24.1.16. Point Cloud Data Management

Attention

Running these algorithms requires QGIS installed with PDAL >= 2.5.0 (see Help ► About menu).

24.1.16.1. Assign projection

Assigns a Coordinate Reference System to a point cloud layer, if it is missing or wrong. A new layer is created.

See also

Reproject point cloud

Parameters

Basic parameters

Label

Name

Type

Description

Input layer

INPUT

[point cloud]

Input point cloud layer to assign a CRS to

Desired CRS

CRS

[crs]

The CRS to apply to the layer

Output layer

OUTPUT

[point cloud]

Default: [Save to temporary file]

Specify the point cloud file to use as output. One of:

  • Save to a Temporary File

  • Save to File…

Advanced parameters

Label

Name

Type

Description

VPC Output Format

NEW in 4.0

VPC_OUTPUT_FORMAT

[enumeration]

Default: 0

Specify the underlying format in which data are stored for Virtual Point Cloud (.vpc) output. Possible formats are:

  • 0 — COPC

  • 1 — LAZ

  • 2 — LAS

LAZ/LAS may be faster to process, however they only allow rendering of the point cloud extents.

Outputs

Label

Name

Type

Description

Converted

OUTPUT

[point cloud]

Output point cloud layer with a new CRS. Currently supported formats are .LAS, .LAZ, .COPC.LAZ and .VPC.

Python code

Algorithm ID: pdal:assignprojection

import processing
processing.run("algorithm_id", {parameter_dictionary})

The algorithm id is displayed when you hover over the algorithm in the Processing Toolbox. The parameter dictionary provides the parameter NAMEs and values. See Using processing algorithms from the console for details on how to run processing algorithms from the Python console.

24.1.16.2. Build virtual point cloud (VPC)

Creates a virtual point cloud (VPC) from input point cloud data.

If you leave the optional parameters unchecked, the VPC file will be built very quickly as the algorithm will only read metadata of input files. With any of the optional parameters set, the algorithm will read all points which can take some time.

../../../../_images/point_cloud_buildvpc.png

Fig. 24.44 Generating Virtual Point Cloud with overview from a set of point cloud tiles

See also

Merge point cloud

Parameters

Label

Name

Type

Description

Input layers

LAYERS

[point cloud] [list]

Input point cloud layers to combine inside a virtual point cloud layer

Calculate boundary polygon

BOUNDARY

[boolean]

Default: False

Set to True to show the exact boundaries of data (rather than just rectangular extent)

Calculate statistics

STATISTICS

[boolean]

Default: False

Set to True to understand ranges of values of the various attributes

Build overview point cloud

OVERVIEW

[boolean]

Default: False

Generates a single “thinned” point cloud of all the input data (using only every 1000th point from original data).

The overview point cloud will be created next to the VPC file - for example, for mydata.vpc, the overview point cloud would be named mydata-overview.copc.laz.

Convert individual files to COPC format

NEW in 4.0

CONVERT_COPC

[boolean]

Default: False

When True, all the individual files in the virtual point cloud will also be converted to .COPC format to allow rendering of their points in QGIS.

When disabled, the format of each individual file will be preserved. This is faster, however only the extent will be rendered for files using formats other than .COPC and .EPT.

Virtual point cloud

OUTPUT

[point cloud]

Default: [Save to temporary file]

Specify the point cloud file to build the data into. One of:

  • Save to a Temporary File

  • Save to File…

Outputs

Label

Name

Type

Description

Virtual point cloud

OUTPUT

[raster]

Output point cloud layer combining all the input data, as a virtual file.

Python code

Algorithm ID: pdal:virtualpointcloud

import processing
processing.run("algorithm_id", {parameter_dictionary})

The algorithm id is displayed when you hover over the algorithm in the Processing Toolbox. The parameter dictionary provides the parameter NAMEs and values. See Using processing algorithms from the console for details on how to run processing algorithms from the Python console.

24.1.16.3. Classify ground points

NEW in 4.0

Classifies ground points using the Simple Morphological Filter (SMRF) algorithm. Analyses elevation differences and local surface structure to separate ground from non-ground points. Morphological filtering is applied to identify iteratively, considering various parameters such as cell size, slope, elevation threshold and window size.

Parameters

Basic parameters

Label

Name

Type

Description

Input layer

INPUT

[point cloud]

Input point cloud layer to classify.

Grid Cell Size

CELL_SIZE

[numeric: double]

Default: 1.0

Cell size for processing grid (in map units). Smaller values gives finer detail but may increase noise.

