which finally enables local and global reconstruction losses to train L2G-AE.

Our key contributions are summarized as follows.

-   We propose L2G-AE to enable the learning of global and local structures of point clouds in an auto-encoder architecture, where the local structure is very important in learning highly discriminative representations of point clouds.

-   We propose hierarchical self-attention to highlight important elements in point, scale and region levels by learning the correlations among the elements in the same level.

-   We introduce RNN as decoding layer in an auto-encoder architecture to employ more detailed self supervision, where the RNN takes the advantage of the ordered multi-scale areas in each local region.

Related Work
============

Point clouds is a fundamental type of 3D data format which is very close to the raw data of various 3D sensors. Recently, applications of learning directly on point clouds have received extensive attention, including shape completion [@Stutz2018CVPR], autonomous driving [@qi2017frustum], 3D object detection [@simon2018complex; @Yang2018pixor; @zhou2017voxelnet], recognition and classification [@qi2016pointnet; @NIPS2017_7095; @golovinskiy2009shape; @li2018pointcnn; @wang2018dynamic; @xu2018spidercnn; @shen2018mining; @xie2018attentional; @li2018so; @you2018pvnet], scene labeling [@NIPS2011_4226], upsampling [@yu2018pu; @2019arXiv181111286Y], dense labeling and segmentation [@Wang2018pointseg] , etc.

Due to the irregular property of point cloud and the inspiring performances of 