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Grendel Yang
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docs/assets/Bibtex.txt
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docs/assets/Bibtex.txt
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@article{TODO,
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title={PointFlow : 3D Point Cloud Generation with Continuous Normalizing Flows},
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author={TODO},
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journal={TODO},
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year={2019}
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}
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docs/index.html
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<!doctype html>
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<html lang="en">
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<head>
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<!-- Required meta tags -->
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<meta charset="utf-8">
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<meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no">
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<!-- Bootstrap CSS -->
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<link rel="stylesheet" href="https://stackpath.bootstrapcdn.com/bootstrap/4.3.1/css/bootstrap.min.css"
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integrity="sha384-ggOyR0iXCbMQv3Xipma34MD+dH/1fQ784/j6cY/iJTQUOhcWr7x9JvoRxT2MZw1T" crossorigin="anonymous">
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<title>PointFlow</title>
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</head>
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<body class="container" style="max-width:780px">
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<!-- Title -->
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<div>
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<div class='row mt-5 mb-3'>
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<div class='col text-center'>
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<p class="h2 font-weight-normal">PointFlow: 3D Point Cloud Generation with Continuous Normalizing Flows</p>
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</div>
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</div>
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<div class='row text-center h5 font-weight-bold pl-4 pr-4 mb-4'>
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<a class="col-3" href="http://www.guandaoyang.com"><span>Guandao Yang*</span></a>
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<a class="col-3" href="http://www.cs.cornell.edu/~xhuang/"><span>Xun Huang*</span></a>
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<a class="col-3" href="http://www.cs.cornell.edu/~zekun/"><span>Zekun Hao</span></a>
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<a class="col-3" href="http://mingyuliu.net/"><span>Ming-Yu Liu</span></a>
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<a class="col-6" href="http://blogs.cornell.edu/techfaculty/serge-belongie/"><span>Serge Belongie</span></a>
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<a class="col-6" href="http://home.bharathh.info/"><span>Bharath Hariharan</span></a>
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</div>
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<div class='row mt-1 mt-2' >
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<div class='col text-center'>
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<p class="h5 font-weight-light">
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<a class="ml-4" href=""><span>Cornell University</span></a>
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<a class="mr-4 ml-4" href=""><span>Cornell Tech</span></a>
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<a class="mr-4 ml-4" href=""><span>NVIDIA</span></a>
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</p>
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</div>
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</div>
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<div class='row justify-content-center'>
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<img src="assets/teaser.gif" class="img-fluid rounded mx-auto d-block" alt="airplane">
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</div>
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</div>
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<!-- Paper section -->
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<div>
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<hr>
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<div class='row'>
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<div class='col-3 text-center col-sm-3'>
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<!-- <p class='h2'>Paper</p> -->
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<div class="row mt-4">
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<a href="">
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<img src="assets/paper.jpg" alt="paper-snapshot" class="img-thumbnail" width="80%" style="box-shadow: 10px 10px 5px grey;">
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</a>
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</div>
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<div class="row mt-4">
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<div class="col">
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<a class="h5" href="" style="margin-right:10px">
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<span>[Arxiv]</span>
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</a>
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<a class="h5" href="https://github.com/stevenygd/PointFlow" style="margin-right:10px">
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<span>[Codes]</span>
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</a>
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<a class="h5" href="assets/Bibtex.txt" target="_blank">
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<span>[Bibtex]</span>
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</a>
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</div>
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</div>
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</div>
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<div class='col-9'>
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<p class='h4 font-weight-bold'>Abstract</p>
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<p style='line-height:1;'>
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As 3D point clouds become the representation of choice
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for multiple vision and graphics applications, the ability to
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synthesize or reconstruct high-resolution, high-fidelity point
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clouds becomes crucial. Despite the recent success of deep
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learning models in discriminative tasks of point clouds,
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generating point clouds remains challenging. This paper
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proposes a principled probabilistic framework to generate
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3D point clouds by modeling them as a distribution of
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distributions. Specifically, we learn a two-level hierarchy
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of distributions where the first level is the distribution of
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shapes and the second level is the distribution of points
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given a shape. This formulation allows us to both sample
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shapes and sample an arbitrary number of points from
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a shape. Our generative model, named PointFlow, learns
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each level of the distribution with a continuous normalizing
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flow. The invertibility of normalizing flows enables
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computation of the likelihood during training and allows us
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to train our model in the variational inference framework.
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Empirically, we demonstrate that PointFlow achieves stateof-
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the-art performance in point cloud generation. We additionally
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show that our model can faithfully reconstruct
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point clouds and learn useful representations in an unsupervised
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manner.
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</p>
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</div>
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</div>
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</div>
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<!-- Video -->
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<div>
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<hr>
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<div class='row text-center'>
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<div class='col'>
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<p class='h2'>Video</p>
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</div>
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</div>
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<div class='row mt-3 text-center'>
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<div class='col ml-1 mr-1'>
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<iframe
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width="480" height="300"
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src="https://www.youtube.com/embed/MXWm6w4E5q0" frameborder="0"
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allow="accelerometer; autoplay; encrypted-media; gyroscope; picture-in-picture" allowfullscreen
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>
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</iframe>
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</div>
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</div>
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</div>
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<!-- Architecture, explaination -->
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<div>
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<hr>
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<div class='row text-center'>
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<div class='col'>
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<p class='h2'>Architecture</p>
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</div>
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</div>
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<div class='row mt-3'>
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<div class='col'>
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<img src="assets/arch_bw.jpg" class="img-fluid" style="margin-top:15px" alt="architecture">
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</div>
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</div>
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</div>
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<!-- Results, transformation -->
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<div>
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<hr>
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<div class='row text-center'>
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<div class='col'>
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<p class='h2'>Flow Transformation</p>
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</div>
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</div>
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<div class='row'>
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<div class='col'>
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<img src="assets/flows.gif" class="img-fluid rounded mx-auto d-block" alt="flow-airplane-4">
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</div>
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</div>
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</div>
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<!-- Ack -->
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<div>
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<hr>
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<div class='row mb-5 text-center'>
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<div class='col'>
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<p class='h2'>Acknowledgements</p>
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<p>This work was supported in part by a research gift from Magic Leap. Xun Huang was supported by NVIDIA fellowship.</p>
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</div>
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</div>
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</div>
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<!-- Optional JavaScript -->
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<!-- jQuery first, then Popper.js, then Bootstrap JS -->
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<script src="https://code.jquery.com/jquery-3.3.1.slim.min.js"
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integrity="sha384-q8i/X+965DzO0rT7abK41JStQIAqVgRVzpbzo5smXKp4YfRvH+8abtTE1Pi6jizo" crossorigin="anonymous"></script>
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<script src="https://cdnjs.cloudflare.com/ajax/libs/popper.js/1.14.7/umd/popper.min.js"
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integrity="sha384-UO2eT0CpHqdSJQ6hJty5KVphtPhzWj9WO1clHTMGa3JDZwrnQq4sF86dIHNDz0W1" crossorigin="anonymous"></script>
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<script src="https://stackpath.bootstrapcdn.com/bootstrap/4.3.1/js/bootstrap.min.js"
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integrity="sha384-JjSmVgyd0p3pXB1rRibZUAYoIIy6OrQ6VrjIEaFf/nJGzIxFDsf4x0xIM+B07jRM" crossorigin="anonymous"></script>
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</body>
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</html>
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