methodologies Computer vision&lt;/concept\_desc&gt; &lt;concept\_significance&gt;500&lt;/concept\_significance&gt; &lt;/concept&gt; &lt;concept&gt; &lt;concept\_id&gt;10010147.10010178.10010224.10010240.10010242&lt;/concept\_id&gt; &lt;concept\_desc&gt;Computing methodologies Shape representations&lt;/concept\_desc&gt; &lt;concept\_significance&gt;500&lt;/concept\_significance&gt; &lt;/concept&gt; &lt;concept&gt; &lt;concept\_id&gt;10002951.10003317&lt;/concept\_id&gt; &lt;concept\_desc&gt;Information systems Information retrieval&lt;/concept\_desc&gt; &lt;concept\_significance&gt;300&lt;/concept\_significance&gt; &lt;/concept&gt; &lt;/ccs2012&gt;

![image](architecture.pdf){height="5cm" width="16cm"}

Introduction
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In recent years, point clouds have attracted increasing attention due to the popularity of various depth sensors in different applications. Not only the traditional methods, deep neural networks have also been applied to point cloud analysis and understanding. However, it remains a challenge to directly learn from point clouds. Different from 2D images, point cloud is an irregular 3D data which makes it difficult to directly use traditional deep learning framework, e.g., traditional convolution neural network (CNN). The traditional CNN usually requires some fixed spatial distribution around each pixel so as to facilitate the convolution. One way to alleviate the problem is to voxelize a point cloud into voxels and then apply 3D Cov-Nets. However, because of the sparsity of point clouds, it leads to resolution-loss and explosive computation complexity, which sacrifices the representation accuracy.

To address above challenges, PointNet [@qi2016pointnet] has been proposed to directly learn shape representations from raw point sets. Along with the availability of directly learning from point clouds by deep learning models, auto-encoder (AE) has become an vital architecture 