Abstract
In this work unstructured point clouds, resulting from 3D range acquisition are point wise-processed, using a proposed kd-tree nearest neighbor method, based in a generative data driven, local radial basis function’s (RBF) support:\(\phi(\textit{S},p_i(x_i,y_i,z_i))\), for the point set S:{p i } iε I , using surface statistic and a Gaussian convolution kernel, point sets are smoothed according to local surface features. As a minor contribution we also present a point cloud semi-rigid grid decimation method, based on a similar framework, using multi-core hardware, experiment results achieve comparable quality results with existing and more complex methods; time performance and results are presented for comparison.
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Morales, R., Wang, Y., Zhang, Z. (2010). Unstructured Point Cloud Surface Denoising and Decimation Using Distance RBF K-Nearest Neighbor Kernel. In: Qiu, G., Lam, K.M., Kiya, H., Xue, XY., Kuo, CC.J., Lew, M.S. (eds) Advances in Multimedia Information Processing - PCM 2010. PCM 2010. Lecture Notes in Computer Science, vol 6298. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-15696-0_20
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DOI: https://doi.org/10.1007/978-3-642-15696-0_20
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