Unstructured Point Cloud Surface Denoising and Decimation Using Distance RBF K-Nearest Neighbor Kernel

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:φ(S, pi(xi, yi, zi)), for the point set S : {pi}i∈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 semirigid 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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