preference. This provides a new perceptual framework to help evaluate future lighting estimation algorithms.">
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<span style="font-size:24px"><a href='./assets/poster.pdf'>[Poster]</a></span><br>
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<center><h1>Video - ICCV 2023</h1></center>
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<center><h1>Citation</h1></center>
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<pre><code>@InProceedings{giroux2024,
<pre><code>@InProceedings{giroux2023,
author = {Giroux, Justine and Dastjerdi, Mohammad Reza Karimi and Hold-Geoffroy, Yannick and Vazquez-Corral, Javier and Lalonde, Jean-Fran\c{c}ois},
title = {Towards a Perceptual Evaluation Framework for Lighting Estimatio},
month = {June},
year = {2024}
title = {Towards a Perceptual Evaluation Framework for Lighting Estimation},
month = {December},
year = {2023}
}</code></pre>
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<div class="card mb-4 shadow-sm text-center">
<h3 class="text-muted">Acknowledgements </h3>
The name of this paper EverLight is a homage to the Critical Role’s The Legend of Vox Machina. This work was partially supported by NSERC grant ALLRP557208-20. We thank Sai Bi for his help with extending the dynamic range of the panoramas and everyone at UL who helped with proofreading.
This research was supported by Sentinel North and NSERC grant RGPIN 2020-04799. The authors thank all members of the lab for discussions and help with the paper, as well as all participants for taking part in our user study.
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