#include <pcl/pcl_macros.h>

Macro to add a no-op or a fallthrough attribute based on compiler feature.

Definition at line 433 of file pcl_macros.h.

#include <pcl/memory.h>

Macro to signal a class requires a custom allocator.

It's an implementation detail to have pcl::has_custom_allocator work, a thin wrapper over Eigen's own macro

See also

pcl::has_custom_allocator, pcl::make_shared

Definition at line 63 of file memory.h.

#include <pcl/point_types.h>

Data type to store extended information about a transition from foreground to backgroundSpecification of the fields for BorderDescription::traits.

Definition at line 307 of file point_types.h.

#include <pcl/point_types.h>

Specification of the fields for BorderDescription::traits.

Definition at line 312 of file point_types.h.

#include <pcl/common/norms.h>

Enum that defines all the types of norms available.

Note

Any new norm type should have its own enum value and its own case in the selectNorm () method

Definition at line 54 of file norms.h.

#include <pcl/common/impl/norms.hpp>

Compute the B norm of the vector between two points.

Parameters

A	the first point
B	the second point
dim	the number of dimensions in A and B (dimensions must match)

Note

FloatVectorT is any type of vector with its values accessible via [ ]

Definition at line 140 of file norms.hpp.

Referenced by pcl::selectNorm().

#include <pcl/common/common.h>

Calculate the area of a polygon given a point cloud that defines the polygon.

Parameters

polygon

point cloud that contains those vertices that comprises the polygon. Vertices are stored in counterclockwise.

Returns

the polygon area

Definition at line 414 of file common.hpp.

References pcl::PointCloud< PointT >::size().

#include <pcl/common/centroid.h>

Compute the 3D (X-Y-Z) centroid of a set of points using their indices and return it as a 3D vector.

Parameters

[in]	cloud	the input point cloud
[in]	indices	the point cloud indices that need to be used
[out]	centroid	the output centroid

Returns

number of valid points used to determine the centroid. In case of dense point clouds, this is the same as the size of input indices.

Note

if return value is 0, the centroid is not changed, thus not valid. The last component of the vector is set to 1, this allows to transform the centroid vector with 4x4 matrices.

Definition at line 129 of file centroid.hpp.

References pcl::PointCloud< PointT >::is_dense, and pcl::isFinite().

#include <pcl/common/centroid.h>

Compute the 3D (X-Y-Z) centroid of a set of points using their indices and return it as a 3D vector.

Parameters

[in]	cloud	the input point cloud
[in]	indices	the point cloud indices that need to be used
[out]	centroid	the output centroid

Returns

number of valid points used to determine the centroid. In case of dense point clouds, this is the same as the size of input indices.

Note

if return value is 0, the centroid is not changed, thus not valid. The last component of the vector is set to 1, this allows to transform the centroid vector with 4x4 matrices.

Definition at line 171 of file centroid.hpp.

References pcl::compute3DCentroid(), and pcl::PointIndices::indices.

#include <pcl/common/centroid.h>

Compute the 3D (X-Y-Z) centroid of a set of points and return it as a 3D vector.

Parameters

[in]	cloud	the input point cloud
[out]	centroid	the output centroid

Returns

number of valid points used to determine the centroid. In case of dense point clouds, this is the same as the size of input cloud.

Note

if return value is 0, the centroid is not changed, thus not valid. The last component of the vector is set to 1, this allows to transform the centroid vector with 4x4 matrices.

Definition at line 85 of file centroid.hpp.

References pcl::PointCloud< PointT >::empty(), pcl::PointCloud< PointT >::is_dense, and pcl::isFinite().

#include <pcl/common/centroid.h>

Compute the 3D (X-Y-Z) centroid of a set of points and return it as a 3D vector.

Parameters

[in]	cloud_iterator	an iterator over the input point cloud
[out]	centroid	the output centroid

Returns

number of valid points used to determine the centroid. In case of dense point clouds, this is the same as the size of input cloud.

Note

if return value is 0, the centroid is not changed, thus not valid. The last component of the vector is set to 1, this allows to transform the centroid vector with 4x4 matrices.

Definition at line 56 of file centroid.hpp.

References pcl::isFinite(), and pcl::ConstCloudIterator< PointT >::isValid().

Referenced by pcl::gpu::people::buildTree(), pcl::ConvexHull< PointInT >::calculateInputDimension(), pcl::compute3DCentroid(), pcl::MLSResult::computeMLSSurface(), pcl::MomentInvariantsEstimation< PointInT, PointOutT >::computePointMomentInvariants(), pcl::registration::TransformationEstimation2D< PointSource, PointTarget, Scalar >::estimateRigidTransformation(), pcl::registration::TransformationEstimationSVD< PointT, PointT, Scalar >::estimateRigidTransformation(), pcl::registration::TransformationEstimation3Point< PointSource, PointTarget, Scalar >::estimateRigidTransformation(), pcl::registration::FPCSInitialAlignment< PointSource, PointTarget, pcl::Normal, float >::linkMatchWithBase(), pcl::SampleConsensusModelLine< PointT >::optimizeModelCoefficients(), pcl::ConcaveHull< PointInT >::performReconstruction(), pcl::ConvexHull< PointInT >::performReconstruction2D(), pcl::ESFEstimation< PointInT, PointOutT >::scale_points_unit_sphere(), pcl::registration::FPCSInitialAlignment< PointSource, PointTarget, pcl::Normal, float >::selectBase(), and pcl::gpu::people::label_skeleton::sortIndicesToBlob2().

#include <pcl/common/centroid.h>

Compute the centroid of a set of points and return it as a point.

Parameters

[in]	cloud
[in]	indices	point cloud indices that need to be used
[out]	centroid	This is an overloaded function provided for convenience. See the documentation for computeCentroid().

Definition at line 913 of file centroid.hpp.

References pcl::PointCloud< PointT >::is_dense, and pcl::isFinite().

#include <pcl/common/centroid.h>

Compute the centroid of a set of points and return it as a point.

Implementation leverages CentroidPoint class and therefore behaves differently from compute3DCentroid() and computeNDCentroid(). See CentroidPoint documentation for explanation.

Parameters

[in]	cloud	input point cloud
[out]	centroid	output centroid

Returns

number of valid points used to determine the centroid (will be the same as the size of the cloud if it is dense)

Note

If return value is 0, then the centroid is not changed, thus is not valid.

