Filters

Filters operate on data as inline operations. They can remove, modify, reorganize, and add points to the data stream as it goes by. Some filters can only operate on dimensions they understand (consider filters.reprojection doing geographic reprojection on XYZ coordinates), while others do not interrogate the point data at all and simply reorganize or split data.

Create

PDAL filters commonly create new dimensions (e.g., HeightAboveGround) or alter existing ones (e.g., Classification). These filters will not invalidate an existing KD-tree.

Note

We treat those filters that alter XYZ coordinates separately.

Note

When creating new dimensions, be mindful of the writer you are using and whether or not the custom dimension can be written to disk if that is the desired behavior.

filters.approximatecoplanar
Estimate pointwise planarity, based on k-nearest neighbors. Returns a new dimension Coplanar where a value of 1 indicates that a point is part of a coplanar neighborhood (0 otherwise).
filters.assign
Assign values for a dimension range to a specified value.
filters.cluster
Extract and label clusters using Euclidean distance metric. Returns a new dimension ClusterID that indicates the cluster that a point belongs to. Points not belonging to a cluster are given a cluster ID of 0.
filters.colorinterp
Assign RGB colors based on a dimension and a ramp
filters.colorization
Fetch and assign RGB color information from a GDAL-readable datasource.
filters.eigenvalues
Compute pointwise eigenvalues, based on k-nearest neighbors.
filters.estimaterank
Compute pointwise rank, based on k-nearest neighbors.
filters.elm
Marks low points as noise.
filters.ferry
Copy data from one dimension to another.
filters.hag
Compute pointwise height above ground estimate. Requires points to be classified as ground/non-ground prior to estimating.
filters.kdistance
Compute pointwise K-Distance (the Euclidean distance to a point’s k-th nearest neighbor). [Deprecated - use filters.nndistance]
filters.lof
Compute pointwise Local Outlier Factor (along with K-Distance and Local Reachability Distance).
filters.mongus
Label ground/non-ground returns using [Mongus2012].
filters.neighborclassifier
Update pointwise classification using k-nearest neighbor consensus voting.
filters.nndistance
Compute a distance metric based on nearest neighbors.
filters.normal
Compute pointwise normal and curvature, based on k-nearest neighbors.
filters.outlier
Label noise points using either a statistical or radius outlier detection.
filters.overlay
Assign values to a dimension based on the extent of an OGR-readable data source or an OGR SQL query.
filters.pmf
Label ground/non-ground returns using [Zhang2003].
filters.radialdensity
Compute pointwise density of points within a given radius.
filters.smrf
Label ground/non-ground returns using [Pingel2013].

Order

There are currently three PDAL filters that can be used to reorder points. These filters will invalidate an existing KD-tree.

filters.mortonorder
Sort XY data using Morton ordering (aka Z-order/Z-curve).
filters.randomize
Randomize points in a view.
filters.sort
Sort data based on a given dimension.

Move

PDAL filters that move XYZ coordinates will invalidate an existing KD-tree.

filters.cpd
Compute and apply transformation between two point clouds using the Coherent Point Drift algorithm.
filters.icp
Compute and apply transformation between two point clouds using the Iterative Closest Point algorithm.
filters.reprojection
Reproject data using GDAL from one coordinate system to another.
filters.transformation
Transform each point using a 4x4 transformation matrix.

Cull

Some PDAL filters will cull points, returning a point cloud that is smaller than the input. These filters will invalidate an existing KD-tree.

filters.crop
Filter points inside or outside a bounding box or a polygon
filters.decimation
Keep every Nth point.
filters.dem
Remove points that are in a raster cell but have a value far from the value of the raster.
filters.head
Return N points from beginning of the point cloud.
filters.iqr
Cull points falling outside the computed Interquartile Range for a given dimension.
filters.locate
Return a single point with min/max value in the named dimension.
filters.mad
Cull points falling outside the computed Median Absolute Deviation for a given dimension.
filters.range
Pass only points given a dimension/range.
filters.sample
Perform Poisson sampling and return only a subset of the input points.
filters.tail
Return N points from end of the point cloud.
filters.voxelcenternearestneighbor
Return the point within each voxel that is nearest the voxel center.
filters.voxelcentroidnearestneighbor
Return the point within each voxel that is nearest the voxel centroid.

New

PDAL filters can be used to split the incoming point cloud into subsets. These filters will invalidate an existing KD-tree.

filters.chipper
Organize points into spatially contiguous, squarish, and non-overlapping chips.
filters.divider
Divide points into approximately equal sized groups based on a simple scheme.
filters.groupby
Split data categorically by dimension.
filters.returns
Split data by return order (e.g., ‘first’, ‘last’, ‘intermediate’, ‘only’).
filters.splitter
Split data based on a X/Y box length.

Join

Multiple point clouds can be joined to form a single point cloud. These filters will invalidate an existing KD-tree.

filters.merge
Merge data from two different readers into a single stream.

Metadata

PDAL filters can be used to create new metadata. These filters will not invalidate an existing KD-tree.

Note

filters.cpd and filters.icp can optionally create metadata as well, inserting the computed transformation matrix.

filters.hexbin
Tessellate XY domain and determine point density and/or point boundary.
filters.info
Generate metadata about the point set, including a point count and spatial reference information.
filters.stats
Compute statistics about each dimension (mean, min, max, etc.).

Mesh

Meshes can be computed from point clouds. These filters will invalidate an existing KD-tree.

filters.delaunay
Create mesh using Delaunay triangulation.
filters.greedyprojection
Create mesh using the Greedy Projection Triangulation approach.
filters.gridprojection
Create mesh using the Grid Projection approach [Li2010].
filters.movingleastsquares
Data smoothing and normal estimation using the approach of [Alexa2003].
filters.poisson
Create mesh using the Poisson surface reconstruction algorithm [Kazhdan2006].

Languages

PDAL has two filters than can be used to pass point clouds to other languages. These filters will invalidate an existing KD-tree.

filters.matlab
Embed MATLAB software in a pipeline.
filters.pclblock
Embed select PCL filters in a pipeline.
filters.python
Embed Python software in a pipeline.

Other

filters.streamcallback
Provide a hook for a simple point-by-point callback.
filters.voxelgrid
Create a new point cloud composed of voxel centroids computed from the input point cloud. All incoming dimension data (e.g., intensity, RGB) will be lost.