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Fastest three-dimensional (3D) surface reconstruction algorithms, from point clouds, require of the knowledge of the surface-normals. The accuracy, of state of the art methods, depends on the precision of estimated surface-normals. Surface-normals are estimated by assuming that the surface can be locally modelled by a plane as was proposed by Hoppe et. al [1]. Thus, current methods for estimating surface-normals are prone to introduce artifacts at the geometric edges or corners of the objects. In this paper an algorithm for Normal Estimation with Neighborhood Reorganization (NENR) is presented. Our proposal changes the characteristics of the neighborhood in places with corners or edges by assuming a locally plane piecewise surface. The results obtained by NENR improve the quality of the normal with respect to the state of the art algorithms. The new neighborhood computed by NENR, use only those points that belong to the same plane and they are the nearest neighbors. Experiments in synthetic and real data shown an improvement on the geometric edges of 3D reconstructed surfaces when our algorithm is used.

Surface–Normal Estimation with Neighborhood Reorganization for 3D Reconstruction