Classify Statistical Outliers

Classify Statistical Outlier is another type of noise classifier. This routine first creates a surface from a neighborhood of points (Neighbor Count). Then, residuals in respect to this surface are computed for each point. Any points with residuals that fall outside of the sigma specified (Sigma Multiplier) are assigned to the Output Class.

In the example below, any points with a residual larger than sigma 1.0 are assigned to the Low Point (noise) class:

This tool is ideal for cleaning high density data sets and for tightening up planar surfaces. In general, the Sigma Multiplier parameter controls how aggressively this tool classifies points as noise. Below are some example values:

Sigma Multiplier Value	Use-case
3.00	Will classify very few points as noise. Ideal for high-precision point clouds.
1.00	Will classify a moderate amount of points as noise, and generally a good starting point when experimenting with outlier removal.
0.5	Will classify about half of the data set as noise, and useful when attempting to drastically improve low precision data sets.