Auto-encoder is an important architecture to understand point clouds in an
encoding and decoding procedure of self reconstruction. Current auto-encoder
mainly focuses on the learning of global structure by global shape
reconstruction, while ignoring the learning of local structures. To resolve
this issue, we propose Local-to-Global auto-encoder (L2G-AE) to simultaneously
learn the local and global structure of point clouds by local to global
reconstruction. Specifically, L2G-AE employs an encoder to encode the geometry
information of multiple scales in a local region at the same time. In addition,
we introduce a novel hierarchical self-attention mechanism to highlight the
important points, scales and regions at different levels in the information
aggregation of the encoder. Simultaneously, L2G-AE employs a recurrent neural
network (RNN) as decoder to reconstruct a sequence of scales in a local region,
based on which the global point cloud is incrementally reconstructed. Our
outperforming results in shape classification, retrieval and upsampling show
that L2G-AE can understand point clouds better than state-of-the-art methods.

本研究提出了一种名为 Local-to-Global auto-encoder（L2G-AE）的自编码器模型，通过引入层次自注意力机制和重构局部区域大小以及局部到全局的关系，能够更好地了解点云的局部和整体结构，同时在形状分类，检索和上采样等方面比现有方法表现更卓越。