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<script src="http://www.google.com/jsapi" type="text/javascript"></script>
<script type="text/javascript">google.load("jquery", "1.3.2");</script>
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<head>
<title>VA-DepthNet: A Variational Approach to Single Image Depth Prediction</title>
<meta property="og:image" content="" />
<meta property="og:title" content="VA-DepthNet: A Variational Approach to Single Image Depth Prediction" />
<link href='https://fonts.googleapis.com/css?family=Lora:400italic' rel='stylesheet' type='text/css'>
</head>
<body>
<br>
<center>
<span style="font-size:36px">VA-DepthNet: A Variational Approach to Single Image Depth Prediction</span>
</center>
<br>
<table align=center width=750px>
<tr>
<td align=center width=100px>
<center>
<span style="font-size:18px"><a href="https://github.com/cnexah">Ce Liu</a><sup>1</sup></span>
</center>
</td>
<td align=center width=100px>
<center>
<span style="font-size:18px"><a href="https://suryanshkumar.github.io/">Suryansh Kumar</a><sup>1</sup></span>
</center>
</td>
<td align=center width=100px>
<center>
<span style="font-size:18px"><a href="https://shuhanggu.github.io">Shuhang Gu</a><sup>2</sup></span>
</center>
</td>
<td align=center width=100px>
<center>
<span style="font-size:18px"><a href="https://www.informatik.uni-wuerzburg.de/computervision/home">Radu Timofte</a><sup>1, 3</sup></span>
</center>
</td>
<td align=center width=100px>
<center>
<span style="font-size:18px"><a href="https://scholar.google.com/citations?user=TwMib_QAAAAJ&hl=en">Luc Van Gool</a><sup>1, 4</sup></span>
</center>
</td>
</tr>
</table>
<table align=center width=700px>
<tr>
<td align=center width=100px>
<center>
<span style="font-size:20px">CVL ETH Zürich<sup>1</sup>, UESTC China<sup>2</sup>, University of Würzburg<sup>3</sup>, KU Lueven<sup>4</sup></span>
</center>
</td>
</tr>
</table>
<table align=center width=750px>
<tr>
<td align=center width=100px>
<center>
<span style="font-size:20px">The Eleventh International Conference on Learning Representations (ICLR), 2023.
</span>
</center>
</td>
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</table>
<br>
<!-- Image for the project-->
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<a><img src="./images/teaser.png" height="200px"></img></href></a><br>
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<br>
<hr>
<!-- Abstract of the project-->
<p style="text-align: justify;">
<span style="font-weight:bold">Abstract</span>
<br>
<font style="font-family: Lora; font-size: 100%">
We introduce VA-DepthNet, a simple, effective, and accurate deep neural network approach for the single-image depth prediction (SIDP) problem. The proposed approach advocates using classical first-order variational constraints for this problem. While state-of-the-art deep neural network methods for SIDP learn the scene depth from images in a supervised setting, they often overlook the invaluable invariances and priors in the rigid scene space, such as the regularity of the scene. The paper's main contribution is to reveal the benefit of classical and well-founded variational constraints in the neural network design for the SIDP task. It is shown that imposing first-order variational constraints in the scene space together with popular encoder-decoder-based network architecture design provides excellent results for the supervised SIDP task. The imposed first-order variational constraint makes the network aware of the depth gradient in the scene space, i.e., regularity. The paper demonstrates the usefulness of the proposed approach via extensive evaluation and ablation analysis over several benchmark datasets, such as KITTI, NYU Depth V2, and SUN RGB-D. The VA-DepthNet at test time shows considerable improvements in depth prediction accuracy compared to the prior art and is accurate also at high-frequency regions in the scene space. At the time of writing this paper, our method---labeled as VA-DepthNet, when tested on the KITTI depth-prediction evaluation set benchmarks, shows state-of-the-art results, and is the top-performing published approach.
</font>
</p><br><br>
<hr>
<!--Paper title, thumbnails, and publication details -->
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<center>
<h1>Paper</h1>
</center>
<tr>
<td><a href="https://openreview.net/forum?id=xjxUjHa_Wpa"><img style="height:200px" src="./images/papershot.png" /></a></td>
<td><span style="font-size:16pt">VA-DepthNet: A Variational Approach to Single Image Depth Prediction<br><br>
<i>Ce Liu, Suryansh Kumar, Shuhang Gu, Radu Timofte, Luc Van Gool.</i><br><br>
<span style="font-weight:bold">ICLR</span> 2023, Kigali, Rwanda.<br></td>
</tr>
</table>
<br>
<!-- paper link, supplementary link, bibtex link-->
<table align=center width=250px>
<tr>
<td>
<span style="font-size:14pt">
<center><a href="https://openreview.net/forum?id=xjxUjHa_Wpa">[Paper]</a>
</center>
</span>
</td>
<td>
<span style="font-size:14pt">
<center><a href="https://github.com/cnexah/VA-DepthNet">[Code]</a>
</center>
</span>
</td>
<td><span style="font-size:14pt">
<center><a href="./bibtex.txt">[Bibtex]</a></center>
</td>
</tr>
</table>
<br>
<hr>
<!--Results (Pictures or Youtube link) or Presentation link-->
<center>
<h1>Video Presentation</h1>
</center><br><br>
<table align=center width=1100px>
<!--Video link-->
<tr height="400px">
<td valign="top" width=1100px>
<center>
<video width="1000" controls autoplay muted loop>
<source src="./videos/vadepthnet_video.mp4" type="video/mp4">
Your browser does not support the video tag.
</video>
</center>
</td>
</tr>
</table>
<br>
<hr>
<!--Authors-->
<table align=center width=1100px>
<center>
<h1>Authors</h1>
</center>
<tr>
<td>
<div class="author_image"><img style="height:150px" src="./images/authors/ce.jpg"><p>Ce Liu</p></div>
</td>
<td>
<div class="author_image"><img style="height:150px" src="./images/authors/suryansh.jpg"><p>Suryansh Kumar</p></div>
</td>
<td>
<div class="author_image"><img style="height:150px" src="./images/authors/shuhang.jpg"><p>Shuhang Gu</p></div>
</td>
<td>
<div class="author_image"><img style="height:150px" src="./images/authors/radu.jpg"><p>Radu Timofte</p></div>
</td>
<td>
<div class="author_image"><img style="height:150px" src="./images/authors/luc.png">
<p>Luc Van Gool</p></div>
</td>
</tr>
</table>
<hr>
<!--Acknowledgements-->
<table align=center width=1100px>
<center>
<h1>Acknowledgements</h1>
</center>
<tr>
<td>
<p style="text-align: justify;">
This work was partly supported by ETH General Fund (OK), Chinese Scholarship Council (CSC), and The Alexander von Humboldt Foundation.
</p>
</td>
</tr>
</table>
<br><br>
</body>
</html>