Scalar

SCALAR

[numeric: double]

Default: 1.25

Threshold for steeper slopes. Higher value if the terrain is rough, otherwise real ground might be misclassified.

Slope

SLOPE

[numeric: double]

Default: 0.15

Slope threshold (rise over run). How much slope is tolerated as ground. Should be higher for steep terrain.

Threshold

THRESHOLD

[numeric: double]

Default: 0.5

Elevation threshold for separating ground from objects. Higher values allow larger deviations from the ground.

Window Size

WINDOW_SIZE

[numeric: double]

Default: 18.0

Maximum filter window size. Higher values better identify large buildings or objects, smaller values protect smaller features.

Classified Ground

OUTPUT

[point cloud]

Default: [Save to temporary file]

Specify the point cloud file to export the classified ground points to. One of:

  • Save to a Temporary File

  • Save to File…

Advanced parameters

Label

Name

Type

Description

VPC Output Format

VPC_OUTPUT_FORMAT

[enumeration]

Default: 0

Specify the underlying format in which data are stored for Virtual Point Cloud (.VPC) output. Possible formats are:

  • 0 — COPC

  • 1 — LAZ

  • 2 — LAS

LAZ/LAS may be faster to process, however only allow rendering of the point cloud extents.

Outputs

Label

Name

Type

Description

Classified Ground

OUTPUT

[point cloud]

Output point cloud with ground classification applied.

Python code

Algorithm ID: pdal:classifyground

import processing
processing.run("algorithm_id", {parameter_dictionary})

The algorithm id is displayed when you hover over the algorithm in the Processing Toolbox. The parameter dictionary provides the parameter NAMEs and values. See Using processing algorithms from the console for details on how to run processing algorithms from the Python console.

24.1.16.4. Clip point cloud

Clips a point cloud layer by a polygon layer so that the resulting point cloud contains only points within the polygons.

../../../../_images/point_cloud_clip.png

Fig. 24.45 Clipping an input point cloud layer with a polygon coverage

Parameters

Basic parameters

Label

Name

Type

Description

Input layer

INPUT

[point cloud]

Input point cloud layer to clip

Clipping polygons

OVERLAY

[vector: polygon]

Polygon vector layer to use as coverage for clipping the points

Clipped

OUTPUT

[point cloud]

Default: [Save to temporary file]

Specify the point cloud file to export the clipped points to. One of:

  • Save to a Temporary File

  • Save to File…

Advanced parameters

Label

Name

Type

Description

Filter expression

Optional

FILTER_EXPRESSION

[expression]

A PDAL expression for selecting a subset of features in the point cloud data

Cropping extent

Optional

FILTER_EXTENT

[extent]

A map extent for selecting a subset of features in the point cloud data

Available methods are:

  • Calculate from layer…: uses extent of a layer loaded in the current project

  • Calculate from layout map…: uses extent of a layout map item in the active project

  • Calculate from bookmark…: uses extent of a saved bookmark

  • Use map canvas extent

  • Draw on canvas: click and drag a rectangle delimiting the area to take into account

  • Enter the coordinates as xmin, xmax, ymin, ymax

VPC Output Format

NEW in 4.0

VPC_OUTPUT_FORMAT

[enumeration]

Default: 0

Specify the underlying format in which data are stored for Virtual Point Cloud (.vpc) output. Possible formats are:

  • 0 — COPC

  • 1 — LAZ

  • 2 — LAS

LAZ/LAS may be faster to process, however they only allow rendering of the point cloud extents.

Outputs

Label

Name

Type

Description

Clipped

OUTPUT

[point cloud]

Output point cloud whose features are the points within the coverage polygon layer.

Python code

Algorithm ID: pdal:clip

import processing
processing.run("algorithm_id", {parameter_dictionary})

The algorithm id is displayed when you hover over the algorithm in the Processing Toolbox. The parameter dictionary provides the parameter NAMEs and values. See Using processing algorithms from the console for details on how to run processing algorithms from the Python console.

24.1.16.5. Compare point clouds

NEW in 4.0

Compares two point clouds using an M3C2 (Multiscale Model-to-Model Cloud Comparison) algorithm and outputs a subset (filtered using Poisson sampling, based on Subsampling cell size parameter) of the original point cloud.

The M3C2 algorithm calculates the distance between two point clouds by considering the local orientation of the surface. It estimates surface normals at a user-defined scale and measures the average distance between clouds within a cylindrical projection along those normals.

