This paper addresses the problem of reconstructing implicit function from point clouds with noise and outliers acquired with 3D scanners. We introduce a filtering operator based on mean shift scheme, which shift each point to local maximum of kernel density function, resulting in suppression of noise with different amplitudes and removal of outliers. The "clean" data points are then divided into subdomains using an adaptive octree subdivision method, and a local radial basis function is constructed at each octree leaf cell. Finally, we blend these local shape functions together with partition of unity to approximate the entire global domain. Numerical experiments demonstrate robust and high quality performance of the proposed method in processing a great variety of 3D reconstruction from point clouds containing noise and outliers. © Springer-Verlag Berlin Heidelberg 2007.
CITATION STYLE
Yang, J., Wang, Z., Zhu, C., & Peng, Q. (2007). Implicit surface reconstruction from scattered point data with noise. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 4488 LNCS, pp. 57–64). Springer Verlag. https://doi.org/10.1007/978-3-540-72586-2_8
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