In the field of 2D image generation modeling and representation learning,
Masked Generative Encoder (MAGE) has demonstrated the synergistic potential
between generative modeling and representation learning. Inspired by this, we
propose Point-MAGE to extend this concept to point cloud data. Specifically,
this framework first utilizes a Vector Quantized Variational Autoencoder
(VQVAE) to reconstruct a neural field representation of 3D shapes, thereby
learning discrete semantic features of point patches. Subsequently, by
combining the masking model with variable masking ratios, we achieve
synchronous training for both generation and representation learning.
Furthermore, our framework seamlessly integrates with existing point cloud
self-supervised learning (SSL) models, thereby enhancing their performance. We
extensively evaluate the representation learning and generation capabilities of
Point-MAGE. In shape classification tasks, Point-MAGE achieved an accuracy of
94.2% on the ModelNet40 dataset and 92.9% (+1.3%) on the ScanObjectNN dataset.
Additionally, it achieved new state-of-the-art performance in few-shot learning
and part segmentation tasks. Experimental results also confirmed that
Point-MAGE can generate detailed and high-quality 3D shapes in both
unconditional and conditional settings.

2D 图像生成建模，表示学习，Point-MAGE，点云数据，神经场表示。