Robust Method for Estimating Normals on Point Clouds Using Adaptive Neighborhood Size

dc.contributor.authorLeal, E.A.en_US
dc.contributor.authorLeal, N.E.en_US
dc.contributor.editorSilva, F. and Gutierrez, D. and Rodríguez, J. and Figueiredo, M.en_US
dc.description.abstractNormal estimation on sampled curves or surfaces is a basic step of many algorithms in computer graphics, computer vision, and especially in recognition and reconstruction of three dimensional objects. This paper presents a simple and intuitive method for estimating normals on point based surfaces. The method is based on Robust Principal Component Analysis (RPCA) therefore is capable to deal with noisy data and outliers. In order to estimate an accurate normal on a point, our method takes a neighborhood of variable size around the point. The neighborhood size depends on local properties of the sampled surface. It is shown that the estimation of the tangent plane on a point is more accurate using a neighborhood of variable size than using a fixed one.en_US
dc.description.sectionheaders3D Modeling and Interaction
dc.description.seriesinformationV Ibero-American Symposium in Computer Graphics
dc.publisherThe Eurographics Associationen_US
dc.subjectI.3.3 [Computer Graphics]
dc.subjectLine and Curve Generation)
dc.titleRobust Method for Estimating Normals on Point Clouds Using Adaptive Neighborhood Sizeen_US
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