Robust Normal Estimation in Unstructured 3D Point Clouds by Selective Normal Space Exploration

Abstract

We present a fast and practical approach for estimating robust normal vectors in unorganized point clouds. Our proposed technique is robust to noise and outliers and can preserve sharp features in the input model while being significantly faster than the current state-of-the-art alternatives. The key idea to this is a novel strategy for the exploration of the normal space: First, an initial candidate normal vector, optimal under a robust least median norm, is selected from a discrete subregion of this space, chosen conservatively to i Show more