Definition at line 894 of file centroid.hpp.

References pcl::PointCloud< PointT >::is_dense, and pcl::isFinite().

#include <pcl/common/eigen.h>

determines the corresponding eigenvector to the given eigenvalue of the symmetric positive semi definite input matrix

Parameters

[in]	mat	symmetric positive semi definite input matrix
[in]	eigenvalue	the eigenvalue which corresponding eigenvector is to be computed
[out]	eigenvector	the corresponding eigenvector for the input eigenvalue

Definition at line 226 of file eigen.hpp.

Referenced by pcl::PrincipalCurvaturesEstimation< PointInT, PointNT, PointOutT >::computePointPrincipalCurvatures(), pcl::SampleConsensusModelLine< PointT >::optimizeModelCoefficients(), and pcl::SampleConsensusModelStick< PointT >::optimizeModelCoefficients().

#include <pcl/common/centroid.h>

Compute the 3x3 covariance matrix of a given set of points.

The result is returned as a Eigen::Matrix3f. Note: the covariance matrix is not normalized with the number of points. For a normalized covariance, please use computeCovarianceMatrixNormalized.

Parameters

[in]	cloud	the input point cloud
[in]	centroid	the centroid of the set of points in the cloud
[out]	covariance_matrix	the resultant 3x3 covariance matrix

Returns

number of valid points used to determine the covariance matrix. In case of dense point clouds, this is the same as the size of input cloud.

Note

if return value is 0, the covariance matrix is not changed, thus not valid.

Definition at line 180 of file centroid.hpp.

References pcl::PointCloud< PointT >::empty(), pcl::PointCloud< PointT >::is_dense, pcl::isFinite(), and pcl::PointCloud< PointT >::size().

Referenced by pcl::CovarianceSampling< PointT, PointNT >::applyFilter(), pcl::MomentOfInertiaEstimation< PointT >::compute(), pcl::CovarianceSampling< PointT, PointNT >::computeConditionNumber(), pcl::computeCovarianceMatrix(), pcl::computeCovarianceMatrixNormalized(), pcl::MLSResult::computeMLSSurface(), and pcl::SampleConsensusModelLine< PointT >::optimizeModelCoefficients().

#include <pcl/common/centroid.h>

Compute the 3x3 covariance matrix of a given set of points using their indices.

The result is returned as a Eigen::Matrix3f. Note: the covariance matrix is not normalized with the number of points. For a normalized covariance, please use computeCovarianceMatrixNormalized.

Parameters

[in]	cloud	the input point cloud
[in]	indices	the point cloud indices that need to be used
[in]	centroid	the centroid of the set of points in the cloud
[out]	covariance_matrix	the resultant 3x3 covariance matrix

Returns

number of valid points used to determine the covariance matrix. In case of dense point clouds, this is the same as the size of input indices.

Definition at line 263 of file centroid.hpp.

References pcl::PointCloud< PointT >::is_dense, and pcl::isFinite().

#include <pcl/common/centroid.h>

Compute the normalized 3x3 covariance matrix for a already demeaned point cloud.

Normalized means that every entry has been divided by the number of entries in indices. For small number of points, or if you want explicitly the sample-variance, scale the covariance matrix with n / (n-1), where n is the number of points used to calculate the covariance matrix and is returned by this function.

Note

This method is theoretically exact. However using float for internal calculations reduces the accuracy but increases the efficiency.

Parameters

[in]	cloud	the input point cloud
[in]	indices	subset of points given by their indices
[out]	covariance_matrix	the resultant 3x3 covariance matrix

Returns

number of valid points used to determine the covariance matrix. In case of dense point clouds, this is the same as the size of input indices.

Definition at line 422 of file centroid.hpp.

References pcl::PointCloud< PointT >::is_dense, and pcl::isFinite().

#include <pcl/common/centroid.h>

Compute the 3x3 covariance matrix of a given set of points using their indices.

The result is returned as a Eigen::Matrix3f. Note: the covariance matrix is not normalized with the number of points. For a normalized covariance, please use computeCovarianceMatrixNormalized.

Parameters

[in]	cloud	the input point cloud
[in]	indices	the point cloud indices that need to be used
[in]	centroid	the centroid of the set of points in the cloud
[out]	covariance_matrix	the resultant 3x3 covariance matrix

Returns

number of valid points used to determine the covariance matrix. In case of dense point clouds, this is the same as the size of input indices.

Definition at line 334 of file centroid.hpp.

References pcl::computeCovarianceMatrix(), and pcl::PointIndices::indices.

#include <pcl/common/centroid.h>

Compute the normalized 3x3 covariance matrix for a already demeaned point cloud.

Normalized means that every entry has been divided by the number of entries in indices. For small number of points, or if you want explicitly the sample-variance, scale the covariance matrix with n / (n-1), where n is the number of points used to calculate the covariance matrix and is returned by this function.

Note

This method is theoretically exact. However using float for internal calculations reduces the accuracy but increases the efficiency.

Parameters

[in]	cloud	the input point cloud
[in]	indices	subset of points given by their indices
[out]	covariance_matrix	the resultant 3x3 covariance matrix

Returns

number of valid points used to determine the covariance matrix. In case of dense point clouds, this is the same as the size of input indices.

Definition at line 476 of file centroid.hpp.

References pcl::computeCovarianceMatrix(), and pcl::PointIndices::indices.

#include <pcl/common/centroid.h>

Compute the normalized 3x3 covariance matrix for a already demeaned point cloud.

Normalized means that every entry has been divided by the number of entries in the input point cloud. For small number of points, or if you want explicitly the sample-variance, scale the covariance matrix with n / (n-1), where n is the number of points used to calculate the covariance matrix and is returned by this function.

Note

This method is theoretically exact. However using float for internal calculations reduces the accuracy but increases the efficiency.

Parameters

[in]	cloud	the input point cloud
[out]	covariance_matrix	the resultant 3x3 covariance matrix

Returns

number of valid points used to determine the covariance matrix. In case of dense point clouds, this is the same as the size of input cloud.

Definition at line 368 of file centroid.hpp.

References pcl::PointCloud< PointT >::is_dense, pcl::isFinite(), and pcl::PointCloud< PointT >::size().

