2D CNNs on large-scale image repositories such as ImageNet [@deng2009imagenet], it is intuitive to rasterize point clouds into 3D voxels and then apply 3D CNNs. Some studies [@qi2017frustum; @zhou2017voxelnet; @HanCyber17a] represent each voxel with a binary value which indicates the occupation of this location in space. The main problem of voxel-based methods is the fast growth of neural network size and computation complexity with the increasing of spatial resolution. To alleviate this problem, some improvements [@li2016fpnn] have been proposed to explore the data sparsity of point clouds. However, when dealing with point clouds with huge number of points, the complexity of the neural network is still unacceptable.

Recently, deep neural networks work quite effectively on the raw 3D point clouds. Different from learning from readered views [@han2018seqviews2seqlabels; @han20193d2seqviews; @han20192seq2seq; @han2019view; @han2019parts4feature; @han20193dviewgraph] 2D meshes [@Zhizhong2016] or 3D voxels [@Zhizhong2016b; @han2017boscc; @han2018deep], PointNet [@qi2016pointnet] is the pioneer study which directly learns the representation for point clouds by computing features for each point individually and aggregating these features with max-pool operation. To capture the contextual information of local patterns inside point clouds, PointNet++ [@NIPS2017_7095] uses sampling and grouping operations to extract features from point clusters hierarchically. Similarly, several recent studies [@riegler2017octnet; @klokov2017escape] 