  Self-supervised learning is attracting wide attention in point cloud
processing. However, it is still not well-solved to gain discriminative and
transferable features of point clouds for efficient training on downstream
tasks, due to their natural sparsity and irregularity. We propose PointSmile, a
reconstruction-free self-supervised learning paradigm by maximizing curriculum
mutual information (CMI) across the replicas of point cloud objects. From the
perspective of how-and-what-to-learn, PointSmile is designed to imitate human
curriculum learning, i.e., starting with an easy curriculum and gradually
increasing the difficulty of that curriculum. To solve "how-to-learn", we
introduce curriculum data augmentation (CDA) of point clouds. CDA encourages
PointSmile to learn from easy samples to hard ones, such that the latent space
can be dynamically affected to create better embeddings. To solve
"what-to-learn", we propose to maximize both feature- and class-wise CMI, for
better extracting discriminative features of point clouds. Unlike most of
existing methods, PointSmile does not require a pretext task, nor does it
require cross-modal data to yield rich latent representations. We demonstrate
the effectiveness and robustness of PointSmile in downstream tasks including
object classification and segmentation. Extensive results show that our
PointSmile outperforms existing self-supervised methods, and compares favorably
with popular fully-supervised methods on various standard architectures.
