Update: website
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permalink: /index.html
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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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<a class="ml-4" href="https://www.cornell.edu/"><span>Cornell University</span></a>
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<a class="mr-4 ml-4" href="https://tech.cornell.edu/"><span>Cornell Tech</span></a>
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<a class="mr-4 ml-4" href="https://www.nvidia.com/"><span>NVIDIA</span></a>
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</p>
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</div>
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</div>
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@ -114,11 +114,10 @@
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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 width="560" height="315"
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src="https://www.youtube.com/embed/kG_8V1ooY8w" frameborder="0"
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allow="accelerometer; autoplay; encrypted-media; gyroscope; picture-in-picture" allowfullscreen>
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</iframe>
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</iframe>
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</div>
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</div>
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@ -132,16 +131,36 @@
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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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<p class='h2'>Brief Introduction to the Method</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 class='col-7 mt-3'>
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<img src="assets/pcl_is_a_sample.gif" class="img-fluid" alt="architecture">
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</div>
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<div class='col-5 align-middle mt-5'>
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<p class="text-break">
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Each shape can be viewed as a distribution of 3D points.
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In such distribution, points on the shape have higher probability and are more likely to be sampled.
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A point cloud can be viewed as a set of sampled points from such a distribution.
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</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-5 align-middle mt-5'>
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<p class="text-break">
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We use two continuous normalizing flows (CNF) to model the distribution of shapes, each of which is a distribution of 3D points.
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The latent CNF transforms a vector sampled from the shape prior to a latent shape vector.
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The point CNF transforms 3D points sampled from the point prior to a point cloud on the shape.
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</p>
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</div>
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<div class='col-7 mt-3'>
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<img src="assets/inference-architecture.gif" class="img-fluid" 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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@ -149,7 +168,7 @@
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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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<p class='h2'>Visulaization of the Flow</p>
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</div>
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</div>
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<div class='row'>
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