classification accuracy (%) under ModelNet10 and ModelNet40.[]{data-label="table:compare"}

Classification
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In this subsection, we evaluate the performance of L2G-AE under ModelNet10 and ModelNet40 benchmarks, where ModelNet40 contains 12, 311 CAD models which is split into 9, 843 for training and 2, 468 for testing. Table \[table:compare\] compares L2G-AE with state-of-the-art methods in the shape classification task on ModelNet10 and ModelNet40. The compared methods include PointNet [@qi2016pointnet], PointNet++ [@NIPS2017_7095], ShapeContextNet [@xie2018attentional], KD-Net [@klokov2017escape], KC-Net [@shen2018mining], PointCNN [@li2018pointcnn], DGCNN [@wang2018dynamic], SO-Net [@li2018so], Point2Sequence [@liu2019point2sequence], MAP-VAE [@Zhizhong2019mapvae], LGAN [@Achlioptas2017latent] and FoldingNet [@yang2018foldingnet].

L2G-AE significantly outperforms all the unsupervised competitors under ModelNet10 and ModelNet40, respectively. In particular, L2G-AE achieves accuracy $95.37\%$ which is even higher than other methods of supervision under ModelNet10. Although the results of LGAN [@Achlioptas2017latent] and FoldingNet [@yang2018foldingnet] also show good performances under ModelNet10 and ModelNet40. This is because these methods are trained under a version of ShapeNet55 that contains more than 57,000 3D shapes. However, this version of ShapeNet55 dataset is not avaiable for public download from the official website. Therefore, we train all these methods under ModelNet40 for the fair comparison.

   Methods   LGAN    FoldingNet      Our
  --------- ------- ------------ -----------
   Acc (%)   49.94     53.42      **67.81**

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