terms of under ModelNet10.[]{data-label="table:retrieval"}

![The comparison of PR curves for retrieval under ModelNet10.[]{data-label="fig:pr"}](pr.pdf){height="3cm" width="4cm"}

   $10^{-3}$   bathtub      bed       chair       desk     dresser    monitor    n.stand      sofa      table      toilet   
  ----------- ---------- ---------- ---------- ---------- ---------- ---------- ---------- ---------- ---------- ---------- --
      PU       **1.01**   **1.12**   **0.82**   **1.22**     1.55     **1.19**   **1.77**   **1.13**   **0.69**   **1.39**  
      EC         1.43       1.81       1.80       1.30       1.43       2.04       1.88       1.79       1.00       1.72    
      Our        1.74       1.46       1.58       2.08     **1.40**     1.61       1.86       1.67       1.86       2.10    

Retrieval
---------

L2G-AE is further evaluated in the shape retrieval task under ModelNet10 and compared with some other unsupervised methods of learning on point clouds. The compared results include two state-of-the-art unsupervised methods for point clouds, i.e., LGAN [@Achlioptas2017latent] and FoldingNet [@yang2018foldingnet]. The target of shape retrieval is to obtain the relevant information of a inquiry from a collection. In these experiments, the 3D shapes in the test set are used as quires to retrieve the rest shapes in the same set, and mean Average Precision (mAP) is used as a metric.

As shown in Table \[table:retrieval\], our results outperform all the compared results under ModelNet10. It shows that L2G-AE can be effect in improving the performance of unsupervised shape retrieval on point clouds. Their PR curves under ModelNet10 are 