3D-TexSeg: Unsupervised Segmentation of 3D Texture using Mutual Transformer Learning

• URL: http://arxiv.org/abs/2311.10651v1
• Date: Fri, 17 Nov 2023 17:13:14 GMT
• Title: 3D-TexSeg: Unsupervised Segmentation of 3D Texture using Mutual Transformer Learning
• Authors: Iyyakutti Iyappan Ganapathi, Fayaz Ali, Sajid Javed, Syed Sadaf Ali, Naoufel Werghi
• Abstract summary: This paper presents an original framework for the unsupervised segmentation of the 3D texture on the mesh manifold. We devise a mutual transformer-based system comprising a label generator and a cleaner. Experiments on three publicly available datasets with diverse texture patterns demonstrate that the proposed framework outperforms standard and SOTA unsupervised techniques.
• Score: 11.510823733292519
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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.

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