The approach is highly robust against sensor and surface roughness, making it the standard for detecting change in complex natural environments. It also provides a sign (indicating whether a surface has moved in or out) and a statistically significant level of detection to distinguish real change from measurement error.

The output point cloud will have several new dimensions: m3c2_distance, m3c2_uncertainty, m3c2_significant, m3c2_std_dev1, m3c2_std_dev2, m3c2_count1 and m3c2_count2.

References: Lague, Dimitri, Nicolas Brodu, and Jérôme Leroux. Accurate 3D Comparison of Complex Topography with Terrestrial Laser Scanner: Application to the Rangitikei Canyon (N-Z). arXiv, 2013, https://arxiv.org/abs/1302.1183.

Attention

Running this algorithm requires QGIS installed with PDAL >= 2.10 (see Help ► About menu).

Parameters

Basic parameters

Label

Name

Type

Description

Input layer

INPUT

[point cloud]

Input point cloud layer to compare

Compare layer

INPUT_COMPARE

[point cloud]

Point cloud to compare against

Subsampling cell size

SUBSAMPLING_CELL_SIZE

[numeric: double]

Default: 0.0

Minimum spacing between points (in map units).

Normal radius

NORMAL_RADIUS

[numeric: double]

Default: 2.0

Radius of the sphere around each core point that defines the neighbors from which normals are calculated.

Cylinder radius

CYLINDER_RADIUS

[numeric: double]

Default: 2.0

Radius of the cylinder inside of which points are searched for when calculating change.

Cylinder half-length

CYLINDER_HALF_LENGTH

[numeric: double]

Default: 2.0

The half-length of the cylinder of neighbors used for calculating change.

Registration error

REGISTRATION_ERROR

[numeric: double]

Default: 0.0

The estimated registration error between the two point clouds.

Cylinder orientation

CYLINDER_ORIENTATION

[enumeration]

Default: 0

Which direction to orient the cylinder/normal vector used for comparison between the two point clouds.

  • 0 — Up

  • 1 — Origin

  • 2 — None

Comparison point cloud

OUTPUT

[point cloud]

Default: [Save to temporary file]

Specify the point cloud file to export the comparison point cloud to. One of:

  • Save to a Temporary File

  • Save to File…

Advanced parameters

Label

Name

Type

Description

Filter expression

Optional

FILTER_EXPRESSION

[expression]

A PDAL expression for selecting a subset of features in the point cloud data

Cropping extent

Optional

FILTER_EXTENT

[extent]

A map extent for selecting a subset of features in the point cloud data

Available methods are:

  • Calculate from layer…: uses extent of a layer loaded in the current project

  • Calculate from layout map…: uses extent of a layout map item in the active project

  • Calculate from bookmark…: uses extent of a saved bookmark

  • Use map canvas extent

  • Draw on canvas: click and drag a rectangle delimiting the area to take into account

  • Enter the coordinates as xmin, xmax, ymin, ymax

Outputs

Label

Name

Type

Description

Comparison point cloud

OUTPUT

[point cloud]

Output comparison point cloud. The output point cloud contains the following additional dimensions:

  • m3c2_distance

  • m3c2_uncertainty

  • m3c2_significant

  • m3c2_std_dev1

  • m3c2_std_dev2

  • m3c2_count1

  • m3c2_count2

Python code

Algorithm ID: pdal:compare

import processing
processing.run("algorithm_id", {parameter_dictionary})

The algorithm id is displayed when you hover over the algorithm in the Processing Toolbox. The parameter dictionary provides the parameter NAMEs and values. See Using processing algorithms from the console for details on how to run processing algorithms from the Python console.

24.1.16.6. Create COPC

Creates the index for all the input point cloud files in a batch mode.

Parameters

Label

Name

Type

Description

Input layers

LAYERS

[point cloud] [list]

Input point cloud layers to create an index for

Output directory

Optional

OUTPUT

[folder]

Default: [Skip output]

Specify the folder to create the new files in. One of:

  • Skip Output

  • Save to a Temporary Directory

  • Save to Directory

Outputs

Label

Name

Type

Description

Output directory

OUTPUT

[folder]

Output folder containing point cloud layers with accompanying COPC index files.

Python code

Algorithm ID: pdal:createcopc

import processing
processing.run("algorithm_id", {parameter_dictionary})

The algorithm id is displayed when you hover over the algorithm in the Processing Toolbox. The parameter dictionary provides the parameter NAMEs and values. See Using processing algorithms from the console for details on how to run processing algorithms from the Python console.

