为任意拓扑3D网格设计高效卷积与池化操作
MeshConv3D: Efficient convolution and pooling operators for triangular 3D meshes
- 专为三角形网格设计卷积与面坍缩池化算子
- 在三个数据集上实现媲美或超越现有方法的分类精度
- 无需重网格化，内存占用和计算量显著降低
卷积神经网络在2D图像分析中扮演关键角色，包括计算机视觉、图像索引与检索及语义分类。将CNN扩展到点云和3D网格等3D数据面临巨大挑战，因基本卷积与池化操作需针对不规则连接性重新定义。本文提出MeshConv3D，一种专用于3D网格的方法，集成专用卷积与基于面坍缩的池化算子。该方法直接作用于任意拓扑的网格，无需预先重网格化或转换。为验证方法有效性，我们进行了语义分类任务实验。在三个不同基准数据集上的结果表明，所提方法在保持或超越现有性能的同时，显著降低内存占用与计算开销。
原文摘要 · Abstract (English)
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.
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