论文标题
从几个准确的2D对应到3D点云
From a few Accurate 2D Correspondences to 3D Point Clouds
论文作者
论文摘要
关键点，对应关系，投影矩阵，点云和密集云是基于图像的3D重建中的骨架，其中点云在生成3D重建对象的现实和自然模型方面具有重要作用。为了实现良好的3D重建，点云几乎必须是物体表面的任何地方。在本文中，我们的主要目的是构建覆盖物体整个表面的点云，我们提出了一个名为Geodesic特征或地理特征的新功能。基于新的地理特征，如果对象表面上有几个（给定的）初始世界点以及所有准确估计的投影矩阵，则将重建连接这些给定世界观点的任何两个的测量学上的一些新世界点。然后，与这些初始世界点接壤的表面上的区域将被点云覆盖。因此，如果最初的世界点位于表面围绕地面，则点云将覆盖整个表面。 本文提出了一种新方法，以估算世界的点和投影矩阵。该方法为世界点和投影矩阵提供了封闭形式和迭代的解决方案，并证明当世界点的数量少于七个，并且图像的数量至少五个，拟议的解决方案是全球最佳的。我们提出了一种算法从其对应关系（WPFC）的算法，以估算其对应关系的世界点和投影矩阵，以及另一种名为Creation Point Clouds（CRPC）的算法，以创建第一个算法给出的世界点和投影矩阵创建点云。
Key points, correspondences, projection matrices, point clouds and dense clouds are the skeletons in image-based 3D reconstruction, of which point clouds have the important role in generating a realistic and natural model for a 3D reconstructed object. To achieve a good 3D reconstruction, the point clouds must be almost everywhere in the surface of the object. In this article, with a main purpose to build the point clouds covering the entire surface of the object, we propose a new feature named a geodesic feature or geo-feature. Based on the new geo-feature, if there are several (given) initial world points on the object's surface along with all accurately estimated projection matrices, some new world points on the geodesics connecting any two of these given world points will be reconstructed. Then the regions on the surface bordering by these initial world points will be covered by the point clouds. Thus, if the initial world points are around the surface, the point clouds will cover the entire surface. This article proposes a new method to estimate the world points and projection matrices from their correspondences. This method derives the closed-form and iterative solutions for the world points and projection matrices and proves that when the number of world points is less than seven and the number of images is at least five, the proposed solutions are global optimal. We propose an algorithm named World points from their Correspondences (WPfC) to estimate the world points and projection matrices from their correspondences, and another algorithm named Creating Point Clouds (CrPC) to create the point clouds from the world points and projection matrices given by the first algorithm.
