diff --git a/docs/index.html b/docs/index.html index d09e370..b46e460 100755 --- a/docs/index.html +++ b/docs/index.html @@ -22,12 +22,12 @@
- Guandao Yang* - Xun Huang* - Zekun Hao - Ming-Yu Liu - Serge Belongie - Bharath Hariharan + Guandao Yang* + Xun Huang* + Zekun Hao + Ming-Yu Liu + Serge Belongie + Bharath Hariharan
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Abstract

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Abstract

As 3D point clouds become the representation of choice for multiple vision and graphics applications, the ability to @@ -127,7 +127,6 @@

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architecture
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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. @@ -151,14 +150,14 @@

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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. 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.

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architecture