in Table \[table:upsampling\], mean Chamfer Distance (mCD) is used as a metric for quantitative comparison with PU-Net (PU) and EC-Net (EC) under ModelNet10. Although the results of PU-Net and EC-Net are better than “Our” in some classes under ModelNet10, the most likely reason is that the ground-truth is not visible to L2G-AE in the training. In addition, the input point cloud with 625 points contains very limited information. Figure \[fig:upsampled\] shows some upsamled results of our L2G-AE.

![Some reconstructed examples of L2G-AE.[]{data-label="fig:reconstruct"}](reconstruct.pdf){height="7cm" width="7cm"}

![Some examples of the attention in the region level, where each subfigure represents a 3D object.[]{data-label="fig:attetnion"}](attention_rl.pdf){height="10cm" width="7cm"}

Visualization
-------------

In this section,we will show some important visualization results of L2G-AE. Firstly, some reconstructed point clouds by L2G-AE are listed with the ground-truths as shown in Figure \[fig:reconstruct\]. From the results, the reconstructed point clouds of L2G-AE are consistent with the ground-truths.

Then, some visualizations of the attention map inside self-attention modules are engaged to show the effect of attentions in the hierarchical feature abstraction. There are three self-attention modules in the encoder, and we first visualize the attention map inside the local region level. For intuitively understanding, we directly attach the attention values to the centroids of 