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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="DESCRIPTION META TAG">
<meta property="og:title" content="SOCIAL MEDIA TITLE TAG"/>
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<title>Inverse Rendering of Translucent Objects using Shape-adaptive Importance Sampling</title>
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<h2 class="title is-1 publication-title">
<!-- Academic Project Page</h2> -->
Inverse Rendering of Translucent Objects using Shape-adaptive Importance Sampling
</h2>
<div class="is-size-5 publication-authors">
<!-- Paper authors -->
<span class="author-block">
<a href="https://spock-the-wizard.github.io/" target="_blank">Jooeun Son</a>,</span>
<span class="author-block">
<a href="https://ycjung.info" target="_blank">Yucheol Jung</a>,</span>
<span class="author-block">
<a href="https://www.notion.so/KYUNGMINLEE-59d7020d4c0643b899693b8fd25b5d83" target="_blank">Kyungmin Lee</a>,</span>
<span class="author-block">
<!-- <a href="THIRD AUTHOR PERSONAL LINK" target="_blank"> -->
Soongjin Kim,
<!-- </a>,</span> -->
<span class="author-block">
<a href="https://sites.google.com/view/jooholee" target="_blank">Joo Ho Lee</a>,</span>
<span class="author-block">
<a href="https://cg.postech.ac.kr/leesy/" target="_blank">Seungyong Lee</a></span>
</div>
<div class="is-size-5 publication-authors">
<span class="author-block">POSTECH, Sogang University<br>Pacific Graphics 2024</span>
<!-- <span class="eql-cntrb"><small><br><sup>*</sup>Indicates Equal Contribution</small></span> -->
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<span>arXiv</span>
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<!-- Your image here -->
<img src="static/images/teaser.png" alt="Teaser Image"/>
<h3 class="subtitle has-text-centered">
Inverse rendering with shape-adaptive importance sampling.
(a-c) We jointly optimize the extinction coefficient and scattering albedo of a homo-geneous translucent object using the shape-BSSRDF model [Vicini 2019].
We address the challenges in differentiability posed by the importance sampling framework by approximating gradients using offset samples.
(d, e) Our method computes highly accurate gradients with low variance.
</h3>
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<!-- End teaser video -->
<!-- Paper abstract -->
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<h2 class="title is-3">Abstract</h2>
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<p>
Subsurface scattering is ubiquitous in organic materials and has been widely researched in computer graphics. Inverse rendering of subsurface scattering, however, is often constrained by the planar geometry assumption of traditional analytic Bidirectional Scattering Surface Reflectance Distribution Functions (BSSRDF). To address this issue, a shape-adaptive BSSRDF model has been proposed to render translucent objects on curved geometry with high accuracy. In this paper, we leverage this model to estimate parameters of subsurface scattering for inverse rendering. We compute the finite difference of the rendering equation for subsurface scattering and iteratively update material parameters. We demonstrate the performance of our shape-adaptive inverse rendering model by analyzing the estimation accuracy and comparing to inverse rendering with plane-based BSSRDF models and volumetric methods.
</p>
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<h2 class="title is-3">Results</h2>
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<img src="static/images/validation.png" alt="comparison with FD gradients"/>
<h3 class="subtitle has-text-centered">
First image description.
</h3>
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<h2 class="title is-3">Results</h2>
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<img src="static/images/validation.png" alt="comparison with FD gradients"/>
<h2 class="subtitle has-text-centered">
First image description.
</h2>
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<h2 class="title is-3">Results</h2>
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<div class="item carousel-image">
<img width="60%" class="carousel-image" src="static/images/validation.png" alt="comparison with FD gradients"/>
<h3 class="subtitle has-text-centered">
Validation of Gradients obtained with our method and finite differences.
</h3>
</div>
<div class="item carousel-image item-video2">
<img width="80%" class="carousel-image" id="video2" src="static/images/comp_deng.png" alt="comparison with FD gradients"/>
<h3 class="subtitle has-text-centered">
Comparison with Planar BSSRDF model [Deng 2022].
</h3>
</div>
<div class="item carousel-image" style="margin-top: -10px">
<img width="50%" class="carousel-image" id="video3" src="static/images/comp_prb.png" alt="comparison with FD gradients"/>
<h3 class="subtitle has-text-centered">
Comparison with Volumetric Method: Path Replay Backpropagation (PRB) [Vicini 2021].
<br>
</h3>
</div>
<div class="item">
<img width="80%" class="carousel-image" src="static/images/comp_psdrvol.png" alt="comparison with FD gradients"/>
<h3 class="subtitle has-text-centered">
Comparison with Volumetric method: PSDR-VOL [Zhang 2021]. <br>
Note that we disable gradient computation regarding geometric discontinuities.
</h3>
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</section>
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<h2 class="title is-3">Method</h2>
<!-- Your image here -->
<img style="margin-bottom: -60px;" src="static/images/method.png" alt="Teaser Image"/>
<h3 class="subtitle has-text-centered">
We analyzed challenges in differentiability of the shape-adaptive importance sampling framework due to 1) implicitly defined BSSRDFs and 2) non-invertibility of the sampling transform.
Our solution is to compute gradients via the difference of offset samples, which is essentially finite differences in the sample domain.
It is effective as it implicitly addresses boundary effects caused by parameter-dependent visibility discontinuities.
It is also efficient becauses it utilizes the correlation of paths compared to naive automatic differentiation.
</h3>
</div>
</div>
</section>
<!--BibTex citation -->
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<h2 class="title">BibTeX</h2>
<pre><code>Coming Soon</code></pre>
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