标题： MeshConv3D：三角形3D网格的高效卷积和池化操作符 显示英文标题

标题： MeshConv3D: Efficient convolution and pooling operators for triangular 3D meshes

摘要： 卷积神经网络（CNNs）在各种二维图像分析任务中起到了关键作用，包括计算机视觉、图像索引和检索或语义分类。 将CNN扩展到点云和3D网格等三维数据带来了重大挑战，因为基本的卷积和池化操作需要以适当的方式完全重新审视和重新定义，以解决不规则连接问题。 在本文中，我们介绍了MeshConv3D，这是一种专门针对3D网格的方法，集成了专用卷积和基于面坍塌的池化操作符。 MeshConv3D直接在任意拓扑的网格上运行，无需任何先验的重网格化/转换技术。 为了验证我们的方法，我们考虑了一个语义分类任务。 在三个不同的基准数据集上获得的实验结果表明，所提出的方法能够在最小化相关内存占用和计算负载的同时实现相当或更优的分类结果。 显示英文摘要

摘要： Convolutional neural networks (CNNs) have been pivotal in various 2D image analysis tasks, including computer vision, image indexing and retrieval or semantic classification. Extending CNNs to 3D data such as point clouds and 3D meshes raises significant challenges since the very basic convolution and pooling operators need to be completely re-visited and re-defined in an appropriate manner to tackle irregular connectivity issues. In this paper, we introduce MeshConv3D, a 3D mesh-dedicated methodology integrating specialized convolution and face collapse-based pooling operators. MeshConv3D operates directly on meshes of arbitrary topology, without any need of prior re-meshing/conversion techniques. In order to validate our approach, we have considered a semantic classification task. The experimental results obtained on three distinct benchmark datasets show that the proposed approach makes it possible to achieve equivalent or superior classification results, while minimizing the related memory footprint and computational load.