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.

中文翻译：

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GAMesh：用于深点网络的引导和增强网格

我们提出了一种新的网格划分算法，称为引导和增强网格划分，GAMesh，它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这个先验上并简化生成的网格，GAMesh 确保表面具有与先验网格相同的拓扑，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影。我们表明，几何与拓扑的这种分离具有多种优势，尤其是在单视图形状预测、点网络的公平评估以及为输出稀疏点云的网络重建表面方面。我们进一步表明，通过使用 GAMesh 训练点网络，