论文标题
facetwise网格精炼用于多视图立体
Facetwise Mesh Refinement for Multi-View Stereo
论文作者
论文摘要
网状细化是准确的多视图立体声的基本步骤。它修改了初始歧管网格的几何形状，以最大程度地减少一组相机对中引起的光度误差。该初始网格通常是基于Delaunay三角剖分的最小切割的体积3D重建的输出。这样的方法产生了大量的非Manifold顶点，因此它们需要一个顶点拆分步骤才能明确修复它们。在本文中，我们扩展了此方法，以直接在Delaunay三角剖分上推理并避免大多数顶点拆分，从而先发出非字母顶点。本文的主要贡献解决了选择改进过程采用的相机对的问题。我们将问题视为网状标记过程，每个标签都对应于相机对。与最先进的方法不同，该方法使用每个相机对来完善网格的所有可见部分，我们为每个方面选择实施整体可见性和覆盖范围的最佳对。仅使用相机对选择每个方面的改进步骤。这种方面的改进有助于以最均匀的方式应用过程。
Mesh refinement is a fundamental step for accurate Multi-View Stereo. It modifies the geometry of an initial manifold mesh to minimize the photometric error induced in a set of camera pairs. This initial mesh is usually the output of volumetric 3D reconstruction based on min-cut over Delaunay Triangulations. Such methods produce a significant amount of non-manifold vertices, therefore they require a vertex split step to explicitly repair them. In this paper, we extend this method to preemptively fix the non-manifold vertices by reasoning directly on the Delaunay Triangulation and avoid most vertex splits. The main contribution of this paper addresses the problem of choosing the camera pairs adopted by the refinement process. We treat the problem as a mesh labeling process, where each label corresponds to a camera pair. Differently from the state-of-the-art methods, which use each camera pair to refine all the visible parts of the mesh, we choose, for each facet, the best pair that enforces both the overall visibility and coverage. The refinement step is applied for each facet using only the camera pair selected. This facetwise refinement helps the process to be applied in the most evenly way possible.
