论文标题
具有基于注意的自动编码器的多尺度网格变形组件分析
Multiscale Mesh Deformation Component Analysis with Attention-based Autoencoders
论文作者
论文摘要
变形组件分析是几何处理和形状理解中的基本问题。现有方法主要以相似的规模提取本地区域中的变形组件，而现实世界对象的变形通常以多尺度方式分布。在本文中，我们提出了一种新型方法，以自动使用基于注意的自动编码器自动确切的多尺度变形组件。注意机制旨在学会在主动变形区中轻度重量多尺度变形组件，并且学会了基于注意力的自动编码器，以表示不同尺度上的变形组件。定量和定性评估表明，我们的方法优于最先进的方法。此外，使用我们方法提取的多尺度变形组件，用户可以以粗到精细的方式编辑形状，从而有助于新形状的有效建模。
Deformation component analysis is a fundamental problem in geometry processing and shape understanding. Existing approaches mainly extract deformation components in local regions at a similar scale while deformations of real-world objects are usually distributed in a multi-scale manner. In this paper, we propose a novel method to exact multiscale deformation components automatically with a stacked attention-based autoencoder. The attention mechanism is designed to learn to softly weight multi-scale deformation components in active deformation regions, and the stacked attention-based autoencoder is learned to represent the deformation components at different scales. Quantitative and qualitative evaluations show that our method outperforms state-of-the-art methods. Furthermore, with the multiscale deformation components extracted by our method, the user can edit shapes in a coarse-to-fine fashion which facilitates effective modeling of new shapes.
