论文标题
Gamesh：引导和增强的网络网络网络
GAMesh: Guided and Augmented Meshing for Deep Point Networks
论文作者
论文摘要
我们提出了一种新的网格划分算法，称为指导和增强的网格划线Gamesh，该算法在为点网络的输出点生成表面之前使用网格。通过将输出点投影到此之前并简化了所得网格，Gamesh确保表面具有与先验的网格相同的拓扑，但其几何保真度由点网络控制。这使得GAMESH独立于输出点的密度和分布，这是传统表面重建算法中的常见伪像。我们表明，这种几何形状与拓扑之间的分离可以具有多个优点，尤其是在单视形状预测中，对点网络的公平评估以及对输出稀疏点云的网络的重建表面的重建表面。我们进一步表明，通过使用Gamesh的训练点网络，我们可以直接优化顶点位置，以生成具有任意拓扑的自适应网格。
We present a new meshing algorithm called guided and augmented meshing, GAMesh, which uses a mesh prior to generate a surface for the output points of a point network. By projecting the output points onto this prior and simplifying the resulting mesh, GAMesh ensures a surface with the same topology as the mesh prior but whose geometric fidelity is controlled by the point network. This makes GAMesh independent of both the density and distribution of the output points, a common artifact in traditional surface reconstruction algorithms. We show that such a separation of geometry from topology can have several advantages especially in single-view shape prediction, fair evaluation of point networks and reconstructing surfaces for networks which output sparse point clouds. We further show that by training point networks with GAMesh, we can directly optimize the vertex positions to generate adaptive meshes with arbitrary topologies.
