In this work, we present a novel scheme of masked autoencoders for point cloud self-supervised learning, termed as Point-MAE. Our Point-MAE is neat and efficient, with minimal modifications based on the properties of the point cloud. In classification tasks, Point-MAE outperforms all the other self-supervised learning methods on ScanObjectNN and ModelNet40. Point-MAE also advances state-of-the-art accuracies by 1.5%-2.3% in the few-shot learning on ModelNet40.
@@ -9,7 +12,7 @@ In this work, we present a novel scheme of masked autoencoders for point cloud s
Our codes are build upon [Point-BERT](https://github.com/lulutang0608/Point-BERT), [Pointnet2_PyTorch](https://github.com/erikwijmans/Pointnet2_PyTorch) and [Pointnet_Pointnet2_pytorch](https://github.com/yanx27/Pointnet_Pointnet2_pytorch)
Our codes are built upon [Point-BERT](https://github.com/lulutang0608/Point-BERT), [Pointnet2_PyTorch](https://github.com/erikwijmans/Pointnet2_PyTorch) and [Pointnet_Pointnet2_pytorch](https://github.com/yanx27/Pointnet_Pointnet2_pytorch)
## Reference
```
@misc{pang2022masked,
title={Masked Autoencoders for Point Cloud Self-supervised Learning},
author={Yatian Pang and Wenxiao Wang and Francis E. H. Tay and Wei Liu and Yonghong Tian and Li Yuan},
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