32        64    
  --------- ------- ----------- ------- --
   Acc (%)   93.94   **95.37**   94.16  

  : The effects of the feature dimension $C$ of the self-attention module under ModelNet10.[]{data-label="table:c"}

Finally, we show the effects of feature dimension of local areas $D$ and the global feature $D_{global}$. The dimension is varied as shown in Table \[table:d\] and Table \[table:dglobal\]. Neither the biggest nor the smallest, L2G-AE gets better performances when $D$, $D_{global}$ are set to 256 and 1024 respectively. There is a trade-off between the network complexity and the expressive ability of our L2G-AE.

     $D$      128       256       512   
  --------- ------- ----------- ------- --
   Acc (%)   93.72   **95.37**   93.28  

  : The effects of the local feature dimension $D$ on ModelNet10.[]{data-label="table:d"}

   $D_{global}$    512      1024      2048   
  -------------- ------- ----------- ------- --
     Acc (%)      94.16   **95.37**   93.94  

  : The effects of the global feature dimension $D_{global}$ under ModelNet10.[]{data-label="table:dglobal"}

Ablation Study
--------------

To quantitatively evaluate the effect of the self-attention module, we show the performances of L2G-AE under four settings: with point level self-attention module only (PL), with area level self-attention module only (AL), with region level self-attention module only (RL), remove all self-attention modules (NSA) and with all self-attention modules (ASA). As shown in Table \[table:rsa\], the self-attention 