#include <pcl/common/centroid.h>

Compute normalized the 3x3 covariance matrix of a given set of points.

The result is returned as a Eigen::Matrix3f. Normalized means that every entry has been divided by the number of points in the point cloud. For small number of points, or if you want explicitly the sample-variance, use computeCovarianceMatrix and scale the covariance matrix with 1 / (n-1), where n is the number of points used to calculate the covariance matrix and is returned by the computeCovarianceMatrix function.

Parameters

[in]	cloud	the input point cloud
[in]	centroid	the centroid of the set of points in the cloud
[out]	covariance_matrix	the resultant 3x3 covariance matrix

Returns

number of valid points used to determine the covariance matrix. In case of dense point clouds, this is the same as the size of input cloud.

Definition at line 251 of file centroid.hpp.

References pcl::computeCovarianceMatrix().

Referenced by pcl::gpu::people::buildTree(), pcl::ConvexHull< PointInT >::calculateInputDimension(), pcl::computeCovarianceMatrixNormalized(), pcl::ConcaveHull< PointInT >::performReconstruction(), pcl::ConvexHull< PointInT >::performReconstruction2D(), and pcl::gpu::people::label_skeleton::sortIndicesToBlob2().

#include <pcl/common/centroid.h>

Compute the normalized 3x3 covariance matrix of a given set of points using their indices.

The result is returned as a Eigen::Matrix3f. Normalized means that every entry has been divided by the number of entries in indices. For small number of points, or if you want explicitly the sample-variance, use computeCovarianceMatrix and scale the covariance matrix with 1 / (n-1), where n is the number of points used to calculate the covariance matrix and is returned by the computeCovarianceMatrix function.

Parameters

[in]	cloud	the input point cloud
[in]	indices	the point cloud indices that need to be used
[in]	centroid	the centroid of the set of points in the cloud
[out]	covariance_matrix	the resultant 3x3 covariance matrix

Returns

number of valid points used to determine the covariance matrix. In case of dense point clouds, this is the same as the size of input indices.

Definition at line 344 of file centroid.hpp.

References pcl::computeCovarianceMatrix().

#include <pcl/common/centroid.h>

Compute the normalized 3x3 covariance matrix of a given set of points using their indices.

The result is returned as a Eigen::Matrix3f. Normalized means that every entry has been divided by the number of entries in indices. For small number of points, or if you want explicitly the sample-variance, use computeCovarianceMatrix and scale the covariance matrix with 1 / (n-1), where n is the number of points used to calculate the covariance matrix and is returned by the computeCovarianceMatrix function.

Parameters

[in]	cloud	the input point cloud
[in]	indices	the point cloud indices that need to be used
[in]	centroid	the centroid of the set of points in the cloud
[out]	covariance_matrix	the resultant 3x3 covariance matrix

Returns

number of valid points used to determine the covariance matrix. In case of dense point clouds, this is the same as the size of input indices.

Definition at line 358 of file centroid.hpp.

References pcl::computeCovarianceMatrixNormalized(), and pcl::PointIndices::indices.

#include <pcl/common/centroid.h>

Compute the normalized 3x3 covariance matrix and the centroid of a given set of points in a single loop.

Normalized means that every entry has been divided by the number of entries in indices. For small number of points, or if you want explicitly the sample-variance, scale the covariance matrix with n / (n-1), where n is the number of points used to calculate the covariance matrix and is returned by this function.

Note

This method is theoretically exact. However using float for internal calculations reduces the accuracy but increases the efficiency.

Parameters

[in]	cloud	the input point cloud
[in]	indices	subset of points given by their indices
[out]	covariance_matrix	the resultant 3x3 covariance matrix
[out]	centroid	the centroid of the set of points in the cloud

Returns

number of valid points used to determine the covariance matrix. In case of dense point clouds, this is the same as the size of input indices.

Definition at line 557 of file centroid.hpp.

References pcl::PointCloud< PointT >::is_dense, pcl::isFinite(), pcl::K, and pcl::PointCloud< PointT >::size().

#include <pcl/common/centroid.h>

Compute the normalized 3x3 covariance matrix and the centroid of a given set of points in a single loop.

Normalized means that every entry has been divided by the number of entries in indices. For small number of points, or if you want explicitly the sample-variance, scale the covariance matrix with n / (n-1), where n is the number of points used to calculate the covariance matrix and is returned by this function.

Note

This method is theoretically exact. However using float for internal calculations reduces the accuracy but increases the efficiency.

Parameters

[in]	cloud	the input point cloud
[in]	indices	subset of points given by their indices
[out]	centroid	the centroid of the set of points in the cloud
[out]	covariance_matrix	the resultant 3x3 covariance matrix

Returns

number of valid points used to determine the covariance matrix. In case of dense point clouds, this is the same as the size of input indices.

Definition at line 630 of file centroid.hpp.

References pcl::computeMeanAndCovarianceMatrix(), and pcl::PointIndices::indices.

#include <pcl/common/centroid.h>

Compute the normalized 3x3 covariance matrix and the centroid of a given set of points in a single loop.

Normalized means that every entry has been divided by the number of valid entries in the point cloud. For small number of points, or if you want explicitly the sample-variance, scale the covariance matrix with n / (n-1), where n is the number of points used to calculate the covariance matrix and is returned by this function.

Note

This method is theoretically exact. However using float for internal calculations reduces the accuracy but increases the efficiency.

Parameters

[in]	cloud	the input point cloud
[out]	covariance_matrix	the resultant 3x3 covariance matrix
[out]	centroid	the centroid of the set of points in the cloud

Returns

number of valid points used to determine the covariance matrix. In case of dense point clouds, this is the same as the size of input cloud.

Definition at line 485 of file centroid.hpp.

References pcl::isFinite(), and pcl::K.

Referenced by pcl::computeMeanAndCovarianceMatrix(), pcl::computePointNormal(), pcl::NormalEstimation< PointInT, PointNT >::computePointNormal(), pcl::SampleConsensusModelRegistration< PointT >::computeSampleDistanceThreshold(), pcl::getPrincipalTransformation(), pcl::GridProjection< PointNT >::getProjectionWithPlaneFit(), pcl::isPointIn2DPolygon(), pcl::SampleConsensusModelStick< PointT >::optimizeModelCoefficients(), pcl::SampleConsensusModelPlane< PointT >::optimizeModelCoefficients(), pcl::ExtractPolygonalPrismData< PointT >::segment(), and pcl::OrganizedMultiPlaneSegmentation< pcl::PointXYZRGBA, pcl::Normal, pcl::Label >::segment().

