module is effective in learning highly discriminative representations of point clouds by capturing the correlation among feature vectors. The results with only one self-attention module outperform the results without any self-attention module. And we achieve the best performance when three self-attention modules work together. The performance of self-attentions is affected by the discriminative ability of features. At the area level, the features of areas in the same region are similar, since there are only four areas, which makes the self-attention at area level contribute the least among all three self-attentions. In contrast, at the point level and the region level, the features of points or regions change a lot, so these self-attentions contribute more. From our observation, the results of PL and RL are coincidentally equal in the experiments.

   Metric     PL      AL      RL      NSA       ASA
  --------- ------- ------- ------- ------- -----------
   Acc (%)   94.16   94.05   94.16   93.72   **95.37**

  : The effects of the self-attention module on ModelNet10.[]{data-label="table:rsa"}

![The reconstruction results of L2G-AE with only the local loss and only the global loss.[]{data-label="fig:loss_compare"}](loss_compare.pdf){width="8cm" height="2cm"}

After exploring the self-attention module, we also discuss the contributions of the two loss functions $L_{local}$ and $L_{global}$. In Table \[table:loss\], the results with local loss only 