  We present Point-BERT, a new paradigm for learning Transformers to generalize
the concept of BERT to 3D point cloud. Inspired by BERT, we devise a Masked
Point Modeling (MPM) task to pre-train point cloud Transformers. Specifically,
we first divide a point cloud into several local point patches, and a point
cloud Tokenizer with a discrete Variational AutoEncoder (dVAE) is designed to
generate discrete point tokens containing meaningful local information. Then,
we randomly mask out some patches of input point clouds and feed them into the
backbone Transformers. The pre-training objective is to recover the original
point tokens at the masked locations under the supervision of point tokens
obtained by the Tokenizer. Extensive experiments demonstrate that the proposed
BERT-style pre-training strategy significantly improves the performance of
standard point cloud Transformers. Equipped with our pre-training strategy, we
show that a pure Transformer architecture attains 93.8% accuracy on ModelNet40
and 83.1% accuracy on the hardest setting of ScanObjectNN, surpassing carefully
designed point cloud models with much fewer hand-made designs. We also
demonstrate that the representations learned by Point-BERT transfer well to new
tasks and domains, where our models largely advance the state-of-the-art of
few-shot point cloud classification task. The code and pre-trained models are
available at https://github.com/lulutang0608/Point-BERT
