2019-06-26 17:11:33 +00:00
|
|
|
<!doctype html>
|
|
|
|
|
<html lang="en">
|
|
|
|
|
<head>
|
|
|
|
|
<!-- Required meta tags -->
|
|
|
|
|
<meta charset="utf-8">
|
|
|
|
|
<meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no">
|
|
|
|
|
|
|
|
|
|
<!-- Bootstrap CSS -->
|
|
|
|
|
<link rel="stylesheet" href="https://stackpath.bootstrapcdn.com/bootstrap/4.3.1/css/bootstrap.min.css"
|
|
|
|
|
integrity="sha384-ggOyR0iXCbMQv3Xipma34MD+dH/1fQ784/j6cY/iJTQUOhcWr7x9JvoRxT2MZw1T" crossorigin="anonymous">
|
|
|
|
|
<title>PointFlow</title>
|
|
|
|
|
</head>
|
2019-06-30 07:45:24 +00:00
|
|
|
<body class="container" style="max-width:780px">
|
2019-06-26 17:11:33 +00:00
|
|
|
|
|
|
|
|
<!-- Title -->
|
|
|
|
|
<div>
|
|
|
|
|
<div class='row mt-5 mb-3'>
|
|
|
|
|
<div class='col text-center'>
|
|
|
|
|
<p class="h2 font-weight-normal">PointFlow: 3D Point Cloud Generation with Continuous Normalizing Flows</p>
|
|
|
|
|
</div>
|
|
|
|
|
</div>
|
|
|
|
|
|
2019-06-28 16:30:14 +00:00
|
|
|
<!-- authors -->
|
2019-06-26 17:11:33 +00:00
|
|
|
<div class='row text-center h5 font-weight-bold pl-4 pr-4 mb-4'>
|
2019-06-30 08:17:32 +00:00
|
|
|
<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>
|
2019-06-26 17:11:33 +00:00
|
|
|
</div>
|
|
|
|
|
|
2019-06-28 16:30:14 +00:00
|
|
|
<!-- affiliations -->
|
2019-06-26 17:11:33 +00:00
|
|
|
<div class='row mt-1 mt-2' >
|
|
|
|
|
<div class='col text-center'>
|
|
|
|
|
<p class="h5 font-weight-light">
|
2019-06-28 16:13:02 +00:00
|
|
|
<a class="ml-4" href="https://www.cornell.edu/"><span>Cornell University</span></a>
|
|
|
|
|
<a class="mr-4 ml-4" href="https://tech.cornell.edu/"><span>Cornell Tech</span></a>
|
|
|
|
|
<a class="mr-4 ml-4" href="https://www.nvidia.com/"><span>NVIDIA</span></a>
|
2019-06-26 17:11:33 +00:00
|
|
|
</p>
|
|
|
|
|
</div>
|
|
|
|
|
</div>
|
|
|
|
|
|
2019-06-30 07:45:24 +00:00
|
|
|
<div class='row justify-content-center' style="position: relative; width: 100%;height: 0;padding-bottom: 33%;">
|
|
|
|
|
<img src="assets/teaser.gif" class="img-fluid rounded mx-auto d-block" alt="airplane" style="position:absolute; width:100%; height:100%; top:0px; left:0px;">
|
2019-06-26 17:11:33 +00:00
|
|
|
</div>
|
|
|
|
|
</div>
|
|
|
|
|
|
|
|
|
|
<!-- Paper section -->
|
|
|
|
|
<div>
|
|
|
|
|
<hr>
|
|
|
|
|
<div class='row'>
|
2019-06-30 08:17:32 +00:00
|
|
|
<div class='col-md-3 col-sm-3 col-xs-12 text-center col-sm-3'>
|
2019-06-26 17:11:33 +00:00
|
|
|
<div class="row mt-4">
|
2019-06-30 08:17:32 +00:00
|
|
|
<a href="assets/PointFlow.pdf" style="max-width:200px; margin-left:auto; margin-right:auto">
|
2019-06-26 17:11:33 +00:00
|
|
|
<img src="assets/paper.jpg" alt="paper-snapshot" class="img-thumbnail" width="80%" style="box-shadow: 10px 10px 5px grey;">
|
|
|
|
|
</a>
|
|
|
|
|
</div>
|
|
|
|
|
<div class="row mt-4">
|
|
|
|
|
<div class="col">
|
|
|
|
|
<a class="h5" href="" style="margin-right:10px">
|
|
|
|
|
<span>[Arxiv]</span>
|
|
|
|
|
</a>
|
|
|
|
|
<a class="h5" href="https://github.com/stevenygd/PointFlow" style="margin-right:10px">
|
|
|
|
|
<span>[Codes]</span>
|
|
|
|
|
</a>
|
|
|
|
|
<a class="h5" href="assets/Bibtex.txt" target="_blank">
|
|
|
|
|
<span>[Bibtex]</span>
|
|
|
|
|
</a>
|
|
|
|
|
</div>
|
|
|
|
|
</div>
|
|
|
|
|
</div>
|
2019-06-30 08:17:32 +00:00
|
|
|
<div class='col-md-9 col-sm-9 col-xs-12'>
|
|
|
|
|
<p class='h4 font-weight-bold '>Abstract</p>
|
2019-06-26 17:11:33 +00:00
|
