  Masked Autoencoders (MAE) have demonstrated promising performance in
self-supervised learning for both 2D and 3D computer vision. Nevertheless,
existing MAE-based methods still have certain drawbacks. Firstly, the
functional decoupling between the encoder and decoder is incomplete, which
limits the encoder's representation learning ability. Secondly, downstream
tasks solely utilize the encoder, failing to fully leverage the knowledge
acquired through the encoder-decoder architecture in the pre-text task. In this
paper, we propose Point Regress AutoEncoder (Point-RAE), a new scheme for
regressive autoencoders for point cloud self-supervised learning. The proposed
method decouples functions between the decoder and the encoder by introducing a
mask regressor, which predicts the masked patch representation from the visible
patch representation encoded by the encoder and the decoder reconstructs the
target from the predicted masked patch representation. By doing so, we minimize
the impact of decoder updates on the representation space of the encoder.
Moreover, we introduce an alignment constraint to ensure that the
representations for masked patches, predicted from the encoded representations
of visible patches, are aligned with the masked patch presentations computed
from the encoder. To make full use of the knowledge learned in the pre-training
stage, we design a new finetune mode for the proposed Point-RAE. Extensive
experiments demonstrate that our approach is efficient during pre-training and
generalizes well on various downstream tasks. Specifically, our pre-trained
models achieve a high accuracy of \textbf{90.28\%} on the ScanObjectNN hardest
split and \textbf{94.1\%} accuracy on ModelNet40, surpassing all the other
self-supervised learning methods. Our code and pretrained model are public
available at: \url{https://github.com/liuyyy111/Point-RAE}.
