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.

该研究提出了一种原始框架，用于在网格流形上对 3D 纹理进行无监督分割，通过相互变换的系统将网格表面划分为纹理区域和非纹理区域，使用几何图像表示对网格面片进行标记，并在三个公开数据集上进行大量实验证明该框架优于标准和 SOTA 无监督技术，并且与有监督方法具有合理的竞争力。

3D-TexSeg: 基于互补式转换学习的 3D 纹理无监督分割

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

Prior work to infer 3D texture use either texture atlases, which require
uv-mappings and hence have discontinuities, or colored voxels, which are memory
inefficient and limited in resolution. Recent work, predicts RGB color at every
XYZ coordinate forming a texture field, but focus on completing texture given a
single 2D image. Instead, we focus on 3D texture and geometry completion from
partial and incomplete 3D scans. IF-Nets have recently achieved
state-of-the-art results on 3D geometry completion using a multi-scale deep
feature encoding, but the outputs lack texture. In this work, we generalize
IF-Nets to texture completion from partial textured scans of humans and
arbitrary objects. Our key insight is that 3D texture completion benefits from
incorporating local and global deep features extracted from both the 3D partial
texture and completed geometry. Specifically, given the partial 3D texture and
the 3D geometry completed with IF-Nets, our model successfully in-paints the
missing texture parts in consistence with the completed geometry. Our model won
the SHARP ECCV'20 challenge, achieving highest performance on all challenges.

本研究旨在实现通过 IF-Nets 模型进行基于局部和全局深度特征提取，从部分三维纹理和已完成几何体上完成 3D 纹理的完整。我们的模型成功地用靠近已完成几何体的内插法来处理丢失的纹理部分，并在 SHARP ECCV20 挑战中取得了最高表现。