PCL_OPENNI_MLS_SMOOTHING(1)

✨ NAME ✨

pcl_openni_mls_smoothing — bestie, it's literally just pcl_openni_mls_smoothing 💅 (this is a User Commands page for the pcl_openni_mls_smoothing command, section 1, iykyk)

🌸 DESCRIPTION 🌸

okay so here's the vibe. the way you run this is:

usage: pcl_openni_mls_smoothing <device_id> <options>

and the options are, like, the whole personality of this program 🫶:

• -search_radius X — this is the sphere radius used for finding the k-nearest neighbors that get used for the fitting. default is 0. it's giving "how big is your bubble" energy 🎀
• -sqr_gauss_param X — the parameter used for the distance based weighting of neighbors. the recommendation? search_radius^2. so like, square it, bestie, no cap ✨ (default: 0)
• -use_polynomial_fit X — decides whether the surface and the normal get approximated using a polynomial, or only via tangent estimation. default is 0 💀
• -polynomial_order X — the order of the polynomial that gets fit. lowkey important: this implicitly sets use_polynomial_fit = 1. default is 2 👑

not me reminding you that the defaults are literally all 0 except the polynomial order... the vibe check says double-check your flags before you come for me 🙈

👑 AUTHOR 👑

pcl_openni_mls_smoothing is part of the 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. iconic behavior fr fr 💖

this manual page was written by Leopold Palomo-Avellaneda <leo@alaxarxa.net> with the help of the help2man tool and some handmade arrangement for the Debian project (and may be used by others). we stan a documented legend 🫶✨

and that's a wrap, bestie 🎀 this was pcl_openni_mls_smoothing version 1.7.1, dated May 2014, living its life as PCL_OPENNI_MLS_SMOOTHING(1) in the User Commands section. slay, periodt 💅🦋