Analysis of the 3D Texture is indispensable for various tasks, such as retrieval, segmentation, classification, and inspection of sculptures, knitted fabrics, and biological tissues. A 3D texture is a locally repeated surface variation independent of the surface's overall shape and can be determined using the local neighborhood and its characteristics. Existing techniques typically employ computer vision techniques that analyze a 3D mesh globally, derive features, and then utilize the obtained features for retrieval or classification. Several traditional and learning-based methods exist in the literature, however, only a few are on 3D texture, and nothing yet, to the best of our knowledge, on the unsupervised schemes. This paper presents an original framework for the unsupervised segmentation of the 3D texture on the mesh manifold. We approach this problem as binary surface segmentation, partitioning the mesh surface into textured and non-textured regions without prior annotation. We devise a mutual transformer-based system comprising a label generator and a cleaner. The two models take geometric image representations of the surface mesh facets and label them as texture or non-texture across an iterative mutual learning scheme. Extensive experiments on three publicly available datasets with diverse texture patterns demonstrate that the proposed framework outperforms standard and SOTA unsupervised techniques and competes reasonably with supervised methods.

翻译：三维纹理分析在雕塑、针织织物及生物组织的检索、分割、分类与检测等任务中不可或缺。三维纹理是独立于曲面整体形状的局部重复表面变化，可通过局部邻域及其特征进行判定。现有方法通常采用计算机视觉技术对三维网格进行全局分析、提取特征，并利用所得特征进行检索或分类。文献中虽存在多种传统与基于学习的方法，但专门针对三维纹理的研究较少，据我们所知，尚无关于无监督方案的研究。本文提出了一种原创框架，用于在网格流形上实现三维纹理的无监督分割。我们将该问题视为二元表面分割任务，无需先验标注即可将网格表面划分为纹理与非纹理区域。我们设计了一个由标签生成器与清洁器组成的互Transformer系统：两个模型以几何图像形式表征网格面片，并通过迭代互学习方案逐面标注纹理或非纹理。在三个包含不同纹理模式的公开数据集上的大量实验表明，所提框架优于标准及最新无监督技术，并与有监督方法表现相当。