24.1.16.7. Height above ground

NEW in 4.0

Calculates the height of points above the ground surface in a point cloud using a nearest neighbor algorithm. For each point, the algorithm finds the specified number of nearest ground-classified points (classification value 2) and interpolates the ground elevation from them using Inverse Distance Weighting (IDW). The output adds a HeightAboveGround dimension to the point cloud. If ‘Replace Z values’ is enabled, the Z coordinate will be replaced with the height above ground value. Maximum Distance parameter can limit the search radius (0 = no limit).

Parameters

Label

Name

Type

Description

Input layer

INPUT

[point cloud]

Input point cloud layer used to calculate height above ground.

Replace Z values with height above ground

REPLACE_Z

[boolean]

Default: True

If set to True, Z coordinate will be replaced with the height above ground value. Otherwise, the original Z values are kept.

Number of neighbors for terrain interpolation

COUNT

[numeric: integer]

Default: 1

The number of neighboring ground points used to interpolate the terrain elevation.

Maximum search distance

MAX_DISTANCE

[numeric: double]

Default: 0.0

Maximum distance used to limit the search radius. If set to 0, the search distance is unlimited.

Height above ground (nearest neighbour)

OUTPUT

[point cloud]

Default: [Save to temporary file]

Specify the point cloud file to export the point cloud with height above ground value. One of:

  • Save to a Temporary File

  • Save to File…

Advanced parameters

Label

Name

Type

Description

VPC Output Format

VPC_OUTPUT_FORMAT

[enumeration]

Default: 0

Specify the underlying format in which data are stored for Virtual Point Cloud (.vpc) output. Possible formats are:

  • 0 — COPC

  • 1 — LAZ

  • 2 — LAS

LAZ/LAS may be faster to process, however they only allow rendering of the point cloud extents.

Outputs

Label

Name

Type

Description

Height above ground (nearest neighbour)

OUTPUT

[point cloud]

Output point cloud layer with height above ground calculated for each point.

Python code

Algorithm ID: pdal:heightabovegroundbynearestneighbor

import processing
processing.run("algorithm_id", {parameter_dictionary})

The algorithm id is displayed when you hover over the algorithm in the Processing Toolbox. The parameter dictionary provides the parameter NAMEs and values. See Using processing algorithms from the console for details on how to run processing algorithms from the Python console.

24.1.16.8. Create tiles from point cloud

Creates tiles from input point cloud files, recommended for best performance (in display or analysis) with such datasets in QGIS.

See also

Build virtual point cloud (VPC), Create COPC

Parameters

Basic parameters

Label

Name

Type

Description

Input layers

LAYERS

[point cloud] [list]

Input point cloud layers to create tiles from

Tile length

LENGTH

[numeric: double]

Default: 1000.0

Size of the edge of each generated tile

Output directory

OUTPUT

[folder]

Default: [Save to temporary folder]

Specify the folder to store the generated tiles. One of:

  • Save to a Temporary Directory

  • Save to Directory

Advanced parameters

Label

Name

Type

Description

Assign CRS

Optional

CRS

[crs]

The CRS to apply to the layer

VPC Output Format

NEW in 4.0

VPC_OUTPUT_FORMAT

[enumeration]

Default: 0

Specify the underlying format in which data are stored for Virtual Point Cloud (.VPC) output. Possible formats are:

  • 0 — COPC

  • 1 — LAZ

  • 2 — LAS

LAZ/LAS may be faster to process, however they only allow rendering of the point cloud extents.

Outputs

Label

Name

Type

Description

Output directory

OUTPUT

[folder]

Output folder containing the tiles generated from input files.

Python code

Algorithm ID: pdal:tile

import processing
processing.run("algorithm_id", {parameter_dictionary})

The algorithm id is displayed when you hover over the algorithm in the Processing Toolbox. The parameter dictionary provides the parameter NAMEs and values. See Using processing algorithms from the console for details on how to run processing algorithms from the Python console.

24.1.16.9. Filter noise

NEW in 4.0

Filters noise in a point cloud using a statistical outlier removal algorithm. For each point, the algorithm computes the mean distance to its K nearest neighbors. Points whose mean distance exceeds a threshold (mean distance + multiplier × standard deviation) are classified as noise.

Parameters

Label

Name

Type

Description

Input layer

INPUT

[point cloud]

Input point cloud layer to be filtered for noise.

