# 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.