用3D网格卷积自动编码器实现无需预处理的几何压缩
A 3D mesh convolution-based autoencoder for geometry compression
- 直接从网格面学习特征，通过专用池化保持连接性
- 在多类别数据集上重建精度和潜空间分类均优于当前最佳
- 适合需要高效压缩不规则网格的场景
本文提出一种基于3D网格卷积的自动编码器用于几何压缩，可直接处理非规则网格数据，无需预处理或流形/封闭性条件。该方法通过学习网格面的特征提取有意义的潜在表示，并利用专门设计的池化与反池化操作保持拓扑连接性。编码器将输入网格压缩至紧凑的基网格空间，确保潜空间可比性；解码器恢复原始连接结构并重建全分辨率几何形状。在多类别数据集上的大量实验表明，本方法在3D网格几何重建和潜空间分类任务中均优于现有最优方法。代码已开源：github.com/germainGB/MeshConv3D
原文摘要 · Abstract (English)
In this paper, we introduce a novel 3D mesh convolution-based autoencoder for geometry compression, able to deal with irregular mesh data without requiring neither preprocessing nor manifold/watertightness conditions. The proposed approach extracts meaningful latent representations by learning features directly from the mesh faces, while preserving connectivity through dedicated pooling and unpooling operations. The encoder compresses the input mesh into a compact base mesh space, which ensures that the latent space remains comparable. The decoder reconstructs the original connectivity and restores the compressed geometry to its full resolution. Extensive experiments on multi-class datasets demonstrate that our method outperforms state-of-the-art approaches in both 3D mesh geometry reconstruction and latent space classification tasks. Code available at: github.com/germainGB/MeshConv3D
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