Remove noise points

REMOVE_NOISE_POINTS

[boolean]

Default: False

Set to True to remove points classified as noise from the output point cloud. If False, noise points are kept in the output but remain classified as noise.

Mean number of neighbors

MEAN_K

[numeric: integer]

Default: 8

Number of nearest neighbors (K) used to compute the average distance for each point.

Standard deviation multiplier

MULTIPLIER

[numeric: double]

Default: 2.0

Value used to multiply the standard deviation of the mean distances in order to compute the threshold for classifying points as noise. Higher values result in fewer points being classified as noise.

Filtered (statistical algorithm)

OUTPUT

[point cloud]

Default: [Save to temporary file]

Specify the point cloud file to export the filtered point cloud to. One of:

  • Save to a Temporary File

  • Save to File…

Advanced parameters

Label

Name

Type

Description

VPC Output Format

VPC_OUTPUT_FORMAT

[enumeration]

Default: 0

Specify the underlying format in which data are stored for Virtual Point Cloud (.vpc) output. Possible formats are:

  • 0 — COPC

  • 1 — LAZ

  • 2 — LAS

LAZ/LAS may be faster to process, however they only allow rendering of the point cloud extents.

Outputs

Label

Name

Type

Description

Filtered (statistical algorithm)

OUTPUT

[point cloud]

Output point cloud layer where points are classified as noise based on the statistical filter. If REMOVE_NOISE_POINTS is set to True, noise-classified points are removed from the output. Otherwise, they remain in the layer and can be identified by their classification value (typically LAS class 7).

Python code

Algorithm ID: pdal:filternoisestatistical

import processing
processing.run("algorithm_id", {parameter_dictionary})

The algorithm id is displayed when you hover over the algorithm in the Processing Toolbox. The parameter dictionary provides the parameter NAMEs and values. See Using processing algorithms from the console for details on how to run processing algorithms from the Python console.

24.1.16.10. Filter noise (using radius)

NEW in 4.0

Filters noise in a point cloud using radius algorithm. Points are marked as noise if they have fewer than the minimum number of neighbors within the specified radius.

Parameters

Label

Name

Type

Description

Input layer

INPUT

[point cloud]

Input point cloud layer to be filtered for noise.

Remove noise points

REMOVE_NOISE_POINTS

[boolean]

Default: False

Set to True to remove points classified as noise from the output point cloud. If False, noise points are kept in the output but remain classified as noise.

Minimum number of neighbors in radius

MIN_K

[numeric: integer]

Default: 2

Minimum number of neighboring points within the search radius.

Radius

RADIUS

[numeric: double]

Default: 1.0

Search radius for counting neighboring points.

Filtered (radius algorithm)

OUTPUT

[point cloud]

Default: [Save to temporary file]

Specify the point cloud file to export the filtered point cloud to. One of:

  • Save to a Temporary File

  • Save to File…

Advanced parameters

Label

Name

Type

Description

VPC Output Format

VPC_OUTPUT_FORMAT

[enumeration]

Default: 0

Specify the underlying format in which data are stored for Virtual Point Cloud (.vpc) output. Possible formats are:

  • 0 — COPC

  • 1 — LAZ

  • 2 — LAS

LAZ/LAS may be faster to process, however they only allow rendering of the point cloud extents.

Outputs

Label

Name

Type

Description

Filtered (radius algorithm)

OUTPUT

[point cloud]

Output point cloud layer where points are classified as noise based on the statistical filter. If REMOVE_NOISE_POINTS is set to True, noise-classified points are removed from the output. Otherwise, they remain in the layer and can be identified by their classification value (typically LAS class 7).

Python code

Algorithm ID: pdal:filternoiseradius

import processing
processing.run("algorithm_id", {parameter_dictionary})

The algorithm id is displayed when you hover over the algorithm in the Processing Toolbox. The parameter dictionary provides the parameter NAMEs and values. See Using processing algorithms from the console for details on how to run processing algorithms from the Python console.

24.1.16.11. Height above ground (using triangulation)

NEW in 4.0

Calculates the height of points above the ground surface in a point cloud using a Delaunay triangulation algorithm. The algorithm uses ground-classified points (classification value 2) to create a Triangulated Irregular Network (TIN) from specified number of neighbors, then computes the height above this surface for all points. The output adds a HeightAboveGround dimension to the point cloud. If ‘Replace Z values’ is enabled, the Z coordinate will be replaced with the height above ground value.

Parameters

Label

Name

Type

Description

Input layer

INPUT

[point cloud]

Input point cloud layer used to calculate height above ground.

