  In computer-aided design (CAD) community, the point cloud data is pervasively
applied in reverse engineering, where the point cloud analysis plays an
important role. While a large number of supervised learning methods have been
proposed to handle the unordered point clouds and demonstrated their remarkable
success, their performance and applicability are limited to the costly data
annotation. In this work, we propose a novel self-supervised pretraining model
for point cloud learning without human annotations, which relies solely on
upsampling operation to perform feature learning of point cloud in an effective
manner. The key premise of our approach is that upsampling operation encourages
the network to capture both high-level semantic information and low-level
geometric information of the point cloud, thus the downstream tasks such as
classification and segmentation will benefit from the pre-trained model.
Specifically, our method first conducts the random subsampling from the input
point cloud at a low proportion e.g., 12.5%. Then, we feed them into an
encoder-decoder architecture, where an encoder is devised to operate only on
the subsampled points, along with a upsampling decoder is adopted to
reconstruct the original point cloud based on the learned features. Finally, we
design a novel joint loss function which enforces the upsampled points to be
similar with the original point cloud and uniformly distributed on the
underlying shape surface. By adopting the pre-trained encoder weights as
initialisation of models for downstream tasks, we find that our UAE outperforms
previous state-of-the-art methods in shape classification, part segmentation
and point cloud upsampling tasks. Code will be made publicly available upon
acceptance.
