Abstract
This article presents a novel method for estimating normals on unorganized point clouds that preserves sharp features. Many existing methods are unable to reliably estimate normals for points aroun...
Highlights
Reliable estimation of the normals of point clouds is a crucial preprocessing operation
A neighborhood growth strategy is proposed for each non-edge point to generate a neighborhood clear of edge points, which improves the reliability of normal estimation for non-edge point
The proposed method and LRR increase in small amplitude until the noise level is greater than 80%, and the proposed method achieves the lowest root mean square with threshold (RMS_t) and number of bad points (NBP) when the noise level is greater than 80%
Summary
Reliable estimation of the normals of point clouds is a crucial preprocessing operation. The methods estimate normal with the whole neighborhood centered at the point, which tend to smooth sharp features. A neighborhood reconstruction-based method is presented to estimate normals for unorganized point clouds with sharp features. The located neighborhood is used for more faithful normal estimation To this end, the points are first classified into edge points and non-edge points through robust statistics-based method. The experiments illustrate that the presented method can estimate normals accurately even in the presence of noise and anisotropic samplings, while preserving sharp features. The proposed method can accurately identify points near sharp features even in the presence of high level noise. A neighborhood growth strategy is proposed for each non-edge point to generate a neighborhood clear of edge points, which improves the reliability of normal estimation for non-edge point
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