Replace Z values with height above ground

REPLACE_Z

[boolean]

Default: True

If set to True, Z coordinate will be replaced with the height above ground value. Otherwise, the original Z values are kept.

Number of neighbors for terrain construction

COUNT

[numeric: integer]

Default: 10

The number of neighboring ground-classified points used to construct the terrain surface for elevation interpolation.

Height above ground (delaunay algorithm)

OUTPUT

[point cloud]

Default: [Save to temporary file]

Specify the point cloud file to export the point cloud with height above ground value. One of:

  • Save to a Temporary File

  • Save to File…

Advanced parameters

Label

Name

Type

Description

VPC Output Format

VPC_OUTPUT_FORMAT

[enumeration]

Default: 0

Specify the underlying format in which data are stored for Virtual Point Cloud (.vpc) output. Possible formats are:

  • 0 — COPC

  • 1 — LAZ

  • 2 — LAS

LAZ/LAS may be faster to process, however they only allow rendering of the point cloud extents.

Outputs

Label

Name

Type

Description

Height above ground (nearest neighbour)

OUTPUT

[point cloud]

Output point cloud layer with height above ground calculated for each point.

Python code

Algorithm ID: pdal:heightabovegroundtriangulation

import processing
processing.run("algorithm_id", {parameter_dictionary})

The algorithm id is displayed when you hover over the algorithm in the Processing Toolbox. The parameter dictionary provides the parameter NAMEs and values. See Using processing algorithms from the console for details on how to run processing algorithms from the Python console.

24.1.16.12. Merge point cloud

Merges multiple point cloud files into a single one.

See also

Build virtual point cloud (VPC)

Parameters

Basic parameters

Label

Name

Type

Description

Input layers

LAYERS

[point cloud] [list]

Input point cloud layers to merge into a single one

Merged

OUTPUT

[point cloud]

Default: [Save to temporary file]

Specify the output point cloud merging input files. One of:

  • Save to a Temporary File

  • Save to File…

Advanced parameters

Label

Name

Type

Description

Filter expression

Optional

FILTER_EXPRESSION

[expression]

A PDAL expression for selecting a subset of features in the point cloud data

Cropping extent

Optional

FILTER_EXTENT

[extent]

A map extent for selecting a subset of features in the point cloud data

Available methods are:

  • Calculate from layer…: uses extent of a layer loaded in the current project

  • Calculate from layout map…: uses extent of a layout map item in the active project

  • Calculate from bookmark…: uses extent of a saved bookmark

  • Use map canvas extent

  • Draw on canvas: click and drag a rectangle delimiting the area to take into account

  • Enter the coordinates as xmin, xmax, ymin, ymax

Outputs

Label

Name

Type

Description

Merged

OUTPUT

[point cloud]

Output point cloud layer merging all the input files.

Python code

Algorithm ID: pdal:merge

import processing
processing.run("algorithm_id", {parameter_dictionary})

The algorithm id is displayed when you hover over the algorithm in the Processing Toolbox. The parameter dictionary provides the parameter NAMEs and values. See Using processing algorithms from the console for details on how to run processing algorithms from the Python console.

24.1.16.13. Point cloud information

Outputs basic metadata from an input point cloud file.

Example of output information:

LAS           1.4
point format  6
count         56736130
scale         0.001 0.001 0.001
offset        431749.999 5440919.999 968.898
extent        431250 5440420 424.266
              432249.999 5441419.999 1513.531
crs           ETRS89 / UTM zone 34N (N-E) (EPSG:3046)  (vertical CRS missing!)
units         horizontal=metre  vertical=unknown

Attributes:
- X floating 8
- Y floating 8
- Z floating 8
- Intensity unsigned 2
- ReturnNumber unsigned 1
- NumberOfReturns unsigned 1
- ScanDirectionFlag unsigned 1
- EdgeOfFlightLine unsigned 1
- Classification unsigned 1
- ScanAngleRank floating 4
- UserData unsigned 1
- PointSourceId unsigned 2
- GpsTime floating 8
- ScanChannel unsigned 1
- ClassFlags unsigned 1

Parameters

Label

Name

Type

Description

Input layer

INPUT

[point cloud]

Input point cloud layer to extract metadata information from

Layer information

OUTPUT

[file]

Default: [Save to temporary file]

Specify the file to store the metadata information. One of:

  • Save to a Temporary File

  • Save to File…

Outputs

Label

Name

Type

Description

Layer information

OUTPUT

[html]

HTML file to store the metadata information.

