point cloud classification on ModelNet40.[]{data-label="tab:results_modelnet"}

&

  ---------------------
  PointNet++
  [@qi2017pointnet++]
  ---------------------

  : Results of point cloud classification on ModelNet40.[]{data-label="tab:results_modelnet"}

& 12 & - & 90.7\

  ----------
  Spectral
  GCNN
  ----------

  : Results of point cloud classification on ModelNet40.[]{data-label="tab:results_modelnet"}

&

  -------------------
  PointGCN
  [@zhang2018graph]
  -------------------

  : Results of point cloud classification on ModelNet40.[]{data-label="tab:results_modelnet"}

& 41 & 86.1 & 89.5\

  ----------
  Spectral
  GCNN
  ----------

  : Results of point cloud classification on ModelNet40.[]{data-label="tab:results_modelnet"}

&

  ----------------
  RGCNN
  [@te2018rgcnn]
  ----------------

  : Results of point cloud classification on ModelNet40.[]{data-label="tab:results_modelnet"}

& 22 & 87.3 & 90.5\

  ---------
  Spatial
  GCNN
  ---------

  : Results of point cloud classification on ModelNet40.[]{data-label="tab:results_modelnet"}

&

  ------------------
  DGCNN
  [@wang2019dgcnn]
  ------------------

  : Results of point cloud classification on ModelNet40.[]{data-label="tab:results_modelnet"}

& 21 & **90.2** & **92.9**\

  ----------
  Spectral
  GCNN
  ----------

  : Results of point cloud classification on ModelNet40.[]{data-label="tab:results_modelnet"}

& GCN (baseline) & **10** & 84.2 & 88.7\

  ----------
  Spectral
  GCNN
  ----------

  : Results of point cloud classification on ModelNet40.[]{data-label="tab:results_modelnet"}

& JLGCN & 13 & 87.2 & 90.8\

### Robustness test

Further, we test the robustness of our model when the point cloud is of low density. We randomly drop out points with missing ratios {0, 0.25, 0.5, 0.75, 0.9}. As shown in Fig. \[fig:robust\_pc\], our model outperforms the GCN 