[OcCo] Unsupervised Point Cloud Pre-Training
via Occlusion Completion
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 re- construct 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 ac- curacy across different datasets and encoders, on a wide range of downstream tasks. Specifically, we show that our method outperforms previous pre-training methods in ob- ject classification, and both part-based and semantic seg- mentation tasks. We study the pre-trained features and find that they lead to wide downstream minima, have high trans- formation invariance, and have activations that are highly correlated with part labels.