#include <pcl/common/common.h>

Compute the median of a list of values (fast).

If the number of values is even, take the mean of the two middle values. This function can be used like this:

Parameters

[in,out]	begin,end	Iterators that mark the beginning and end of the value range. These values will be reordered!
[in]	f	A lamda, function pointer, or similar that is implicitly applied to all values before median computation. In reality, it will be applied lazily (i.e. at most twice) and thus may not change the sorting order (e.g. monotonic functions like sqrt are allowed)

Returns

the median

Definition at line 285 of file common.h.

References pcl::geometry::distance().

Referenced by pcl::MaximumLikelihoodSampleConsensus< PointT >::computeMedian(), pcl::computeMedian(), pcl::MaximumLikelihoodSampleConsensus< PointT >::computeMedianAbsoluteDeviation(), and pcl::LeastMedianSquares< PointT >::computeModel().

#include <pcl/common/centroid.h>

General, all purpose nD centroid estimation for a set of points using their indices.

Parameters

cloud	the input point cloud
indices	the point cloud indices that need to be used
centroid	the output centroid

Definition at line 840 of file centroid.hpp.

#include <pcl/common/centroid.h>

General, all purpose nD centroid estimation for a set of points using their indices.

Parameters

cloud	the input point cloud
indices	the point cloud indices that need to be used
centroid	the output centroid

Definition at line 863 of file centroid.hpp.

References pcl::computeNDCentroid(), and pcl::PointIndices::indices.

#include <pcl/common/centroid.h>

General, all purpose nD centroid estimation for a set of points using their indices.

Parameters

cloud	the input point cloud
centroid	the output centroid

Definition at line 818 of file centroid.hpp.

References pcl::PointCloud< PointT >::empty().

Referenced by pcl::computeNDCentroid().

#include <pcl/common/io.h>

Concatenate two pcl::PCLPointCloud2.

Warning

This function will concatenate IFF the non-skip fields are in the correct order and same in number.

Parameters

[in]	cloud1	the first input point cloud dataset
[in]	cloud2	the second input point cloud dataset
[out]	cloud_out	the resultant output point cloud dataset

Returns

true if successful, false otherwise

Definition at line 266 of file io.h.

References pcl::PCLPointCloud2::concatenate().

#include <pcl/common/io.h>

Concatenate two pcl::PointCloud<PointT>

Parameters

[in]	cloud1	the first input point cloud dataset
[in]	cloud2	the second input point cloud dataset
[out]	cloud_out	the resultant output point cloud dataset

Returns

true if successful, false otherwise

Definition at line 248 of file io.h.

References pcl::PointCloud< PointT >::concatenate().

Referenced by pcl::PCLPointCloud2::concatenate(), pcl::PolygonMesh::concatenate(), pcl::PointCloud< pcl::RGB >::concatenate(), pcl::outofcore::OutofcoreOctreeDiskContainer< PointT >::insertRange(), pcl::PolygonMesh::operator+=(), pcl::PointCloud< pcl::RGB >::operator+=(), pcl::outofcore::OutofcoreOctreeBaseNode::queryBBIncludes(), and pcl::outofcore::OutofcoreOctreeDiskContainer< PointT >::read().

#include <pcl/common/io.h>

Concatenate two pcl::PolygonMesh.

Parameters

[in]	mesh1	the first input mesh
[in]	mesh2	the second input mesh
[out]	mesh_out	the resultant output mesh

Returns

true if successful, false otherwise

Definition at line 281 of file io.h.

References pcl::PolygonMesh::concatenate().

#include <pcl/common/io.h>

Concatenate two datasets representing different fields.

Note

If the input datasets have overlapping fields (i.e., both contain the same fields), then the data in the second cloud (cloud2_in) will overwrite the data in the first (cloud1_in).

Parameters

[in]	cloud1_in	the first input dataset
[in]	cloud2_in	the second input dataset (overwrites the fields of the first dataset for those that are shared)
[out]	cloud_out	the output dataset created by concatenating all the fields in the input datasets

#include <pcl/common/impl/io.hpp>

Concatenate two datasets representing different fields.

Note

If the input datasets have overlapping fields (i.e., both contain the same fields), then the data in the second cloud (cloud2_in) will overwrite the data in the first (cloud1_in).

Parameters

[in]	cloud1_in	the first input dataset
[in]	cloud2_in	the second input dataset (overwrites the fields of the first dataset for those that are shared)
[out]	cloud_out	the resultant output dataset created by the concatenation of all the fields in the input datasets

Definition at line 305 of file io.hpp.

References pcl::PointCloud< PointT >::header, pcl::PointCloud< PointT >::height, pcl::PointCloud< PointT >::is_dense, pcl::PointCloud< PointT >::resize(), pcl::PointCloud< PointT >::size(), and pcl::PointCloud< PointT >::width.

#include <pcl/common/copy_point.h>

Copy the fields of a source point into a target point.

If the source and the target point types are the same, then a complete copy is made. Otherwise only those fields that the two point types share in common are copied.

Parameters

[in]	point_in	the source point
[out]	point_out	the target point

Definition at line 137 of file copy_point.hpp.

Referenced by pcl::FilterIndices< PointInT >::applyFilter(), pcl::ConditionalRemoval< PointT >::applyFilter(), pcl::MovingLeastSquares< PointInT, PointOutT >::copyMissingFields(), pcl::copyPointCloud(), pcl::detail::copyPointCloudMemcpy(), pcl::registration::CorrespondenceEstimationNormalShooting< PointSource, PointTarget, NormalT, Scalar >::determineCorrespondences(), pcl::registration::CorrespondenceEstimationBackProjection< PointSource, PointTarget, NormalT, Scalar >::determineCorrespondences(), pcl::registration::CorrespondenceEstimation< PointSource, PointTarget, Scalar >::determineCorrespondences(), pcl::registration::CorrespondenceEstimationNormalShooting< PointSource, PointTarget, NormalT, Scalar >::determineReciprocalCorrespondences(), pcl::registration::CorrespondenceEstimationBackProjection< PointSource, PointTarget, NormalT, Scalar >::determineReciprocalCorrespondences(), pcl::registration::CorrespondenceEstimation< PointSource, PointTarget, Scalar >::determineReciprocalCorrespondences(), pcl::gpu::extractEuclideanClusters(), pcl::search::Search< PointXYZRGB >::nearestKSearchT(), pcl::KdTree< FeatureT >::nearestKSearchT(), pcl::KdTree< FeatureT >::radiusSearchT(), and pcl::search::Search< PointXYZRGB >::radiusSearchT().

