The rank estimation filter uses singular value decomposition (SVD) to estimate the rank of a set of points. Point sets with rank 1 correspond to linear features, while sets with rank 2 correspond to planar features. Rank 3 corresponds to a full 3D feature. In practice this can be used alone, or possibly in conjunction with other filters to extract features (e.g., buildings, vegetation).

Two parameters are required to estimate rank (though the default values will be suitable in many cases). First, the knn parameter defines the number of points to consider when computing the SVD and estimated rank. Second, the thresh parameter is used to determine when a singular value shall be considered non-zero (when the absolute value of the singular value is greater than the threshold).

The rank estimation is performed on a pointwise basis, meaning for each point in the input point cloud, we find its knn neighbors, compute the SVD, and estimate rank. The filter creates a new dimension called Rank that can be used downstream of this filter stage in the pipeline. The type of writer used will determine whether or not the Rank dimension itself can be saved to disk.

Default Embedded Stage

This stage is enabled by default


This sample pipeline estimates the rank of each point using this filter and then filters out those points where the rank is three using filters.range.




The number of k-nearest neighbors. [Default: 8]


The threshold used to identify nonzero singular values. [Default: 0.01]


An expression that limits points passed to a filter. Points that don’t pass the expression skip the stage but are available to subsequent stages in a pipeline. [Default: no filtering]


A strategy for merging points skipped by a ‘where’ option when running in standard mode. If true, the skipped points are added to the first point view returned by the skipped filter. If false, skipped points are placed in their own point view. If auto, skipped points are merged into the returned point view provided that only one point view is returned and it has the same point count as it did when the filter was run. [Default: auto]