论文标题
学习网状重建的Delaunay表面元素
Learning Delaunay Surface Elements for Mesh Reconstruction
论文作者
论文摘要
我们提出了一种从点云重建三角形网格的方法。现有的基于学习的网格重建方法主要生成三角形，因此很难创建歧管网格。我们利用2D Delaunay三角剖分的性质从歧管表面元素中构造网格。我们的方法首先估计每个点附近的本地测量社区。然后，我们使用学习的对数图对这些社区进行2D投影。保证在此2D域中的Delaunay三角剖分会产生一个歧管贴片，我们称之为Delaunay表面元素。我们同步相邻元素的局部2D投影，以最大化重建的网格的多种多样。我们的结果表明，与当前与任意拓扑结构的网格相比，我们实现了重建网格的总体多种多样性。我们的代码，数据和预算模型可以在线找到：https：//github.com/mrakotosaon/dse-meshing
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. Our code, data and pretrained models can be found online: https://github.com/mrakotosaon/dse-meshing
