论文标题
学习关节表面地图集
Learning Joint Surface Atlases
论文作者
论文摘要
本文介绍了学习3D表面类似地图集的表示的新技术，即从2D域到表面的同质形态转换。与先前的工作相比，我们提出了两个主要贡献。首先，我们没有通过优化作为高斯人的混合物来绘制固定的2D域（例如一组平方贴片）的固定2D域（例如一组平方斑）。其次，我们在两个方向上学习一致的映射：图表，从3D表面到2D域，以及参数化，它们的倒数。我们证明，这可以提高学到的表面表示的质量，及其在相关形状集合中的一致性。因此，它导致了应用程序的改进，例如对应估计，纹理传输和一致的UV映射。作为额外的技术贡献，我们概述了，尽管合并正常的一致性具有明显的好处，但它会导致优化问题，并且可以使用简单的排斥正则化来缓解这些问题。我们证明，与现有基线相比，我们的贡献提供了更好的表面表示。
This paper describes new techniques for learning atlas-like representations of 3D surfaces, i.e. homeomorphic transformations from a 2D domain to surfaces. Compared to prior work, we propose two major contributions. First, instead of mapping a fixed 2D domain, such as a set of square patches, to the surface, we learn a continuous 2D domain with arbitrary topology by optimizing a point sampling distribution represented as a mixture of Gaussians. Second, we learn consistent mappings in both directions: charts, from the 3D surface to 2D domain, and parametrizations, their inverse. We demonstrate that this improves the quality of the learned surface representation, as well as its consistency in a collection of related shapes. It thus leads to improvements for applications such as correspondence estimation, texture transfer, and consistent UV mapping. As an additional technical contribution, we outline that, while incorporating normal consistency has clear benefits, it leads to issues in the optimization, and that these issues can be mitigated using a simple repulsive regularization. We demonstrate that our contributions provide better surface representation than existing baselines.
