We present a method for reconstructing triangle meshes from point clouds. Existing learning-based methods for mesh reconstruction mostly generate triangles individually, making it hard to create manifold meshes. We leverage the properties of 2D Delaunay triangulations to construct a mesh from manifold surface elements. Our method first estimates local geodesic neighborhoods around each point. We then perform a 2D projection of these neighborhoods using a learned logarithmic map. A Delaunay triangulation in this 2D domain is guaranteed to produce a manifold patch, which we call a Delaunay surface element. We synchronize the local 2D projections of neighboring elements to maximize the manifoldness of the reconstructed mesh. Our results show that we achieve better overall manifoldness of our reconstructed meshes than current methods to reconstruct meshes with arbitrary topology.

翻译：我们提出了一个从点云重建三角间贝的方法。 现有的基于学习的网目重建方法大多产生单个三角, 很难创建多个网目。 我们利用 2D Delaunay 三角对流的特性来用多面元素构建网格。 我们的方法首先估计了每个点周围的本地大地测量区。 然后我们用一个有学识的对数图对这些社区进行 2D 投影。 这个 2D 域的Delaunay 三角对流保证产生一个多块, 我们称之为Delaunay 表面元素。 我们同步了相邻元素的本地 2D 预测, 以尽量扩大重建的网格的多重性。 我们的结果显示, 我们重建的网格比当前用任意的地貌来重建网格的网格方法, 实现了更好的整体多面。