Point-UMAE: Unet-like Masked Autoencoders for Point Cloud Self-supervised Learning

Masked Autoencoders (MAE) demonstrated exceptional performance in natural language processing and 2D vision tasks and have now been introduced into point cloud representation learning. We propose Point-UMAE, a novel self-supervised learning method based on a Unet-like structure, designed to enhance the capture of local details and global semantics in point clouds. Point-UMAE employs an asymmetric encoder-decoder architecture with a top-down fine-grained masking strategy to improve multi-scale consistency. The pre-trained model achieves state-of-the-art performance across various downstream tasks. Compared to the baseline Point-BERT, our method achieves a classification performance improvement of 1% and 4.14% on the ModelNet40 and ScanObjectNN datasets, respectively. We also investigate the impact of masking strategies and encoder structures on performance.