EP-MAE: A resource-efficient masked autoencoding framework for 3D neural representation learning.
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, and provides a scalable and effective foundation for future 3D neural network models is presented.