  Masked auto-encoding is a popular and effective self-supervised learning
approach to point cloud learning. However, most of the existing methods
reconstruct only the masked points and overlook the local geometry information,
which is also important to understand the point cloud data. In this work, we
make the first attempt, to the best of our knowledge, to consider the local
geometry information explicitly into the masked auto-encoding, and propose a
novel Masked Surfel Prediction (MaskSurf) method. Specifically, given the input
point cloud masked at a high ratio, we learn a transformer-based
encoder-decoder network to estimate the underlying masked surfels by
simultaneously predicting the surfel positions (i.e., points) and per-surfel
orientations (i.e., normals). The predictions of points and normals are
supervised by the Chamfer Distance and a newly introduced Position-Indexed
Normal Distance in a set-to-set manner. Our MaskSurf is validated on six
downstream tasks under three fine-tuning strategies. In particular, MaskSurf
outperforms its closest competitor, Point-MAE, by 1.2\% on the real-world
dataset of ScanObjectNN under the OBJ-BG setting, justifying the advantages of
masked surfel prediction over masked point cloud reconstruction. Codes will be
available at https://github.com/YBZh/MaskSurf.