Python code

Algorithm ID: pdal:info

import processing
processing.run("algorithm_id", {parameter_dictionary})

The algorithm id is displayed when you hover over the algorithm in the Processing Toolbox. The parameter dictionary provides the parameter NAMEs and values. See Using processing algorithms from the console for details on how to run processing algorithms from the Python console.

24.1.16.14. Reproject point cloud

Reprojects a point cloud to a different Coordinate Reference System (CRS).

See also

Assign projection

Parameters

Label

Name

Type

Description

Input layer

INPUT

[point cloud]

Input point cloud layer to reproject to a different CRS

Target CRS

CRS

[crs]

The CRS to apply to the layer

Reprojected

OUTPUT

[point cloud]

Default: [Save to temporary file]

Specify the reprojected point cloud file. One of:

  • Save to a Temporary File

  • Save to File…

Advanced parameters

Label

Name

Type

Description

Coordinate operation

Optional

OPERATION

[datum]

The datum transformation to use to reproject the data between the origin and target systems.

VPC Output Format

NEW in 4.0

VPC_OUTPUT_FORMAT

[enumeration]

Default: 0

Specify the underlying format in which data are stored for Virtual Point Cloud (.vpc) output. Possible formats are:

  • 0 — COPC

  • 1 — LAZ

  • 2 — LAS

LAZ/LAS may be faster to process, however they only allow rendering of the point cloud extents.

Outputs

Label

Name

Type

Description

Reprojected

OUTPUT

[point cloud]

Output point cloud layer in the target CRS.

Python code

Algorithm ID: pdal:reproject

import processing
processing.run("algorithm_id", {parameter_dictionary})

The algorithm id is displayed when you hover over the algorithm in the Processing Toolbox. The parameter dictionary provides the parameter NAMEs and values. See Using processing algorithms from the console for details on how to run processing algorithms from the Python console.

24.1.16.15. Thin (by sampling radius)

Creates a thinned version of the point cloud by performing sampling by distance point (reduces the number of points within a certain radius).

../../../../_images/point_cloud_thin.gif

Fig. 24.46 Thining point cloud (by sampling radius)

See also

Thin (by skipping points)

Parameters

Basic parameters

Label

Name

Type

Description

Input layer

INPUT

[point cloud]

Input point cloud layer to create a thinned version from

Sampling radius (in map units)

SAMPLING_RADIUS

[numeric: double]

Default: 1.0

Distance within which points are sampled to a unique point

Thinned (by radius)

OUTPUT

[point cloud]

Default: [Save to temporary file]

Specify the output point cloud with reduced points. One of:

  • Save to a Temporary File

  • Save to File…

Advanced parameters

Label

Name

Type

Description

Filter expression

Optional

FILTER_EXPRESSION

[expression]

A PDAL expression for selecting a subset of features in the point cloud data

Cropping extent

Optional

FILTER_EXTENT

[extent]

A map extent for selecting a subset of features in the point cloud data

Available methods are:

  • Calculate from layer…: uses extent of a layer loaded in the current project

  • Calculate from layout map…: uses extent of a layout map item in the active project

  • Calculate from bookmark…: uses extent of a saved bookmark

  • Use map canvas extent

  • Draw on canvas: click and drag a rectangle delimiting the area to take into account

  • Enter the coordinates as xmin, xmax, ymin, ymax

VPC Output Format

NEW in 4.0

VPC_OUTPUT_FORMAT

[enumeration]

Default: 0

Specify the underlying format in which data are stored for Virtual Point Cloud (.vpc) output. Possible formats are:

  • 0 — COPC

  • 1 — LAZ

  • 2 — LAS

LAZ/LAS may be faster to process, however they only allow rendering of the point cloud extents.

Outputs

Label

Name

Type

Description

Thinned (by radius)

OUTPUT

[point cloud]

Output point cloud layer with reduced points.

Python code

Algorithm ID: pdal:thinbyradius

import processing
processing.run("algorithm_id", {parameter_dictionary})

The algorithm id is displayed when you hover over the algorithm in the Processing Toolbox. The parameter dictionary provides the parameter NAMEs and values. See Using processing algorithms from the console for details on how to run processing algorithms from the Python console.

24.1.16.16. Thin (by skipping points)

Creates a thinned version of the point cloud by keeping only every N-th point (reduces the number of points by skipping nearby points).

