  Some self-supervised cross-modal learning approaches have recently
demonstrated the potential of image signals for enhancing point cloud
representation. However, it remains a question on how to directly model
cross-modal local and global correspondences in a self-supervised fashion. To
solve it, we proposed PointCMC, a novel cross-modal method to model multi-scale
correspondences across modalities for self-supervised point cloud
representation learning. In particular, PointCMC is composed of: (1) a
local-to-local (L2L) module that learns local correspondences through optimized
cross-modal local geometric features, (2) a local-to-global (L2G) module that
aims to learn the correspondences between local and global features across
modalities via local-global discrimination, and (3) a global-to-global (G2G)
module, which leverages auxiliary global contrastive loss between the point
cloud and image to learn high-level semantic correspondences. Extensive
experiment results show that our approach outperforms existing state-of-the-art
methods in various downstream tasks such as 3D object classification and
segmentation. Code will be made publicly available upon acceptance.
