  Masked autoencoder has demonstrated its effectiveness in self-supervised
point cloud learning. Considering that masking is a kind of corruption, in this
work we explore a more general denoising autoencoder for point cloud learning
(Point-DAE) by investigating more types of corruptions beyond masking.
Specifically, we degrade the point cloud with certain corruptions as input, and
learn an encoder-decoder model to reconstruct the original point cloud from its
corrupted version. Three corruption families (\ie, density/masking, noise, and
affine transformation) and a total of fourteen corruption types are
investigated with traditional non-Transformer encoders. Besides the popular
masking corruption, we identify another effective corruption family, \ie,
affine transformation. The affine transformation disturbs all points globally,
which is complementary to the masking corruption where some local regions are
dropped. We also validate the effectiveness of affine transformation corruption
with the Transformer backbones, where we decompose the reconstruction of the
complete point cloud into the reconstructions of detailed local patches and
rough global shape, alleviating the position leakage problem in the
reconstruction. Extensive experiments on tasks of object classification,
few-shot learning, robustness testing, part segmentation, and 3D object
detection validate the effectiveness of the proposed method. The codes are
available at \url{https://github.com/YBZh/Point-DAE}.
