  Following the successes in the fields of vision and language, self-supervised
pretraining via masked autoencoding of 3D point set data, or Masked Point
Modeling (MPM), has achieved state-of-the-art accuracy in various downstream
tasks. However, current MPM methods lack a property essential for 3D point set
analysis, namely, invariance against rotation of 3D objects/scenes. Existing
MPM methods are thus not necessarily suitable for real-world applications where
3D point sets may have inconsistent orientations. This paper develops, for the
first time, a rotation-invariant self-supervised pretraining framework for
practical 3D point set analysis. The proposed algorithm, called MaskLRF, learns
rotation-invariant and highly generalizable latent features via masked
autoencoding of 3D points within Local Reference Frames (LRFs), which are not
affected by rotation of 3D point sets. MaskLRF enhances the quality of latent
features by integrating feature refinement using relative pose encoding and
feature reconstruction using low-level but rich 3D geometry. The efficacy of
MaskLRF is validated via extensive experiments on diverse downstream tasks
including classification, segmentation, registration, and domain adaptation. I
confirm that MaskLRF achieves new state-of-the-art accuracies in analyzing 3D
point sets having inconsistent orientations. Code will be available at:
https://github.com/takahikof/MaskLRF
