SCMS-Net: Self-Supervised Clustering-Based 3D Meshes Segmentation Network

The superior performance of deep learning in different domains has sparked significant interest in its applicability to 3D computer graphics. Deep learning has become the dominant technical architecture in current 3D mesh segmentation. However, learning-based 3D segmentation methods usually rely on high-quality training datasets, which are not readily available in practical applications. How to segment 3D meshes without exhaustive label annotations remains a challenging problem, especially in the context of deep learning. As a subset of unsupervised learning methods, self-supervised learning offers a promising learning paradigm for unlabeled 3D mesh segmentation. In this paper, we introduce a self-supervised clustering-Based network specifically for the segmentation of label-free 3D meshes. Our self-supervised clustering-based 3D mesh segmentation network (SCMS-Net) employs a two-branch architecture to learn effective feature representation. The two branches are unified into an end-to-end framework using a self-supervised strategy. Finally, the label predictions of the parts are generated by iterative clustering. We conducted ablation studies and comparative experiments on a standard benchmark to demonstrate the effectiveness of our approach. (c) 2023 Elsevier Ltd. All rights reserved.

Automated summary

The superior performance of deep learning in various domains has generated significant interest in its applicability to 3D computer graphics. However, learning-based 3D segmentation methods struggle with the lack of high-quality training datasets in practical applications. This paper introduces a self-supervised clustering-based network for label-free 3D mesh segmentation, demonstrating its effectiveness through ablation studies and comparative experiments on a standard benchmark.