ADA-SCMS Net: A self-supervised clustering-based 3D mesh segmentation network with aggregation dual autoencoder

Abstract

Despite significant advances in 3D mesh segmentation techniques driven by deep learning, segmenting 3D meshes without exhaustive manual labeling remains a challenging due to difficulties in acquiring high-quality labeled datasets. This paper introduces an aggregation dual autoencoder self-supervised clustering-based mesh segmentation network for unlabeled 3D meshes (ADA-SCMS Net). Expanding upon the previously proposed SCMS-Net, the ADA-SCMS Net enhances the segmentation process by incorporating a denoising autoencoder with an improved graph autoencoder as its basic structure. This modification prompts the segmentation network to concentrate on the primary structure of the input data during training, enabling the capture of robust features. In addition, the ADA-SCMS network introduces two new modules. One module is named the branch aggregation module, which combines the strengths of two branches to create a semantic latent representation. The other is the aggregation self-supervised clustering module, which facilitates end-to-end clustering training by iteratively updating each branch through mutual supervision. Extensive experiments on benchmark datasets validate the effectiveness of the ADA-SCMS network, demonstrating superior segmentation performance compared to the SCMS network.