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<!DOCTYPE html>
<html>
<head>
<meta charset="utf-8">
<!-- Meta tags for social media banners, these should be filled in appropriatly as they are your "business card" -->
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<meta name="description" content="A sparse non-parametric BRDF model">
<meta property="og:title" content="A sparse non-parametric BRDF model."/>
<meta property="og:description" content="A sparse BRDF model which learns a BRDF manifold to represent BRDFs."/>
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<meta name="keywords" content="BRDF, Dictionary Learning, Sparse Representation">
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<title>A Sparse non-parametric BRDF model</title>
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<h1 class="xtitle is-1 publication-title">A Sparse non-parametric BRDF model</h1>
<div class="is-size-5 publication-authors">
<!-- Paper authors -->
<a href="https://liu.se/en/employee/tanto11" target="_blank"class="external-link "><span class="author-block"> Tanaboon Tongbuasirilai </span></a>
<a href="https://liu.se/en/employee/jonun48" target="_blank"class="external-link "><span class="author-block"> Jonas Unger</span> </a>
<a href="https://people.rennes.inria.fr/Christine.Guillemot/" target="_blank"class="external-link "><span class="author-block"> Christine Guillemot<sup>*</sup> </span></a>
<a href="https://liu.se/en/employee/miaeh27" target="_blank"class="external-link "><span class="author-block"> Ehsan Miandji </span></a>
</div>
<div class="is-size-5 publication-authors">
<span class="author-block">Linköping University, Sweden <br>
<sup>*</sup>Inria Rennes, France <br>
Transactions on Graphics (presented at SIGGRAPH 2023) </span>
</div>
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<!-- Link to the paper -->
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<a href="static/pdfs/TOG2021_non_parametric_sparse_brdf_final.pdf" target="_blank"
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<span>Paper</span>
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<span>Code</span>
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<h2 class="title is-3">Abstract</h2>
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<p>
This paper presents a novel sparse non-parametric Bidirectional Reflectance Distribution Function (BRDF) model derived using a machine learning approach to represent the space of possible BRDFs using a set of multidimensional sub-spaces, or dictionaries. By training the dictionaries under a sparsity constraint, the model guarantees high-quality representations with minimal storage requirements and an inherent clustering of the BDRF-space. The model can be trained once and then reused to represent a wide variety of measured BRDFs. Moreover, the proposed method is flexible to incorporate new unobserved data sets, parameterizations, and transformations. In addition, we show that any two, or more, BRDFs can be smoothly interpolated in the coefficient space of the model rather than the significantly higher-dimensional BRDF space. The proposed sparse BRDF model is evaluated using the MERL, DTU, and RGL-EPFL BRDF databases. Experimental results show that the proposed approach results in about 9.75dB higher signal-to-noise ratio on average for rendered images as compared to current state-of-the-art models.
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<img src="static/images/Pipeline-poster.png" alt="Our proposed pipeline."/>
<h2 class="subtitle has-text-centered">
Our proposed pipeline.
</h2>
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<div class="item">
<!-- Your image here -->
<img src="static/images/Interpolation2.png" alt="our BRDF interpolation."/>
<h2 class="subtitle has-text-centered">
Examples of our BRDF interpolation.
</h2>
</div>
<div class="item">
<!-- Your image here -->
<img src="static/images/coefficient_increased.png" alt="increased coefficients."/>
<h2 class="subtitle has-text-centered">
When the number of coefficients is increased.
</h2>
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type="video/mp4">
</video>
</center>
<h2 class="subtitle has-text-centered">
Comparisons between our model and Bagher et al.'s model.
</h2>
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<!-- Your video here -->
<source src="static/videos/paperstog_110.mp4"
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Fast-forward presentation.
</h2>
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<h2 class="title">Poster</h2>
<center>
<iframe src="static/pdfs/Siggraph2023-poster-non-parametric-BRDF.pdf" width="50%" height="550"></iframe>
</center>
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<h2 class="title">BibTeX</h2>
<pre><code>
@article{TongbuasirilaiTOG2022,
author = {Tongbuasirilai, Tanaboon and Unger, Jonas and Guillemot, Christine and Miandji, Ehsan},
title = {A Sparse Non-Parametric BRDF Model},
year = {2022},
issue_date = {October 2022},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
volume = {41},
number = {5},
issn = {0730-0301},
url = {https://doi.org/10.1145/3533427},
doi = {10.1145/3533427},
journal = {ACM Trans. Graph.},
month = {oct},
articleno = {181},
numpages = {18},
keywords = {BRDF interpolation, reflectance and shading models, machine learning, Rendering, non-parametric BRDF model, dictionary learning}
}
</code></pre>
</div>
</section>
<!--End BibTex citation -->
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