  Deep learning techniques for point clouds have achieved strong performance on
a range of 3D vision tasks. However, it is costly to annotate large-scale point
sets, making it critical to learn generalizable representations that can
transfer well across different point sets. In this paper, we study a new
problem of 3D Domain Generalization (3DDG) with the goal to generalize the
model to other unseen domains of point clouds without any access to them in the
training process. It is a challenging problem due to the substantial geometry
shift from simulated to real data, such that most existing 3D models
underperform due to overfitting the complete geometries in the source domain.
We propose to tackle this problem via MetaSets, which meta-learns point cloud
representations from a group of classification tasks on carefully-designed
transformed point sets containing specific geometry priors. The learned
representations are more generalizable to various unseen domains of different
geometries. We design two benchmarks for Sim-to-Real transfer of 3D point
clouds. Experimental results show that MetaSets outperforms existing 3D deep
learning methods by large margins.
