Extending the concept of texture to the geometry of a mesh manifold surface is an emerging topic in computer vision. This concept is different from gluing images to the surface, but rather indicates the presence of relief patterns that locally change the surface geometry, showing some regular and repetitive patterns. The representation and the analysis of such relief patterns have several potential applications. In this paper, we propose an original and comprehensive framework to address this novel task, which redefines a large variety of local binary patterns on the mesh manifold domain. We also propose an efficient mesh re-sampling technique that enables uniform surface tessellation. We assess the different descriptive variants derived with this framework in terms of uniformity, repeatability and discriminative power. Afterward, we conduct an extensive experimentation on different datasets showcasing the competitiveness of our framework in classification and retrieval tasks, in terms of both accuracy and computational complexity, with respect to state-of-the-art methods.

将纹理的概念扩展到网格流形表面的几何形状是计算机视觉中一个新兴的话题。这个概念不同于将图像粘贴到表面上，而是表明存在局部改变表面几何形状的浮雕图案，显示出一些规则的和重复的图案。这种浮雕图案的表示和分析具有多种潜在应用。在本文中，我们提出了一个原始而全面的框架来解决这一新任务，该框架在网格流形域上重新定义了多种本地二进制模式。我们还提出了一种有效的网格重采样技术，可实现均匀的曲面细分。我们从一致性，可重复性和判别力方面评估了使用此框架衍生的不同描述性变体。之后，