#include <pcl/common/io.h>

Extract the indices of a given point cloud as a new point cloud.

Parameters

[in]	cloud_in	the input point cloud dataset
[in]	indices	the vector of indices representing the points to be copied from cloud_in
[out]	cloud_out	the resultant output point cloud dataset

Note

Assumes unique indices.

#include <pcl/common/io.h>

Extract the indices of a given point cloud as a new point cloud.

Parameters

[in]	cloud_in	the input point cloud dataset
[in]	indices	the vector of indices representing the points to be copied from cloud_in
[out]	cloud_out	the resultant output point cloud dataset

Note

Assumes unique indices.

#include <pcl/common/io.h>

Copy fields and point cloud data from cloud_in to cloud_out.

Parameters

[in]	cloud_in	the input point cloud dataset
[out]	cloud_out	the resultant output point cloud dataset

#include <pcl/common/impl/io.hpp>

Extract the indices of a given point cloud as a new point cloud.

Parameters

[in]	cloud_in	the input point cloud dataset
[in]	indices	the vector of indices representing the points to be copied from cloud_in
[out]	cloud_out	the resultant output point cloud dataset

Note

Assumes unique indices.

Definition at line 190 of file io.hpp.

References pcl::copyPoint(), pcl::PointCloud< PointT >::header, pcl::PointCloud< PointT >::height, pcl::PointCloud< PointT >::is_dense, pcl::PointCloud< PointT >::resize(), pcl::PointCloud< PointT >::sensor_orientation_, pcl::PointCloud< PointT >::sensor_origin_, and pcl::PointCloud< PointT >::width.

#include <pcl/common/impl/io.hpp>

Extract the indices of a given point cloud as a new point cloud.

Parameters

[in]	cloud_in	the input point cloud dataset
[in]	indices	the PointIndices structure representing the points to be copied from cloud_in
[out]	cloud_out	the resultant output point cloud dataset

Note

Assumes unique indices.

Definition at line 219 of file io.hpp.

References pcl::copyPointCloud(), and pcl::PointIndices::indices.

#include <pcl/common/impl/io.hpp>

Extract the indices of a given point cloud as a new point cloud.

Parameters

[in]	cloud_in	the input point cloud dataset
[in]	indices	the vector of indices representing the points to be copied from cloud_in
[out]	cloud_out	the resultant output point cloud dataset

Note

Assumes unique indices.

Definition at line 267 of file io.hpp.

References pcl::copyPoint(), pcl::copyPointCloud(), pcl::PointCloud< PointT >::header, pcl::PointCloud< PointT >::height, pcl::PointCloud< PointT >::is_dense, pcl::PointCloud< PointT >::resize(), pcl::PointCloud< PointT >::sensor_orientation_, pcl::PointCloud< PointT >::sensor_origin_, pcl::PointCloud< PointT >::size(), and pcl::PointCloud< PointT >::width.

#include <pcl/common/impl/io.hpp>

Copy all the fields from a given point cloud into a new point cloud.

Parameters

[in]	cloud_in	the input point cloud dataset
[out]	cloud_out	the resultant output point cloud dataset

Definition at line 144 of file io.hpp.

References pcl::detail::copyPointCloudMemcpy(), pcl::PointCloud< PointT >::empty(), pcl::PointCloud< PointT >::header, pcl::PointCloud< PointT >::height, pcl::PointCloud< PointT >::is_dense, pcl::PointCloud< PointT >::resize(), pcl::PointCloud< PointT >::sensor_orientation_, pcl::PointCloud< PointT >::sensor_origin_, pcl::PointCloud< PointT >::size(), and pcl::PointCloud< PointT >::width.

Referenced by pcl::HypothesisVerification< ModelT, SceneT >::addModels(), pcl::outofcore::OutofcoreOctreeBaseNode::addPointCloud(), pcl::outofcore::OutofcoreOctreeBaseNode::addPointCloud_and_genLOD(), pcl::LineRGBD< PointXYZT, PointRGBT >::addTemplate(), pcl::keypoints::internal::AgastApplyNonMaxSuppresion< Out >::AgastApplyNonMaxSuppresion(), pcl::keypoints::internal::AgastDetector< Out >::AgastDetector(), pcl::FastBilateralFilterOMP< PointT >::applyFilter(), pcl::MedianFilter< PointT >::applyFilter(), pcl::FastBilateralFilter< PointT >::applyFilter(), pcl::ExtractIndices< PointT >::applyFilter(), pcl::FilterIndices< PointInT >::applyFilter(), ObjectRecognition::applyFiltersAndSegment(), pcl::applyMorphologicalOperator(), pcl::GeometricConsistencyGrouping< PointModelT, PointSceneT >::clusterCorrespondences(), pcl::Hough3DGrouping< PointModelT, PointSceneT, PointModelRfT, PointSceneRfT >::clusterCorrespondences(), pcl::tracking::PyramidalKLTTracker< PointInT, IntensityT >::computePyramids(), pcl::copyPointCloud(), pcl::LineRGBD< PointXYZT, PointRGBT >::createAndAddTemplate(), pcl::HarrisKeypoint6D< PointInT, PointOutT, NormalT >::detectKeypoints(), pcl::BriskKeypoint2D< PointInT, PointOutT, IntensityT >::detectKeypoints(), pcl::occlusion_reasoning::ZBuffering< ModelT, SceneT >::filter(), pcl::occlusion_reasoning::filter(), pcl::Filter< pcl::PointXYZRGBL >::filter(), pcl::SupervoxelClustering< PointT >::getLabeledCloud(), pcl::SupervoxelClustering< PointT >::getLabeledVoxelCloud(), pcl::occlusion_reasoning::getOccludedCloud(), pcl::getPointCloudDifference(), pcl::SupervoxelClustering< PointT >::getVoxelCentroidCloud(), pcl::ConcaveHull< PointInT >::performReconstruction(), pcl::outofcore::OutofcoreOctreeBaseNode::queryBBIncludes(), and pcl::io::savePLYFile().

