Denoise - Noise Removal
F6 SMART™ – Volumetric Handheld Camera – User Guide
SOR Outlier Coefficient
SOR perform as a noise cleaner by outlier coefficient, outlier is an observation point that
is distant from other observations. An outlier may be due to variability in the
measurement excluded from the data set.
the coefficient parameter removes outlier points based on distance from the tangent
plane, and doesn't change the position of existing points.
SOR outlier coefficient bar work as 1 will be closest to target and 10 will be farther to
target, for large scale scanning such as rooms it is better to use far number such as 6 or 8
as noise point will be far from plane.
Smoothing algorithm - MLS (Moving Least Squares)
Moving least squares is a method of reconstructing continuous functions from a set of
unorganized point samples via the calculation of a weighted least squares measure
biased towards the region around the point at which the reconstructed value is
requested.
Moving least squares method is useful for reconstructing a surface from a set of points,
by finding the average of the center, often it is used to create a 3D surface from a point
cloud through either down sampling or up sampling. MLS algorithm fits the
neighborhood of each point to a surface and projects the point on it.
MLS is not removing points but find a closest surface to each point and bring it closer to
the center of the surface and by that its smoothing the point cloud for a sharper mesh
results.
MLS Radius search
To approximate the point to surface defined by a local neighborhood of points p1, p2 …
pk at a point q we use a bivariate polynomial height function defined on a on a robustly
computed reference plane, the distance from those planes is the radius of the point
interpolation being performed.
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