of the involved neural networks. Current AE focuses on the learning of the global structure of point clouds in the encoding and decoding procedure. However, current AE structure is still limited by learning the local structure of point clouds, which tends to be an important piece of information for point cloud understanding.

To simultaneously learn global and local structure of point clouds, we propose a novel auto-encoder called Local-to-Global auto-encoder (L2G-AE). Different from traditional auto-encoder, L2G-AE leverages a local region reconstruction to learn the local structure of a point cloud, based on which the global shape is incrementally reconstructed for the learning of the global structure. Specifically, the encoder of L2G-AE can hierarchically encode the information at point, scale and region levels, where a novel hierarchical self-attention is introduced to highlight the important elements in each level. The encoder further aggregates all the information extracted from the point cloud into a global feature. In addition, L2G-AE employs a RNN-based decoder to decode the learned global feature into a sequence of scales in each local region. And based on scale features, the global point cloud is incrementally reconstructed. L2G-AE leverages this local to global reconstruction to facilitate the point cloud understanding, 