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<head>
<link rel="icon" type="image/png" href="resources/ucsd_logo.png">
<title>SIGNet: Semantic Instance Aided Unsupervised 3D Geometry Perception</title>
<meta property='og:title' content='SIGNet: Semantic Instance Aided Unsupervised 3D Geometry Perception' />
<meta property="og:description" content="Meng, Lu, Raj, Sunarjo, Guo, Javidi, Bansal, Bharadia. SIGNet: Semantic Instance Aided Unsupervised 3D Geometry Perception. In CVPR, 2019." />
<meta property='og:url' content='https://mengyuest.github.io/signet/' />
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</head>
<body>
<br>
<center><span style="font-size:40px;font-weight:bold;color:#182B49">SIGNet: Semantic Instance Aided Unsupervised <br/> 3D Geometry Perception</span></center>
<table align=center width=900px>
<tr>
<td align=center width=180px>
<center><span style="font-size:24px"><a href="https://mengyuest.github.io/" target="_blank">Yue
Meng</a></span></center></td>
<td align=center width=180px>
<center><span style="font-size:24px"><a href="https://sites.google.com/site/luyongxi34/" target="_blank">Yongxi Lu </a></span></center></td>
<td align=center width=180px>
<center><span style="font-size:24px"><a href="https://amanrajdce.github.io/" target="_blank">Aman Raj</a></span></center></td>
<td align=center width=180px>
<center><span style="font-size:24px"><a href="https://mengyuest.github.io/signet/" target="_blank">Samuel Sunarjo</a></span></center></td>
<td align=center width=180px>
<center><span style="font-size:24px"><a href="https://mengyuest.github.io/signet/" target="_blank">Rui
Guo</a><sup>†</sup></span></center></td>
<tr/>
</table>
<table align=center width=800px>
<tr>
<td align=center width=150px>
<center><span style="font-size:24px"><a href="https://tjavidi.eng.ucsd.edu/" target="_blank">Tara
Javidi</a></span></center></td>
<td align=center width=150px>
<center><span style="font-size:24px"><a href="http://gaurav-bansal.com/" target="_blank">Gaurav
Bansal</a><sup>†</sup></span></center></td>
<td align=center width=150px>
<center><span style="font-size:24px"><a href="https://web.eng.ucsd.edu/~dineshb/" target="_blank">Dinesh
Bharadia</a></span></center></td>
<tr/>
</table>
<table align=center width=800px>
<tr>
<td align=center width=400px><center><span style="font-size:24px">University of California, San Diego</span></center></td>
<td align=center width=400px><center><span style="font-size:24px">Toyota InfoTechnology Center<sup>†</sup></span></center></td>
<tr/>
</table>
<table align=center width=400px>
<tr>
<td align=center width=150px>
<center><span style="font-size:24px"><a href="http://cvpr2019.thecvf.com/" target="_blank">CVPR 2019</a></span></center></td>
<tr/>
</table>
<!-- <table align=center width=500px>
<tr>
<td align=center width=200px><center><span style="font-size:24px"><a href="https://arxiv.org/pdf/1812.05642.pdf">[Paper]</a></span></center></td>
<td align=center width=200px><center><span style="font-size:24px"><a href='https://github.com/mengyuest/SIGNet'>[Code]</a></span></center></td>
<tr/>
</table> --> <table align=center width=900px>
<tr><td width=900px>
<center><a href="resources/first_one.gif"><img src = "resources/first_one.gif" height="250px"></img></a><br></center>
</td></tr>
</table>
<center id="abstract"><h1>Abstract</h1></center>
Unsupervised learning for visual perception of 3D geometry is of great interest to autonomous systems. This paper introduces SIGNet, a novel frameworkthat provides robust geometry perception without requiring geometrically informative labels. Specifically, SIGNet integrates semantic information to make unsupervised robust geometric predictions for objects in low lighting and noisy environments. SIGNet is shown to improve upon the state-of-the-art unsupervised learning for geometry perception by 30% (in squared relative error for depth prediction). In addition, SIGNet improves the dynamic object class performance by 39% in depth prediction and 29% in flow prediction.
<br>
<hr>
<center id="overview"><h1>Network</h1></center>
<table align=center width=1000px>
<tr><td width=1000px>
<center><a href="resources/network.png"><img src = "resources/network.png" height="250px"></img></a><br></center>
</td></tr>
</table>
<br>
<hr>
<center id="results"><h1>Depth Estimation Results on KITTI</h1></center>
<table align=center width=1000px>
<tr><td width=500px>
<center><a href="resources/table.png"><img src = "resources/table.png" height="250px"></img></a><br></center>
</td></tr>
</table>
<br>
<hr>
<center id="results"><h1>Robustness & Category-Specific Improvements</h1></center>
<table align=center width=1000px>
<tr>
<td width=500px>
<center><a href="resources/lighting.png"><img src = "resources/lighting.png" height="400px"></img></a><br></center>
</td>
<td width=500px>
<center><a href="resources/dynamic.png"><img src = "resources/dynamic.png" height="250px"></img></a><br></center>
</td>
</tr>
</table>
<br>
<hr>
<center id="sourceCode"><h1>Paper and Code</h1></center>
<table align=center width=900px>
<tr></tr>
<tr>
<td >
<a href="https://arxiv.org/pdf/1812.05642.pdf"><img class="paperpreview" src="resources/cap.png" width="200px"/></a>
</td>
<td></td>
<td width=700px > <span style="font-size:20px">
Yue Meng, Yongxi Lu, Aman Raj, Samuel Sunarjo, Rui Guo, Tara Javidi, Gaurav Bansal, and Dinesh Bharadia. <br/> <a href="https://arxiv.org/pdf/1812.05642.pdf">SIGNet: Semantic Instance Aided Unsupervised 3D Geometry Perception</a> <br/> <i>IEEE Conference on Computer Vision and Pattern Recognition (CVPR)</i>, 2019 <br/>[<a href="https://arxiv.org/pdf/1812.05642.pdf">PDF</a>][<a href="https://github.com/mengyuest/SIGNet">Code</a>]
</td>
</tr>
</table>
</center>
<br>
<hr>
<br/>
<br><br>
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