#include <pcl/common/impl/io.hpp>

Extract the indices of a given point cloud as a new point cloud.

Parameters

[in]	cloud_in	the input point cloud dataset
[in]	indices	the vector of indices representing the points to be copied from cloud_in
[out]	cloud_out	the resultant output point cloud dataset

Note

Assumes unique indices.

Definition at line 162 of file io.hpp.

References pcl::PointCloud< PointT >::clear(), pcl::PointCloud< PointT >::header, pcl::PointCloud< PointT >::height, pcl::PointCloud< PointT >::is_dense, pcl::PointCloud< PointT >::reserve(), pcl::PointCloud< PointT >::sensor_orientation_, pcl::PointCloud< PointT >::sensor_origin_, pcl::PointCloud< PointT >::size(), pcl::PointCloud< PointT >::transient_push_back(), and pcl::PointCloud< PointT >::width.

#include <pcl/common/impl/io.hpp>

Extract the indices of a given point cloud as a new point cloud.

Parameters

[in]	cloud_in	the input point cloud dataset
[in]	indices	the PointIndices structure representing the points to be copied from cloud_in
[out]	cloud_out	the resultant output point cloud dataset

Note

Assumes unique indices.

Definition at line 210 of file io.hpp.

References pcl::copyPointCloud(), and pcl::PointIndices::indices.

#include <pcl/common/impl/io.hpp>

Extract the indices of a given point cloud as a new point cloud.

Parameters

[in]	cloud_in	the input point cloud dataset
[in]	indices	the vector of indices representing the points to be copied from cloud_in
[out]	cloud_out	the resultant output point cloud dataset

Note

Assumes unique indices.

Definition at line 228 of file io.hpp.

References pcl::PointCloud< PointT >::clear(), pcl::PointCloud< PointT >::header, pcl::PointCloud< PointT >::height, pcl::PointCloud< PointT >::is_dense, pcl::PointCloud< PointT >::reserve(), pcl::PointCloud< PointT >::sensor_orientation_, pcl::PointCloud< PointT >::sensor_origin_, pcl::PointCloud< PointT >::size(), pcl::PointCloud< PointT >::transient_push_back(), and pcl::PointCloud< PointT >::width.

#include <pcl/common/impl/io.hpp>

Copy a point cloud inside a larger one interpolating borders.

Parameters

[in]	cloud_in	the input point cloud dataset
[out]	cloud_out	the resultant output point cloud dataset
	top
	bottom
	left
	right	Position of cloud_in inside cloud_out is given by top, left, bottom right.
[in]	border_type	the interpolating method (pcl::BORDER_XXX) BORDER_REPLICATE: aaaaaa|abcdefgh|hhhhhhh BORDER_REFLECT: fedcba|abcdefgh|hgfedcb BORDER_REFLECT_101: gfedcb|abcdefgh|gfedcba BORDER_WRAP: cdefgh|abcdefgh|abcdefg BORDER_CONSTANT: iiiiii|abcdefgh|iiiiiii with some specified 'i' BORDER_TRANSPARENT: mnopqr|abcdefgh|tuvwxyz where m-r and t-z are original values of cloud_out
	value

Exceptions

pcl::BadArgumentException

if any of top, bottom, left or right is negative.

Definition at line 339 of file io.hpp.

References pcl::BORDER_CONSTANT, pcl::BORDER_TRANSPARENT, pcl::PointCloud< PointT >::header, pcl::PointCloud< PointT >::height, pcl::interpolatePointIndex(), pcl::PointCloud< PointT >::is_dense, pcl::PointCloud< PointT >::resize(), pcl::PointCloud< PointT >::sensor_orientation_, pcl::PointCloud< PointT >::sensor_origin_, pcl::PointCloud< PointT >::size(), and pcl::PointCloud< PointT >::width.

#include <pcl/common/impl/norms.hpp>

Compute the CS norm of the vector between two points.

Parameters

A	the first point
B	the second point
dim	the number of dimensions in A and B (dimensions must match)

Note

FloatVectorT is any type of vector with its values accessible via [ ]

Definition at line 169 of file norms.hpp.

Referenced by pcl::selectNorm().

#include <pcl/common/angles.h>

Convert an angle from degrees to radians.

Parameters

alpha

the input angle (in degrees)

Definition at line 79 of file angles.hpp.

#include <pcl/common/angles.h>

Convert an angle from degrees to radians.

Parameters

alpha

the input angle (in degrees)

Definition at line 67 of file angles.hpp.

Referenced by pcl::RangeImage::createFromPointCloud(), pcl::RangeImage::createFromPointCloudWithKnownSize(), pcl::RangeImage::getImpactAngleBasedOnLocalNormal(), pcl::UniqueShapeContext< PointInT, PointOutT, PointRFT >::initCompute(), and pcl::ShapeContext3DEstimation< PointInT, PointNT, PointOutT >::initCompute().

#include <pcl/common/centroid.h>

Subtract a centroid from a point cloud and return the de-meaned representation as an Eigen matrix.

Parameters

[in]	cloud_in	the input point cloud
[in]	centroid	the centroid of the point cloud
[out]	cloud_out	the resultant output XYZ0 dimensions of cloud_in as an Eigen matrix (4 rows, N pts columns)

Definition at line 761 of file centroid.hpp.

References pcl::PointCloud< PointT >::size().

#include <pcl/common/centroid.h>

Subtract a centroid from a point cloud and return the de-meaned representation.

Parameters

[in]	cloud_in	the input point cloud
[in]	centroid	the centroid of the point cloud
[out]	cloud_out	the resultant output point cloud

Definition at line 673 of file centroid.hpp.

#include <pcl/common/centroid.h>

Subtract a centroid from a point cloud and return the de-meaned representation as an Eigen matrix.

Parameters

[in]	cloud_in	the input point cloud
[in]	indices	the set of point indices to use from the input point cloud
[in]	centroid	the centroid of the point cloud
[out]	cloud_out	the resultant output XYZ0 dimensions of cloud_in as an Eigen matrix (4 rows, N pts columns)

Definition at line 784 of file centroid.hpp.

