The underlying dynamics and patterns of 3D surface meshes deforming over time can be discovered by unsupervised learning, especially autoencoders, which calculate low-dimensional embeddings of the surfaces. To study the deformation patterns of unseen shapes by transfer learning, we want to train an autoencoder that can analyze new surface meshes without training a new network. Here, most state-of-the-art autoencoders cannot handle meshes of different connectivity and therefore have limited to no generalization capacities to new meshes. Also, reconstruction errors strongly increase in comparison to the errors for the training shapes. To address this, we propose a novel spectral CoSMA (Convolutional Semi-Regular Mesh Autoencoder) network. This patch-based approach is combined with a surface-aware training. It reconstructs surfaces not presented during training and generalizes the deformation behavior of the surfaces' patches. The novel approach reconstructs unseen meshes from different datasets in superior quality compared to state-of-the-art autoencoders that have been trained on these shapes. Our transfer learning errors on unseen shapes are 40% lower than those from models learned directly on the data. Furthermore, baseline autoencoders detect deformation patterns of unseen mesh sequences only for the whole shape. In contrast, due to the employed regional patches and stable reconstruction quality, we can localize where on the surfaces these deformation patterns manifest.
翻译:3D 表面介质随着时间的推移变形的内在动态和模式可以通过不受监督的学习来发现, 特别是自动校正器, 它计算了表层的低维嵌入。 为了通过转移学习来研究未知形状的变形模式, 我们想要训练一个自动校正器, 它可以在不训练新网络的情况下分析新的表面介质。 这里, 大多数最先进的自动校正器无法处理不同连接的模类, 因此只限于没有概括能力到新模件。 另外, 重建错误比培训形状的错误要大得多。 为了解决这个问题, 我们提议了一个新型的 COSMA( 革命半Repular Mesh Autoencoder) 模式。 这种基于补丁法的方法可以结合一种表面认知训练的训练。 它重建了在训练过程中没有显示的表面, 并概括了表面整块的变形行为。 新的方法只能从质量优于状态的直径直的模件重重来重建。 在这些模型上, 我们学习了40个地方级的直径方形的直径校正的模型。 我们学习了这些在这些模型上的变形变形。