  The manual annotation for large-scale point clouds costs a lot of time and is
usually unavailable in harsh real-world scenarios. Inspired by the great
success of the pre-training and fine-tuning paradigm in both vision and
language tasks, we argue that pre-training is one potential solution for
obtaining a scalable model to 3D point cloud downstream tasks as well. In this
paper, we, therefore, explore a new self-supervised learning method, called
Mixing and Disentangling (MD), for 3D point cloud representation learning. As
the name implies, we mix two input shapes and demand the model learning to
separate the inputs from the mixed shape. We leverage this reconstruction task
as the pretext optimization objective for self-supervised learning. There are
two primary advantages: 1) Compared to prevailing image datasets, eg, ImageNet,
point cloud datasets are de facto small. The mixing process can provide a much
larger online training sample pool. 2) On the other hand, the disentangling
process motivates the model to mine the geometric prior knowledge, eg, key
points. To verify the effectiveness of the proposed pretext task, we build one
baseline network, which is composed of one encoder and one decoder. During
pre-training, we mix two original shapes and obtain the geometry-aware
embedding from the encoder, then an instance-adaptive decoder is applied to
recover the original shapes from the embedding. Albeit simple, the pre-trained
encoder can capture the key points of an unseen point cloud and surpasses the
encoder trained from scratch on downstream tasks. The proposed method has
improved the empirical performance on both ModelNet-40 and ShapeNet-Part
datasets in terms of point cloud classification and segmentation tasks. We
further conduct ablation studies to explore the effect of each component and
verify the generalization of our proposed strategy by harnessing different
backbones.