#include <pcl/common/centroid.h>

Subtract a centroid from a point cloud and return the de-meaned representation.

Parameters

[in]	cloud_in	the input point cloud
[in]	indices	the set of point indices to use from the input point cloud
[out]	centroid	the centroid of the point cloud
	cloud_out	the resultant output point cloud

Definition at line 690 of file centroid.hpp.

References pcl::PointCloud< PointT >::header, pcl::PointCloud< PointT >::height, pcl::PointCloud< PointT >::is_dense, pcl::PointCloud< PointT >::resize(), pcl::PointCloud< PointT >::size(), and pcl::PointCloud< PointT >::width.

#include <pcl/common/centroid.h>

Subtract a centroid from a point cloud and return the de-meaned representation as an Eigen matrix.

Parameters

[in]	cloud_in	the input point cloud
[in]	indices	the set of point indices to use from the input point cloud
[in]	centroid	the centroid of the point cloud
[out]	cloud_out	the resultant output XYZ0 dimensions of cloud_in as an Eigen matrix (4 rows, N pts columns)

Definition at line 808 of file centroid.hpp.

References pcl::demeanPointCloud(), and pcl::PointIndices::indices.

#include <pcl/common/centroid.h>

Subtract a centroid from a point cloud and return the de-meaned representation.

Parameters

[in]	cloud_in	the input point cloud
[in]	indices	the set of point indices to use from the input point cloud
[out]	centroid	the centroid of the point cloud
	cloud_out	the resultant output point cloud

Definition at line 720 of file centroid.hpp.

References pcl::demeanPointCloud(), and pcl::PointIndices::indices.

#include <pcl/common/centroid.h>

Subtract a centroid from a point cloud and return the de-meaned representation as an Eigen matrix.

Parameters

[in]	cloud_iterator	an iterator over the input point cloud
[in]	centroid	the centroid of the point cloud
[out]	cloud_out	the resultant output XYZ0 dimensions of cloud_in as an Eigen matrix (4 rows, N pts columns)
[in]	npts	the number of samples guaranteed to be left in the input cloud, accessible by the iterator. If not given, it will be calculated.

Definition at line 730 of file centroid.hpp.

References pcl::ConstCloudIterator< PointT >::isValid(), and pcl::ConstCloudIterator< PointT >::reset().

#include <pcl/common/centroid.h>

Subtract a centroid from a point cloud and return the de-meaned representation.

Parameters

[in]	cloud_iterator	an iterator over the input point cloud
[in]	centroid	the centroid of the point cloud
[out]	cloud_out	the resultant output point cloud
[in]	npts	the number of samples guaranteed to be left in the input cloud, accessible by the iterator. If not given, it will be calculated.

Definition at line 640 of file centroid.hpp.

References pcl::PointCloud< PointT >::height, pcl::ConstCloudIterator< PointT >::isValid(), pcl::ConstCloudIterator< PointT >::reset(), pcl::PointCloud< PointT >::resize(), pcl::PointCloud< PointT >::size(), and pcl::PointCloud< PointT >::width.

Referenced by pcl::demeanPointCloud(), pcl::registration::TransformationEstimation2D< PointSource, PointTarget, Scalar >::estimateRigidTransformation(), pcl::registration::TransformationEstimationSVD< PointT, PointT, Scalar >::estimateRigidTransformation(), pcl::registration::TransformationEstimation3Point< PointSource, PointTarget, Scalar >::estimateRigidTransformation(), pcl::ConcaveHull< PointInT >::performReconstruction(), pcl::ESFEstimation< PointInT, PointOutT >::scale_points_unit_sphere(), and pcl::OURCVFHEstimation< PointInT, PointNT, PointOutT >::sgurf().

#include <pcl/common/eigen.h>

Calculate the determinant of a 3x3 matrix.

Parameters

[in]

matrix

matrix

Returns

determinant of the matrix

Definition at line 492 of file eigen.hpp.

#include <pcl/common/impl/norms.hpp>

Compute the div norm of the vector between two points.

Parameters

A	the first point
B	the second point
dim	the number of dimensions in A and B (dimensions must match)

Note

FloatVectorT is any type of vector with its values accessible via [ ]

Definition at line 183 of file norms.hpp.

Referenced by pcl::selectNorm().

#include <pcl/common/eigen.h>

determine the smallest eigenvalue and its corresponding eigenvector

Parameters

[in]	mat	input matrix that needs to be symmetric and positive semi definite
[out]	eigenvectors	the corresponding eigenvector to the smallest eigenvalue of the input matrix
[out]	eigenvalues	the smallest eigenvalue of the input matrix

Definition at line 172 of file eigen.hpp.

#include <pcl/common/eigen.h>

determine the smallest eigenvalue and its corresponding eigenvector

Parameters

[in]	mat	input matrix that needs to be symmetric and positive semi definite
[out]	eigenvalue	the smallest eigenvalue of the input matrix
[out]	eigenvector	the corresponding eigenvector to the smallest eigenvalue of the input matrix

Definition at line 133 of file eigen.hpp.

Referenced by pcl::approximatePolygon2D().

#include <pcl/common/eigen.h>

determines the eigenvalues and corresponding eigenvectors of the symmetric positive semi definite input matrix

Parameters

[in]	mat	symmetric positive semi definite input matrix
[out]	evecs	corresponding eigenvectors in correct order according to eigenvalues
[out]	evals	resulting eigenvalues in ascending order

Definition at line 334 of file eigen.hpp.

References pcl::computeRoots(), and pcl::geometry::distance().

#include <pcl/common/eigen.h>

determines the eigenvector and eigenvalue of the smallest eigenvalue of the symmetric positive semi definite input matrix

Parameters

[in]	mat	symmetric positive semi definite input matrix
[out]	eigenvalue	smallest eigenvalue of the input matrix
[out]	eigenvector	the corresponding eigenvector for the input eigenvalue

Note

if the smallest eigenvalue is not unique, this function may return any eigenvector that is consistent to the eigenvalue.

Definition at line 296 of file eigen.hpp.

References pcl::computeRoots().

