  Masked autoencoding has achieved great success for self-supervised learning
in the image and language domains. However, mask based pretraining has yet to
show benefits for point cloud understanding, likely due to standard backbones
like PointNet being unable to properly handle the training versus testing
distribution mismatch introduced by masking during training. In this paper, we
bridge this gap by proposing a discriminative mask pretraining Transformer
framework, MaskPoint}, for point clouds. Our key idea is to represent the point
cloud as discrete occupancy values (1 if part of the point cloud; 0 if not),
and perform simple binary classification between masked object points and
sampled noise points as the proxy task. In this way, our approach is robust to
the point sampling variance in point clouds, and facilitates learning rich
representations. We evaluate our pretrained models across several downstream
tasks, including 3D shape classification, segmentation, and real-word object
detection, and demonstrate state-of-the-art results while achieving a
significant pretraining speedup (e.g., 4.1x on ScanNet) compared to the prior
state-of-the-art Transformer baseline. Code is available at
https://github.com/haotian-liu/MaskPoint.
