  To deal with the exhausting annotations, self-supervised representation
learning from unlabeled point clouds has drawn much attention, especially
centered on augmentation-based contrastive methods. However, specific
augmentations hardly produce sufficient transferability to high-level tasks on
different datasets. Besides, augmentations on point clouds may also change
underlying semantics. To address the issues, we propose a simple but efficient
augmentation fusion contrastive learning framework to combine data
augmentations in Euclidean space and feature augmentations in feature space. In
particular, we propose a data augmentation method based on sampling and graph
generation. Meanwhile, we design a data augmentation network to enable a
correspondence of representations by maximizing consistency between augmented
graph pairs. We further design a feature augmentation network that encourages
the model to learn representations invariant to the perturbations using an
encoder perturbation. We comprehensively conduct extensive object
classification experiments and object part segmentation experiments to validate
the transferability of the proposed framework. Experimental results demonstrate
that the proposed framework is effective to learn the point cloud
representation in a self-supervised manner, and yields state-of-the-art results
in the community. The source code is publicly available at:
https://zhiyongsu.github.io/Project/AFSRL.html.