Referenced by pcl::gpu::people::buildTree(), pcl::ConvexHull< PointInT >::calculateInputDimension(), pcl::MLSResult::computeMLSSurface(), pcl::IntensityGradientEstimation< PointInT, PointNT, PointOutT, IntensitySelectorT >::computePointIntensityGradient(), pcl::IntegralImageNormalEstimation< pcl::PointXYZRGBA, pcl::Normal >::computePointNormal(), pcl::IntegralImageNormalEstimation< pcl::PointXYZRGBA, pcl::Normal >::computePointNormalMirror(), pcl::PrincipalCurvaturesEstimation< PointInT, PointNT, PointOutT >::computePointPrincipalCurvatures(), pcl::SampleConsensusModelRegistration< PointT >::computeSampleDistanceThreshold(), pcl::VectorAverage< real, dimension >::doPCA(), pcl::VectorAverage< real, dimension >::getEigenVector1(), pcl::getPrincipalTransformation(), pcl::GridProjection< PointNT >::getProjectionWithPlaneFit(), pcl::isPointIn2DPolygon(), pcl::SampleConsensusModelLine< PointT >::optimizeModelCoefficients(), pcl::SampleConsensusModelStick< PointT >::optimizeModelCoefficients(), pcl::SampleConsensusModelPlane< PointT >::optimizeModelCoefficients(), pcl::ConcaveHull< PointInT >::performReconstruction(), pcl::ConvexHull< PointInT >::performReconstruction2D(), pcl::HarrisKeypoint3D< PointInT, PointOutT, NormalT >::responseTomasi(), pcl::ExtractPolygonalPrismData< PointT >::segment(), pcl::OrganizedMultiPlaneSegmentation< pcl::PointXYZRGBA, pcl::Normal, pcl::Label >::segment(), pcl::solvePlaneParameters(), and pcl::gpu::people::label_skeleton::sortIndicesToBlob2().

#include <pcl/common/eigen.h>

determines the eigenvalues of the symmetric positive semi definite input matrix

Parameters

[in]	mat	symmetric positive semi definite input matrix
[out]	evals	resulting eigenvalues in ascending order

Definition at line 320 of file eigen.hpp.

References pcl::computeRoots().

#include <pcl/common/common.h>

Compute the smallest angle between two 3D vectors in radians (default) or degree.

Parameters

v1	the first 3D vector (represented as a Eigen::Vector3f)
v2	the second 3D vector (represented as a Eigen::Vector3f)
in_degree	determine if angle should be in radians or degrees

Returns

the angle between v1 and v2 in radians or degrees

Definition at line 59 of file common.hpp.

References M_PI.

#include <pcl/common/common.h>

Compute the smallest angle between two 3D vectors in radians (default) or degree.

Parameters

v1	the first 3D vector (represented as a Eigen::Vector4f)
v2	the second 3D vector (represented as a Eigen::Vector4f)
in_degree	determine if angle should be in radians or degrees

Returns

the angle between v1 and v2 in radians or degrees

Note

Handles rounding error for parallel and anti-parallel vectors

Definition at line 47 of file common.hpp.

References M_PI.

Referenced by pcl::tracking::NormalCoherence< PointInT >::computeCoherence(), pcl::tracking::ParticleFilterTracker< PointInT, StateT >::computeTransformedPointCloudWithNormal(), pcl::LCCPSegmentation< PointT >::connIsConvex(), pcl::SampleConsensusModelNormalSphere< PointT, PointNT >::countWithinDistance(), pcl::SampleConsensusModelCylinder< pcl::PointXYZRGB, PointNT >::countWithinDistance(), pcl::SampleConsensusModelCone< pcl::PointXYZRGB, PointNT >::countWithinDistance(), pcl::SampleConsensusModelNormalPlane< PointT, PointNT >::countWithinDistanceStandard(), pcl::SampleConsensusModelNormalSphere< PointT, PointNT >::getDistancesToModel(), pcl::SampleConsensusModelNormalPlane< PointT, PointNT >::getDistancesToModel(), pcl::SampleConsensusModelCylinder< pcl::PointXYZRGB, PointNT >::getDistancesToModel(), pcl::SampleConsensusModelCone< pcl::PointXYZRGB, PointNT >::getDistancesToModel(), pcl::SampleConsensusModelParallelLine< PointT >::isModelValid(), pcl::SampleConsensusModelPerpendicularPlane< PointT >::isModelValid(), pcl::SampleConsensusModelCylinder< pcl::PointXYZRGB, PointNT >::isModelValid(), pcl::SampleConsensusModelCone< pcl::PointXYZRGB, PointNT >::isModelValid(), pcl::SampleConsensusModelNormalSphere< PointT, PointNT >::selectWithinDistance(), pcl::SampleConsensusModelNormalPlane< PointT, PointNT >::selectWithinDistance(), pcl::SampleConsensusModelCylinder< pcl::PointXYZRGB, PointNT >::selectWithinDistance(), and pcl::SampleConsensusModelCone< pcl::PointXYZRGB, PointNT >::selectWithinDistance().

#include <pcl/common/common.h>

Compute the radius of a circumscribed circle for a triangle formed of three points pa, pb, and pc.

Parameters

pa	the first point
pb	the second point
pc	the third point

Returns

the radius of the circumscribed circle

Definition at line 383 of file common.hpp.

Referenced by pcl::ConcaveHull< PointInT >::performReconstruction().

#include <pcl/common/io.h>

Copy the XYZ dimensions from an Eigen MatrixXf into a pcl::PCLPointCloud2 message.

Parameters

[in]	in	the Eigen MatrixXf format containing XYZ0 / point
[out]	out	the resultant point cloud message

Note

the method assumes that the PCLPointCloud2 message already has the fields set up properly !

#include <pcl/common/eigen.h>

Extract the Euler angles (intrinsic rotations, ZYX-convention) from the given transformation.

Parameters

[in]	t	the input transformation matrix
[in]	roll	the resulting roll angle
[in]	pitch	the resulting pitch angle
[in]	yaw	the resulting yaw angle

Definition at line 585 of file eigen.hpp.

Referenced by pcl::tracking::ParticleXYZRPY::sample().

#include <pcl/common/io.h>

Get the index of a specified field (i.e., dimension/channel)

Parameters

[in]	cloud	the point cloud message
[in]	field_name	the string defining the field name

Definition at line 60 of file io.h.

References pcl::geometry::distance(), and pcl::PCLPointCloud2::fields.