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<html>
<head>
<title>Real-Time Dense Mapping for Self-Driving Vehicles using Fisheye Cameras</title></head>
<body>
<div class=WordSection1>
<p class=MsoNormal><o:p> </o:p></p>
<div align=center>
<table class=MsoNormalTable border=0 cellpadding=0 width=800 style='width:600.0pt;
mso-cellspacing:1.5pt;mso-yfti-tbllook:1184;mso-padding-alt:0cm 5.4pt 0cm 5.4pt'>
<tr>
<td>
<p align=center style='text-align:center'>
<b style='mso-bidi-font-weight:normal'>
<span style='font-size:24.0pt'> A Neural Network for Detailed Human Depth Estimation from a Single Image</span></b></p>
</td>
</tr>
</table>
</div>
<p class=MsoNormal><o:p> </o:p></p>
<div align=center>
<table class=MsoNormalTable border=0 cellpadding=0 width=800 style='width:600.0pt;
mso-cellspacing:1.5pt;mso-yfti-tbllook:1184;mso-padding-alt:0cm 5.4pt 0cm 5.4pt'
id=Table2>
<tr style='mso-yfti-irow:0;mso-yfti-firstrow:yes;mso-yfti-lastrow:yes'>
<td ></td>
</tr>
</table>
</div>
<p></p>
<div align=center>
<table class=MsoNormalTable border=0 cellpadding=0 width=800 style='width:600.0pt;
mso-cellspacing:1.5pt;mso-yfti-tbllook:1184;mso-padding-alt:0cm 5.4pt 0cm 5.4pt'>
<tr style='mso-fareast-font-family:"Times New Roman"' style='font-size:14.0pt'>
<td width=803>
<h3>Abstract:</h3>
<p style="text-align: justify">This paper presents a neural network to estimate a detailed
depth map of the foreground human in a single RGB
image. The result captures geometry details such as cloth
wrinkles, which are important in visualization applications.
To achieve this goal, we separate the depth map into a
smooth base shape and a residual detail shape and design
a network with two branches to regress them respectively.
We design a training strategy to ensure both base and detail
shapes can be faithfully learned by the corresponding
network branches. Furthermore, we introduce a novel network
layer to fuse a rough depth map and surface normals
to further improve the final result. Quantitative comparison
with fused ‘ground truth’ captured by real depth cameras
and qualitative examples on unconstrained Internet images
demonstrate the strength of the proposed method.</p>
<h3><strong>Online Video</strong></h3>
<div align=center>
<table class=MsoNormalTable border=0 width=516>
<tr>
<td>
<p align=center style='text-align:center'><strong>Spotlight </strong></p> </td>
</tr>
<tr align=center style='mso-yfti-irow:1'>
<td style='padding:.75pt .75pt .75pt .75pt'>
<iframe width="560" height="315" src="https://youtu.be/ulLpIYHcnCo" frameborder="0" allow="accelerometer; autoplay; encrypted-media; gyroscope; picture-in-picture" allowfullscreen></iframe> </td>
</tr>
</table>
</div>
<h3><strong>Download:</strong>
</h3>
<p><a href="https://arxiv.org/pdf/1910.01275.pdf">"A Neural Network for Detailed Human Depth Estimation from a Single Image"</a>, Sicong Tang, Feitong Tan, Kelvin Cheng, Zhaoyang Li, Siyu Zhu and Ping Tan.
arXiv preprint arXiv:1910.01275, 2019.</p>
<p> </p></td>
</tr>
</table>
</div>
</div>
</body>
</html>