PointCMC: Cross-Modal Multi-Scale Correspondences Learning for Point Cloud Understanding. (arXiv:2211.12032v2 [cs.CV] UPDATED)

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, …

arxiv cloud scale understanding