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<!DOCTYPE html>
<html>
<head>
<meta charset="utf-8">
<meta name="description"
content="TROSD: A new RGB-D dataset for transparent and reflective object segmentation in practice">
<meta name="keywords" content="Segmentation, Transparent and Reflective Object">
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<title>TROSD: A new RGB-D dataset for transparent and reflective object segmentation in practice</title>
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<section class="hero">
<div class="hero-body">
<div class="container is-max-desktop">
<div class="columns is-centered">
<div class="column has-text-centered">
<h1 class="title is-1 publication-title">TROSD: A New RGB-D Dataset for Transparent and Reflective Object Segmentation in Practice</h1>
<h3 class="title is-4 conference-authors"><a target="_blank" href="https://ieee-cas.org/publication/tcsvt">TCSVT 2023</a></h3>
<div class="is-size-5 publication-authors">
<span class="author-block">
<a href="https://provemj.github.io/">Tianyu Sun</a><sup>1</sup>,</span>
<span class="author-block">
Guodong Zhang<sup>1</sup>,</span>
<span class="author-block">
<a href="https://www.sigs.tsinghua.edu.cn/ywm_en/main.htm">Wenming Yang</a><sup>1</sup>,
</span>
<span class="author-block">
<a href="https://www.ucl.ac.uk/statistics/people/jinghaoxue">Jing-hao Xue</a><sup>2</sup>,
</span>
<span class="author-block">
<a href="https://www.ee.tsinghua.edu.cn/en/info/1064/1275.htm">Guijin Wang</a><sup>1</sup>
</span>
</div>
<div class="is-size-5 publication-authors">
<span class="author-block"><sup>1</sup>Tsinghua University,</span>
<span class="author-block"><sup>2</sup>University College London</span>
</div>
<div class="column has-text-centered">
<div class="publication-links">
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class="external-link button is-normal is-rounded is-dark">
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</span>
<span>Paper</span>
</a>
</span>
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<a href="https://codeocean.com/capsule/8854419/tree/v1"
class="external-link button is-normal is-rounded is-dark">
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<i class="fab fa-github"></i>
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<span>Code</span>
</a>
</span>
<!-- Dataset Link. -->
<span class="link-block">
<a href="http://www.tsinghua-ieit.com/trosd"
class="external-link button is-normal is-rounded is-dark">
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</span>
<span>Data</span>
</a>
</div>
</div>
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</div>
</div>
</div>
</section>
<section class="hero teaser">
<div class="container is-max-desktop">
<div class="hero-body">
<div style="text-align: center;">
<img src="./static/images/pipeline.jpg" class="interpolation-image" style="width: 100%; height: auto;"/>
</div>
<h2 class="subtitle has-text-centered">
<span class="dnerf">TROSNet</span> segments transparent and
reflective objects from the scene with corresponding RGB and depth input.
</h2>
</div>
</div>
</section>
<section class="section">
<div class="container is-max-desktop">
<!-- Abstract. -->
<div class="columns is-centered has-text-centered">
<div class="column is-four-fifths">
<h2 class="title is-3">Abstract</h2>
<div class="content has-text-justified">
<p>
Transparent and reflective objects are omnipresent in our daily lives, but their unique
visual and optical characteristics are notoriously challenging even for state-of-the-art
deep networks of semantic segmentation.
</p>
<p>
To alleviate this challenge, we construct a new large-scale real-world RGB-D dataset
called TROSD, which is more comprehensive than existing datasets for transparent and
reflective object segmentation.
Our TROSD dataset contains 11,060 RGB-D images with three semantic classes in terms
of transparent objects, reflective objects, and others, covering a variety of daily
scenes.
</p>
<p>
Together with the dataset, we also introduce a novel network (TROSNet) as a high-
standard baseline to assist other researchers to develop and benchmark their algorithms
of transparent and reflective object segmentation.
Moreover, extensive experiments also clearly show that the proposed TROSD dataset has
an excellent capacity to facilitate the development of semantic segmentation algorithms
with strong generalizability.
</p>
</div>
</div>
</div>
<!--/ Abstract. -->
</div>
</section>
<section class="section">
<div class="container is-max-desktop">
<div class="columns is-centered">
<!-- Visual Effects. -->
<div class="column">
<div class="content">
<h2 class="title is-3">Transparent Subset</h2>
<p>
We give several examples of samples with transparent objects in the scene.
</p>
<img src="./static/images/tran.jpg" class="interpolation-image" style="width: 100%; height: auto;"/>
</div>
</div>
<!--/ Visual Effects. -->
<!-- Matting. -->
<div class="column">
<h2 class="title is-3">Reflective Subset</h2>
<div class="columns is-centered">
<div class="column content">
<p>
Here are some more examples from our TROSD with reflective objects.
</p>
<img src="./static/images/refl.jpg" class="interpolation-image" style="width: 100%; height: auto;"/>
</div>
</div>
</div>
</div>
<!--/ Matting. -->
</div>
</section>
<section class="section" id="BibTeX">
<div class="container is-max-desktop content">
<h2 class="title">Segmentation Results</h2>
<p>
Our TROSNet properly segments the target objects from the scene and classifies them according to their type.
</p>
<div style="text-align: center;">
<img src="./static/images/seg_result.jpg" class="interpolation-image" style="width: 100%; height: auto;"/>
</div>
</div>
</section>
<section class="section" id="BibTeX">
<div class="container is-max-desktop content">
<h2 class="title">BibTeX</h2>
<pre><code>@article{sun2023trosd,
title={Trosd: A new rgb-d dataset for transparent and reflective object segmentation in practice},
author={Sun, Tianyu and Zhang, Guodong and Yang, Wenming and Xue, Jing-Hao and Wang, Guijin},
journal={IEEE Transactions on Circuits and Systems for Video Technology},
volume={33},
number={10},
pages={5721--5733},
year={2023},
publisher={IEEE}
}</code></pre>
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This website is licensed under a <a rel="license"
href="http://creativecommons.org/licenses/by-sa/4.0/">Creative
Commons Attribution-ShareAlike 4.0 International License</a>.
</p>
<p>
This project page was heavily based on the <a
href="https://github.com/nerfies/nerfies.github.io">Nerfies</a> project page.
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