MeshODE：一个鲁棒的可扩展网格变形框架
MeshODE: A Robust and Scalable Framework for Mesh Deformation
摘要：
我们提出了MeshODE，一个可扩展的和强大的框架，成对的CAD模型变形没有预先指定的对应关系。给定一对形状，我们的框架提供了一种新的形状特征保持映射函数，该函数通过基于非刚性迭代最近点（ICP）算法最小化拟合和刚性损失来使一个模型连续变形到另一个模型。在这个问题中，我们解决两个挑战，即设计一个强大的变形功能，并获得一个功能保持CAD变形。虽然传统的变形直接优化的网格顶点或控制笼的顶点的坐标，我们引入了一个深双射映射，利用流模型参数化的神经网络。我们的功能有能力处理复杂的变形，产生的变形，保证没有自相交，并需要低刚度约束的几何形状保存，这导致更好的拟合质量相比，现有的方法。它还可以在两个任意形状之间连续变形，而无需监督中间形状。此外，我们提出了一个强大的预处理流水线的原始CAD网格使用特征感知细分和统一的图形模板表示，以解决工件的原始CAD模型，包括自相交，不规则三角形，拓扑断开组件，非流形边缘，和非均匀分布的顶点。这有助于快速变形优化过程，保留全局和局部细节。我们的代码是公开的。
英文摘要：
We present MeshODE, a scalable and robust framework for pairwise CAD model deformation without prespecified correspondences. Given a pair of shapes, our framework provides a novel shape feature-preserving mapping function that continuously deforms one model to the other by minimizing fitting and rigidity losses based on the non-rigid iterative-closest-point (ICP) algorithm. We address two challenges in this problem, namely the design of a powerful deformation function and obtaining a feature-preserving CAD deformation. While traditional deformation directly optimizes for the coordinates of the mesh vertices or the vertices of a control cage, we introduce a deep bijective mapping that utilizes a flow model parameterized as a neural network. Our function has the capacity to handle complex deformations, produces deformations that are guaranteed free of self-intersections, and requires low rigidity constraining for geometry preservation, which leads to a better fitting quality compared with existing methods. It additionally enables continuous deformation between two arbitrary shapes without supervision for intermediate shapes. Furthermore, we propose a robust preprocessing pipeline for raw CAD meshes using feature-aware subdivision and a uniform graph template representation to address artifacts in raw CAD models including self-intersections, irregular triangles, topologically disconnected components, non-manifold edges, and nonuniformly distributed vertices. This facilitates a fast deformation optimization process that preserves global and local details. Our code is publicly available.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
