  Recently, the self-supervised learning framework data2vec has shown inspiring
performance for various modalities using a masked student-teacher approach.
However, it remains open whether such a framework generalizes to the unique
challenges of 3D point clouds. To answer this question, we extend data2vec to
the point cloud domain and report encouraging results on several downstream
tasks. In an in-depth analysis, we discover that the leakage of positional
information reveals the overall object shape to the student even under heavy
masking and thus hampers data2vec to learn strong representations for point
clouds. We address this 3D-specific shortcoming by proposing point2vec, which
unleashes the full potential of data2vec-like pre-training on point clouds. Our
experiments show that point2vec outperforms other self-supervised methods on
shape classification and few-shot learning on ModelNet40 and ScanObjectNN,
while achieving competitive results on part segmentation on ShapeNetParts.
These results suggest that the learned representations are strong and
transferable, highlighting point2vec as a promising direction for
self-supervised learning of point cloud representations.
