  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.