|
|
<p style='line-height:1;'>
|
|
|
|
|
As 3D point clouds become the representation of choice
|
|
|
|
|
for multiple vision and graphics applications, the ability to
|
|
|
|
|
synthesize or reconstruct high-resolution, high-fidelity point
|
|
|
|
|
clouds becomes crucial. Despite the recent success of deep
|
|
|
|
|
learning models in discriminative tasks of point clouds,
|
|
|
|
|
generating point clouds remains challenging. This paper
|
|
|
|
|
proposes a principled probabilistic framework to generate
|
|
|
|
|
3D point clouds by modeling them as a distribution of
|
|
|
|
|
distributions. Specifically, we learn a two-level hierarchy
|
|
|
|
|
of distributions where the first level is the distribution of
|
|
|
|
|
shapes and the second level is the distribution of points
|
|
|
|
|
given a shape. This formulation allows us to both sample
|
|
|
|
|
shapes and sample an arbitrary number of points from
|
|
|
|
|
a shape. Our generative model, named PointFlow, learns
|
|
|
|
|
each level of the distribution with a continuous normalizing
|
|
|
|
|
flow. The invertibility of normalizing flows enables
|
|
|
|
|
computation of the likelihood during training and allows us
|
|
|
|
|
to train our model in the variational inference framework.
|
|
|
|
|
Empirically, we demonstrate that PointFlow achieves stateof-
|
|
|
|
|
the-art performance in point cloud generation. We additionally
|
|
|
|
|
show that our model can faithfully reconstruct
|
|
|
|
|
point clouds and learn useful representations in an unsupervised
|
|
|
|
|
manner.
|
|
|
|
|
</p>
|
|
|
|
|
</div>
|
|
|
|
|
</div>
|
|
|
|
|
</div>
|
|
|
|
|
|
|
|
|
|
<!-- Video -->
|
|
|
|
|
<div>
|
|
|
|
|
<hr>
|
|
|
|
|
|
|
|
|
|
<div class='row text-center'>
|
|
|
|
|
<div class='col'>
|
2019-06-28 17:43:50 +00:00
|
|
|
<p class='h2 mr-3'>Video</p>
|
2019-06-26 17:11:33 +00:00
|
|
|
</div>
|
|
|
|
|
</div>
|
|
|
|
|
|
|
|
|
|
|
2019-06-30 07:45:24 +00:00
|
|
|
<div class='row mt-3 text-center center-block' style=" margin-left:auto; margin-right:auto; max-width:560px">
|
|
|
|
|
<div class='col ml-1 mr-1' style="position: relative; width: 100%;height: 0;padding-bottom: 56%;">
|
2019-06-28 17:43:50 +00:00
|
|
|
<iframe
|
|
|
|
|
src="https://www.youtube.com/embed/jqBiv77xC0M"
|
|
|
|
|
frameborder="0"
|
2019-06-30 07:45:24 +00:00
|
|
|
allow="accelerometer; autoplay; encrypted-media; gyroscope; picture-in-picture" allowfullscreen
|
|
|
|
|
style="position: absolute;width: 100%;height: 100%; left: 0; top: 0;"
|
|
|
|
|
>
|
2019-06-28 16:13:02 +00:00
|
|
|
</iframe>
|
2019-06-26 17:11:33 +00:00
|
|
|
</div>
|
|
|
|
|
</div>
|
|
|
|
|
|
|
|
|
|
</div>
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
<!-- Architecture, explaination -->
|
|
|
|
|
<div>
|
|
|
|
|
<hr>
|
|
|
|
|
<div class='row text-center'>
|
|
|
|
|
<div class='col'>
|
2019-06-28 16:13:02 +00:00
|
|
|
<p class='h2'>Brief Introduction to the Method</p>
|
2019-06-26 17:11:33 +00:00
|
|
|
</div>
|
|
|
|
|
</div>
|
|
|
|
|
|
2019-06-28 16:13:02 +00:00
|
|
|
<div class='row mt-3'>
|
2019-06-30 08:17:32 +00:00
|
|
|
<div class='col-md-7 col-sm-7 col-xs-12 mt-3'>
|
2019-06-28 16:13:02 +00:00
|
|
|
<img src="assets/pcl_is_a_sample.gif" class="img-fluid" alt="architecture">
|
|
|
|
|
</div>
|
2019-06-30 08:17:32 +00:00
|
|
|
<div class='col-md-5 col-sm-5 col-xs-12 align-middle mt-5'>
|
2019-06-28 16:13:02 +00:00
|
|
|
<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.
