EP-MAE: A resource-efficient masked autoencoding framework for 3D neural representation learning

Self-supervised masked modeling has emerged as a powerful paradigm for learning general-purpose 3D representations without manual annotations. However, existing masked point modeling frameworks rely heavily on neighbor-search-based patch construction, dense token reconstruction, and heavyweight teacher networks, all of which introduce substantial computational overhead and hinder their scalability to large 3D datasets. In this work, we present Efficient Point Masked Autoencoders (EP-MAE), a new framework designed to significantly reduce the training cost of 3D self-supervised pre-training while maintaining strong representation quality. EP-MAE introduces three key components: (1) a serialization-based patch generator that transforms irregular point clouds into spatially coherent sequences, eliminating the quadratic complexity of kNN-based grouping; (2) a context-aware token dropout mechanism that selectively removes masked tokens with insufficient contextual support, reducing decoder input length without harming reconstruction quality; and (3) a lightweight momentum teacher that provides stable feature-space reconstruction targets at minimal computational cost. Together, these components yield a highly efficient masked modeling pipeline. Extensive experiments on standard 3D benchmarks demonstrate that EP-MAE achieves up to 4 ×  faster pre-training than state-of-the-art masked point modeling methods, while achieving equal or superior performance on downstream tasks. These results highlight the importance of structural efficiency in self-supervised 3D neural representation learning and show that EP-MAE provides a scalable and effective foundation for future 3D neural network models. Codes are available at https://github.com/linchengxing/epmae.