Estimating surface normals in noisy point cloud data.
Proceedings of the Annual Symposium on Computational Geometry
In this paper we describe and analyze a method based on local least square fitting for estimating the normals at all sample points of a point cloud data (PCD) set, in the presence of noise. We study the effects of neighborhood size, curvature, sampling density, and noise on the normal estimation when the PCD is sampled from a smooth curve in ℝ2 or a smooth surface in ℝ3 and noise is added. The analysis allows us to find the optimal neighborhood size using other local information from the PCD. Experimental results are also provided.
|Title:||Estimating surface normals in noisy point cloud data|
|Keywords:||Eigen analysis, Neighborhood size estimation, Noisy data, Normal estimation|
|UCL classification:||UCL > School of BEAMS > Faculty of Engineering Science
UCL > School of BEAMS > Faculty of Engineering Science > Computer Science
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