  We describe a simple pre-training approach for point clouds. It works in
three steps: 1. Mask all points occluded in a camera view; 2. Learn an
encoder-decoder model to reconstruct the occluded points; 3. Use the encoder
weights as initialisation for downstream point cloud tasks. We find that even
when we construct a single pre-training dataset (from ModelNet40), this
pre-training method improves accuracy across different datasets and encoders,
on a wide range of downstream tasks. Specifically, we show that our method
outperforms previous pre-training methods in object classification, and both
part-based and semantic segmentation tasks. We study the pre-trained features
and find that they lead to wide downstream minima, have high transformation
invariance, and have activations that are highly correlated with part labels.
Code and data are available at: https://github.com/hansen7/OcCo
