  Point cloud data plays an essential role in robotics and self-driving
applications. Yet, annotating point cloud data is time-consuming and nontrivial
while they enable learning discriminative 3D representations that empower
downstream tasks, such as classification and segmentation. Recently,
contrastive learning-based frameworks have shown promising results for learning
3D representations in a self-supervised manner. However, existing contrastive
learning methods cannot precisely encode and associate structural features and
search the higher dimensional augmentation space efficiently. In this paper, we
present CLR-GAM, a novel contrastive learning-based framework with Guided
Augmentation (GA) for efficient dynamic exploration strategy and Guided Feature
Mapping (GFM) for similar structural feature association between augmented
point clouds. We empirically demonstrate that the proposed approach achieves
state-of-the-art performance on both simulated and real-world 3D point cloud
datasets for three different downstream tasks, i.e., 3D point cloud
classification, few-shot learning, and object part segmentation.
