3D deep learning is a growing field of interest due to the vast amount of
information stored in 3D formats. Triangular meshes are an efficient
representation for irregular, non-uniform 3D objects. However, meshes are often
challenging to annotate due to their high geometrical complexity. Specifically,
creating segmentation masks for meshes is tedious and time-consuming.
Therefore, it is desirable to train segmentation networks with limited-labeled
data. Self-supervised learning (SSL), a form of unsupervised representation
learning, is a growing alternative to fully-supervised learning which can
decrease the burden of supervision for training. We propose SSL-MeshCNN, a
self-supervised contrastive learning method for pre-training CNNs for mesh
segmentation. We take inspiration from traditional contrastive learning
frameworks to design a novel contrastive learning algorithm specifically for
meshes. Our preliminary experiments show promising results in reducing the
heavy labeled data requirement needed for mesh segmentation by at least 33%.

提出了一种自监督对比学习方法 SSL-MeshCNN 用于预训练 CNNs 从而解决三角形网格难以准确定义的问题，该方法表现出降低网格分割所需标记数据的可行性