See also

Thin (by sampling radius)

Parameters

Basic parameters

Label

Name

Type

Description

Input layer

INPUT

[point cloud]

Input point cloud layer to create a thinned version from

Number of points to skip

POINTS_NUMBER

[numeric: integer]

Default: 1

Keep only every N-th point in the input layer

Thinned (by decimation)

OUTPUT

[point cloud]

Default: [Save to temporary file]

Specify the output point cloud with reduced points. One of:

  • Save to a Temporary File

  • Save to File…

Advanced parameters

Label

Name

Type

Description

Filter expression

Optional

FILTER_EXPRESSION

[expression]

A PDAL expression for selecting a subset of features in the point cloud data

Cropping extent

Optional

FILTER_EXTENT

[extent]

A map extent for selecting a subset of features in the point cloud data

Available methods are:

  • Calculate from layer…: uses extent of a layer loaded in the current project

  • Calculate from layout map…: uses extent of a layout map item in the active project

  • Calculate from bookmark…: uses extent of a saved bookmark

  • Use map canvas extent

  • Draw on canvas: click and drag a rectangle delimiting the area to take into account

  • Enter the coordinates as xmin, xmax, ymin, ymax

VPC Output Format

NEW in 4.0

VPC_OUTPUT_FORMAT

[enumeration]

Default: 0

Specify the underlying format in which data are stored for Virtual Point Cloud (.vpc) output. Possible formats are:

  • 0 — COPC

  • 1 — LAZ

  • 2 — LAS

LAZ/LAS may be faster to process, however they only allow rendering of the point cloud extents.

Outputs

Label

Name

Type

Description

Thinned (by decimation)

OUTPUT

[point cloud]

Output point cloud layer with reduced points.

Python code

Algorithm ID: pdal:thinbydecimate

import processing
processing.run("algorithm_id", {parameter_dictionary})

The algorithm id is displayed when you hover over the algorithm in the Processing Toolbox. The parameter dictionary provides the parameter NAMEs and values. See Using processing algorithms from the console for details on how to run processing algorithms from the Python console.

24.1.16.17. Transform point cloud

NEW in 4.0

Transforms point cloud coordinates using translation, rotation, and scaling operations using a 4x4 transformation matrix. Applies transformations in the following order: scaling, rotation (using Euler angles), then translation. Rotation angles are specified in degrees around the X, Y, and Z axes. All parameters are combined into a 4×4 transformation matrix that is passed to PDAL Wrench.

Parameters

Label

Name

Type

Description

Input layer

INPUT

[point cloud]

Input point cloud layer to be transformed.

X Translation

TRANSLATE_X

[numeric: double]

Default: 0

Translation distance along X axis.

Y Translation

TRANSLATE_Y

[numeric: double]

Default: 0

Translation distance along Y axis.

Z Translation

TRANSLATE_Z

[numeric: double]

Default: 0

Translation distance along Z axis.

X Scale

SCALE_X

[numeric: double]

Default: 1.0

Scaling factor along X axis.

Y Scale

SCALE_Y

[numeric: double]

Default: 1.0

Scaling factor along Y axis.

Z Scale

SCALE_Z

[numeric: double]

Default: 1.0

Scaling factor along Z axis.

X Rotation

ROTATE_X

[numeric: double]

Default: 0

Rotation angle around X axis.

Y Rotation

ROTATE_Y

[numeric: double]

Default: 0

Rotation angle around Y axis.

Z Rotation

ROTATE_Z

[numeric: double]

Default: 0

Rotation angle around Z axis.

Transformed

OUTPUT

[point cloud]

Default: [Save to temporary file]

Specify the point cloud file to export the transformed point cloud to. One of:

  • Save to a Temporary File

  • Save to File…

Advanced parameters

Label

Name

Type

Description

VPC Output Format

VPC_OUTPUT_FORMAT

[enumeration]

Default: 0

Specify the underlying format in which data are stored for Virtual Point Cloud (.vpc) output. Possible formats are:

  • 0 — COPC

  • 1 — LAZ

  • 2 — LAS

LAZ/LAS may be faster to process, however they only allow rendering of the point cloud extents.

Outputs

Label

Name

Type

Description

Transformed

OUTPUT

[point cloud]

Output point cloud layer containing transformed point cloud.

Python code

Algorithm ID: pdal:transformpointcloud

import processing
processing.run("algorithm_id", {parameter_dictionary})

The algorithm id is displayed when you hover over the algorithm in the Processing Toolbox. The parameter dictionary provides the parameter NAMEs and values. See Using processing algorithms from the console for details on how to run processing algorithms from the Python console.