Normal estimation on manifolds by gradient learning

Normal estimation is an important topic for processing point cloud data and surface reconstruction in computer graphics. In this paper we consider the problem of estimating normals for a (unknown) submanifold of a Euclidean space of codimension 1 from random points on the manifold. We propose a kernel-based learning algorithm in an unsupervised form of gradient learning. The algorithm can be implemented by solving a linear algebra problem. Error analysis is conducted under conditions on the true normals of the manifold and the sampling distribution. © 2010 John Wiley & Sons, Ltd.