Syntax is: pcl_mls_smoothing input.pcd output.pcd <options>
Moving Least Squares smoothing of a point cloud. For more information, use: pcl_mls_smoothing -h
where options are:
-radius X= sphere radius to be used for finding the k-nearest neighbors used for fitting (default: 0.000000)
-sqr_gauss_param X = parameter used for the distance based weighting of neighbors (recommended = search_radius^2) (default: 0.000000)
-use_polynomial_fit X = decides whether the surface and normal are approximated using a polynomial or only via tangent estimation (default: 0)
-polynomial_order X = order of the polynomial to be fit (implicitly, use_polynomial_fit = 1) (default: 2)
AUTHORpcl_mls_smoothing is part of Point Cloud Library (PCL) - www.pointclouds.org
The Point Cloud Library (PCL) is a standalone, large scale, open project for 2D/3D image and point cloud processing.
This manual page was written by Leopold Palomo-Avellaneda <[email protected]> with the help of help2man tool and some handmade arrangement for the Debian project (and may be used by others).