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Grendel Yang 2019-06-30 01:17:32 -07:00
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<!-- authors -->
<div class='row text-center h5 font-weight-bold pl-4 pr-4 mb-4'>
<a class="col-3" href="http://www.guandaoyang.com"><span>Guandao Yang*</span></a>
<a class="col-3" href="http://www.cs.cornell.edu/~xhuang/"><span>Xun Huang*</span></a>
<a class="col-3" href="http://www.cs.cornell.edu/~zekun/"><span>Zekun Hao</span></a>
<a class="col-3" href="http://mingyuliu.net/"><span>Ming-Yu Liu</span></a>
<a class="col-6" href="http://blogs.cornell.edu/techfaculty/serge-belongie/"><span>Serge Belongie</span></a>
<a class="col-6" href="http://home.bharathh.info/"><span>Bharath Hariharan</span></a>
<a class="col-md-3 col-xs-6" href="http://www.guandaoyang.com"><span>Guandao Yang*</span></a>
<a class="col-md-3 col-xs-6" href="http://www.cs.cornell.edu/~xhuang/"><span>Xun Huang*</span></a>
<a class="col-md-3 col-xs-6" href="http://www.cs.cornell.edu/~zekun/"><span>Zekun Hao</span></a>
<a class="col-md-3 col-xs-6" href="http://mingyuliu.net/"><span>Ming-Yu Liu</span></a>
<a class="col-md-6 col-xs-6" href="http://blogs.cornell.edu/techfaculty/serge-belongie/"><span>Serge Belongie</span></a>
<a class="col-md-6 col-xs-6" href="http://home.bharathh.info/"><span>Bharath Hariharan</span></a>
</div>
<!-- affiliations -->
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<div>
<hr>
<div class='row'>
<div class='col-3 text-center col-sm-3'>
<div class='col-md-3 col-sm-3 col-xs-12 text-center col-sm-3'>
<div class="row mt-4">
<a href="assets/PointFlow.pdf">
<a href="assets/PointFlow.pdf" style="max-width:200px; margin-left:auto; margin-right:auto">
<img src="assets/paper.jpg" alt="paper-snapshot" class="img-thumbnail" width="80%" style="box-shadow: 10px 10px 5px grey;">
</a>
</div>
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</div>
</div>
</div>
<div class='col-9'>
<p class='h4 font-weight-bold'>Abstract</p>
<div class='col-md-9 col-sm-9 col-xs-12'>
<p class='h4 font-weight-bold '>Abstract</p>
<p style='line-height:1;'>
As 3D point clouds become the representation of choice
for multiple vision and graphics applications, the ability to
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</div>
<!-- Architecture, explaination -->
<div>
<hr>
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</div>
<div class='row mt-3'>
<div class='col-7 mt-3'>
<div class='col-md-7 col-sm-7 col-xs-12 mt-3'>
<img src="assets/pcl_is_a_sample.gif" class="img-fluid" alt="architecture">
</div>
<div class='col-5 align-middle mt-5'>
<div class='col-md-5 col-sm-5 col-xs-12 align-middle mt-5'>
<p class="text-break">
Each shape can be viewed as a distribution of 3D points.
In such distribution, points on the shape have higher probability and are more likely to be sampled.
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</div>
<div class='row mt-3'>
<div class='col-5 align-middle mt-5'>
<div class='col-md-5 col-sm-5 col-xs-12 align-middle mt-5'>
<p class="text-break">
We use two continuous normalizing flows (CNF) to model the distribution of shapes, each of which is a distribution of 3D points.
The latent CNF transforms a vector sampled from the shape prior to a latent shape vector.
The point CNF transforms 3D points sampled from the point prior to a point cloud on the shape.
</p>
</div>
<div class='col-7 mt-3'>
<div class='col-md-7 col-sm-7 col-xs-12 mt-3'>
<img src="assets/inference-architecture.gif" class="img-fluid" alt="architecture">
</div>
</div>