MGM-AE: Self-Supervised Learning on 3D Shape Using Mesh Graph Masked Autoencoders

Abstract

The challenges of applying self-supervised learning to 3D mesh data include difficulties in explicitly modeling and leveraging geometric topology information and designing appropriate pretext tasks and augmentation methods for irregular mesh topology. In this paper, we propose a novel approach for pre-training models on large-scale, unlabeled datasets using graph masking on a mesh graph composed of faces. Our method, Mesh Graph Masked Autoencoders (MGM-AE), utilizes masked autoencoding to pre-train the model and extract important features from the data. Our pre-trained model outperforms prior state-of-the-art mesh encoders in shape classification and segmentation benchmarks, achieving 90.8% accuracy on ModelNet40 and 78.5 mIoU on ShapeNet. The best performance is obtained when the model is trained and evaluated under different masking ratios. Our approach demonstrates effectiveness in pretraining models on large-scale, unlabeled datasets and its potential for improving performance on downstream tasks.