|
2019-06-28 16:30:14 +00:00
|
|
|
A point cloud can be viewed as a set of points sampled from such distribution.
|
2019-06-28 16:13:02 +00:00
|
|
|
</p>
|
|
|
|
|
</div>
|
|
|
|
|
</div>
|
2019-06-26 17:11:33 +00:00
|
|
|
|
|
|
|
|
<div class='row mt-3'>
|
2019-06-30 08:17:32 +00:00
|
|
|
<div class='col-md-5 col-sm-5 col-xs-12 align-middle mt-5'>
|
2019-06-28 16:13:02 +00:00
|
|
|
<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>
|
2019-06-30 08:17:32 +00:00
|
|
|
<div class='col-md-7 col-sm-7 col-xs-12 mt-3'>
|
2019-06-28 16:13:02 +00:00
|
|
|
<img src="assets/inference-architecture.gif" class="img-fluid" alt="architecture">
|
2019-06-26 17:11:33 +00:00
|
|
|
</div>
|
|
|
|
|
</div>
|
2019-06-28 16:13:02 +00:00
|
|
|
|
2019-06-26 17:11:33 +00:00
|
|
|
</div>
|
|
|
|
|
|
|
|
|
|
<!-- Results, transformation -->
|
|
|
|
|
<div>
|
|
|
|
|
<hr>
|
|
|
|
|
<div class='row text-center'>
|
|
|
|
|
<div class='col'>
|
2019-06-28 16:13:02 +00:00
|
|
|
<p class='h2'>Visulaization of the Flow</p>
|
2019-06-26 17:11:33 +00:00
|
|
|
</div>
|
|
|
|
|
</div>
|
|
|
|
|
<div class='row'>
|
|
|
|
|
<div class='col'>
|
|
|
|
|
<img src="assets/flows.gif" class="img-fluid rounded mx-auto d-block" alt="flow-airplane-4">
|
|
|
|
|
</div>
|
|
|
|
|
</div>
|
|
|
|
|
</div>
|
|
|
|
|
|
|
|
|
|
<!-- Ack -->
|
|
|
|
|
<div>
|
|
|
|
|
<hr>
|
|
|
|
|
|
|
|
|
|
<div class='row mb-5 text-center'>
|
|
|
|
|
<div class='col'>
|
|
|
|
|
<p class='h2'>Acknowledgements</p>
|
|
|
|
|
<p>This work was supported in part by a research gift from Magic Leap. Xun Huang was supported by NVIDIA fellowship.</p>
|
|
|
|
|
</div>
|
|
|
|
|
</div>
|
|
|
|
|
</div>
|
|
|
|
|
|
|
|
|
|
<!-- Optional JavaScript -->
|
|
|
|
|
<!-- jQuery first, then Popper.js, then Bootstrap JS -->
|
|
|
|
|
<script src="https://code.jquery.com/jquery-3.3.1.slim.min.js"
|
|
|
|
|
integrity="sha384-q8i/X+965DzO0rT7abK41JStQIAqVgRVzpbzo5smXKp4YfRvH+8abtTE1Pi6jizo" crossorigin="anonymous"></script>
|
|
|
|
|
<script src="https://cdnjs.cloudflare.com/ajax/libs/popper.js/1.14.7/umd/popper.min.js"
|
|
|
|
|
integrity="sha384-UO2eT0CpHqdSJQ6hJty5KVphtPhzWj9WO1clHTMGa3JDZwrnQq4sF86dIHNDz0W1" crossorigin="anonymous"></script>
|
|
|
|
|
<script src="https://stackpath.bootstrapcdn.com/bootstrap/4.3.1/js/bootstrap.min.js"
|
|
|
|
|
integrity="sha384-JjSmVgyd0p3pXB1rRibZUAYoIIy6OrQ6VrjIEaFf/nJGzIxFDsf4x0xIM+B07jRM" crossorigin="anonymous"></script>
|
|
|
|
|
</body>
|
|
|
|
|
</html>
|
