  Transformer-based Self-supervised Representation Learning methods learn
generic features from unlabeled datasets for providing useful network
initialization parameters for downstream tasks. Recently, self-supervised
learning based upon masking local surface patches for 3D point cloud data has
been under-explored. In this paper, we propose masked Autoencoders in 3D point
cloud representation learning (abbreviated as MAE3D), a novel autoencoding
paradigm for self-supervised learning. We first split the input point cloud
into patches and mask a portion of them, then use our Patch Embedding Module to
extract the features of unmasked patches. Secondly, we employ patch-wise MAE3D
Transformers to learn both local features of point cloud patches and high-level
contextual relationships between patches and complete the latent
representations of masked patches. We use our Point Cloud Reconstruction Module
with multi-task loss to complete the incomplete point cloud as a result. We
conduct self-supervised pre-training on ShapeNet55 with the point cloud
completion pre-text task and fine-tune the pre-trained model on ModelNet40 and
ScanObjectNN (PB\_T50\_RS, the hardest variant). Comprehensive experiments
demonstrate that the local features extracted by our MAE3D from point cloud
patches are beneficial for downstream classification tasks, soundly
outperforming state-of-the-art methods ($93.4\%$ and $86.2\%$ classification
accuracy, respectively).
