论文标题
从点云重建表面的有效迭代方法
An efficient iterative method for reconstructing surface from point clouds
论文作者
论文摘要
点云的表面重建是计算机视觉中许多应用中的基本步骤。在本文中，我们在点云的表面重建的变分模型上开发了一种有效的迭代方法。表面由指标函数隐式表示，然后基于热内内卷积的这种表示，能量函数近似。然后，我们开发了一种新型的迭代方法，以最大程度地减少近似能量并证明每次迭代期间能量衰减特性。然后，我们使用渐近扩展，在提出的算法和主动轮廓模型之间提供连接。在2-维欧几里得空间中进行了广泛的数值实验，以表明所提出的方法简单，高效且准确。
Surface reconstruction from point clouds is a fundamental step in many applications in computer vision. In this paper, we develop an efficient iterative method on a variational model for the surface reconstruction from point clouds. The surface is implicitly represented by indicator functions and the energy functional is then approximated based on such representations using heat kernel convolutions. We then develop a novel iterative method to minimize the approximate energy and prove the energy decaying property during each iteration. We then use asymptotic expansion to give a connection between the proposed algorithm and active contour models. Extensive numerical experiments are performed in both 2- and 3- dimensional Euclidean spaces to show that the proposed method is simple, efficient, and accurate.
