There have been recent efforts to learn more meaningful representations via fixed length codewords from mesh data, since a mesh serves as a complete model of underlying 3D shape compared to a point cloud. However, the mesh connectivity presents new difficulties when constructing a deep learning pipeline for meshes. Previous mesh unsupervised learning approaches typically assume category-specific templates, e.g., human face/body templates. It restricts the learned latent codes to only be meaningful for objects in a specific category, so the learned latent spaces are unable to be used across different types of objects. In this work, we present WrappingNet, the first mesh autoencoder enabling general mesh unsupervised learning over heterogeneous objects. It introduces a novel base graph in the bottleneck dedicated to representing mesh connectivity, which is shown to facilitate learning a shared latent space representing object shape. The superiority of WrappingNet mesh learning is further demonstrated via improved reconstruction quality and competitive classification compared to point cloud learning, as well as latent interpolation between meshes of different categories.

翻译：近期研究致力于通过固定长度码字从网格数据中学习更具意义的表征，因为相较于点云，网格能完整地建模底层三维形状。然而，网格连接性为构建网格深度学习管线带来了新挑战。以往的网格无监督学习方法通常假设特定类别的模板（例如人脸/人体模板），这导致所学潜在编码仅对特定类别对象有意义，使得潜在空间无法跨不同类型对象使用。为此，本文提出首个支持异构对象通用网格无监督学习的自编码器WrappingNet。该模型在瓶颈层引入专用于表征网格连接性的新型基图，实验表明这有助于学习表征物体形状的共享潜在空间。通过相较于点云学习更优的重建质量与有竞争力的分类性能，以及跨类别网格的潜在插值，进一步验证了WrappingNet网格学习的优越性。