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.

Classification

Ground/Unclassified

filters.csf

Label ground/non-ground returns using [Zhang2016].

filters.pmf

Label ground/non-ground returns using [Zhang2003].

filters.skewnessbalancing

Label ground/non-ground returns using [Bartels2010].

filters.smrf

Label ground/non-ground returns using [Pingel2013].

Noise

filters.elm

Marks low points as noise.

filters.outlier

Label noise points using either a statistical or radius outlier detection.

Consensus

filters.neighborclassifier

Update pointwise classification using k-nearest neighbor consensus voting.

Height Above Ground

filters.hag_delaunay

Compute pointwise height above ground using triangulation. Requires points to classified as ground/non-ground prior to estimating.

filters.hag_dem

Compute pointwise height above GDAL-readable DEM raster.

filters.hag_nn

Compute pointwise height above ground estimate. Requires points to be classified as ground/non-ground prior to estimating.

Colorization

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.

Clustering

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.dbscan

Perform Density-Based Spatial Clustering of Applications with Noise (DBSCAN) [Ester1996].

filters.litree

Segment and label individual trees. Returns a new dimension TreeID that indicates the tree that a point belongs to. TreeID starts at 1, wich non-tree points given a TreeID of 0. [Li2012].

filters.lloydkmeans

Perform K-means clustering using Lloyd’s algorithm. Returns a new dimension ClusterID with each point being assigned to a cluster. ClusterID starts at 0. [Lloyd1982].

Pointwise Features

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.covariancefeatures

Filter that calculates local features based on the covariance matrix of a point’s neighborhood.

filters.eigenvalues

Compute pointwise eigenvalues, based on k-nearest neighbors.

filters.estimaterank

Compute pointwise rank, based on k-nearest neighbors.

filters.lof

Compute pointwise Local Outlier Factor (along with K-Distance and Local Reachability Distance).

filters.miniball

Compute a criterion for point neighbors based on the miniball algorithm.

filters.nndistance

Compute a distance metric based on nearest neighbors.

filters.normal

Compute pointwise normal and curvature, based on k-nearest neighbors.

filters.optimalneighborhood

Compute optimal k nearest neighbors and corresponding radius by minimizing pointwise eigenentropy. Creates two new dimensions OptimalKNN and OptimalRadius.

filters.planefit

Compute a deviation of a point from a manifold approximating its neighbors.

filters.radialdensity

Compute pointwise density of points within a given radius.

filters.reciprocity

Compute the percentage of points that are considered uni-directional neighbors of a point.

Assignment

filters.assign

Assign values for a dimension range to a specified value.

filters.overlay

Assign values to a dimension based on the extent of an OGR-readable data source or an OGR SQL query.

Dimension Create/Copy

filters.ferry

Copy data from one dimension to another.

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.

Registration

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.

Predefined

filters.projpipeline

Apply coordinates operation on point triplets, based on PROJ pipeline string, WKT2 coordinates operations or URN definitions.

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.

Spatial

filters.crop

Filter points inside or outside a bounding box or a polygon

Resampling

filters.decimation

Keep every Nth point.

filters.fps

The Farthest Point Sampling Filter adds points from the input to the output PointView one at a time by selecting the point from the input cloud that is farthest from any point currently in the output.

filters.relaxationdartthrowing

Relaxation dart throwing is a hierarchical variant of Poisson disk sampling, shrinking the minimum radius between iterations until the target number of output points is achieved.

filters.sample

Perform Poisson sampling and return only a subset of the input points.

Conditional

filters.dem

Remove points that are in a raster cell but have a value far from the value of the raster.

filters.iqr

Cull points falling outside the computed Interquartile Range for a given dimension.

filters.mad

Cull points falling outside the computed Median Absolute Deviation for a given dimension.

Voxel

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.

filters.voxeldownsize

Retain either first point detected in each voxel or center of a populated voxel, depending on mode argument.

Position

filters.head

Return N points from beginning of the point cloud.

filters.locate

Return a single point with min/max value in the named dimension.

filters.mongo

Cull points using MongoDB-style expression syntax.

filters.range

Pass only points given a dimension/range.

filters.tail

Return N points from end of the point cloud.

New

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

Spatial

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.splitter

Split data based on a X/Y box length.

Dimension

filters.groupby

Split data categorically by dimension.

filters.returns

Split data by return order (e.g., ‘first’, ‘last’, ‘intermediate’, ‘only’).

filters.separatescanline

Split data based on scan lines.

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.poisson

Create mesh using the Poisson surface reconstruction algorithm [Kazhdan2006].

Languages

PDAL has three 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.python

Embed Python software in a pipeline.

filters.julia

Embed Julia software in a pipeline.

Other

filters.streamcallback

Provide a hook for a simple point-by-point callback.