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Get rid of all exception-specifications, these are useless and soon to be deprecated

Member pcl::assignNormalWeights (const PointCloud< NormalT > &cloud, index_t index, const Indices &k_indices, const std::vector< float > &k_sqr_distances)

Currently, this function equalizes all weights to 1

Member pcl::createMapping (const std::vector< pcl::PCLPointField > &msg_fields, MsgFieldMap &field_map)

One could construct a pathological case where the struct has a field where the serialized data has padding

Member pcl::cuda::SampleConsensusModel< Storage >::setRadiusLimits (float min_radius, float max_radius)

change this to set limits on the entire model

Member pcl::EuclideanClusterExtraction< PointT >::getSearchMethod () const

fix this for a generic search tree

Member pcl::extractEuclideanClusters (const PointCloud< PointT > &cloud, const Indices &indices, const typename search::Search< PointT >::Ptr &tree, float tolerance, std::vector< PointIndices > &clusters, unsigned int min_pts_per_cluster=1, unsigned int max_pts_per_cluster=(std::numeric_limits< int >::max)())

: fix the return value, make sure the exit is not needed anymore

Class pcl::geometry::MeshBase< DerivedT, MeshTraitsT, MeshTagT >

Add documentation

Class pcl::geometry::MeshIO< MeshT >

Only writes the topology (not the mesh data).
Supports only ascii.
Does not consider the mesh traits (e.g. manifold or not)

Member pcl::gpu::EuclideanClusterExtraction< PointT >::getSearchMethod ()

fix this for a generic search tree

Member pcl::gpu::EuclideanLabeledClusterExtraction< PointT >::extract (std::vector< PointIndices > &clusters)

what do we do if input isn't a PointXYZ cloud?

Member pcl::gpu::EuclideanLabeledClusterExtraction< PointT >::getSearchMethod ()

fix this for a generic search tree

Member pcl::gpu::extractEuclideanClusters (const typename pcl::PointCloud< PointT >::Ptr &host_cloud_, const pcl::gpu::Octree::Ptr &tree, float tolerance, std::vector< PointIndices > &clusters, unsigned int min_pts_per_cluster, unsigned int max_pts_per_cluster)

: adjust this to a variable number settable with method

Class pcl::gpu::people::Blob2

: clean this out in the end, perhaps place the children in a separate struct

Member pcl::gpu::people::buildRelations (std::vector< std::vector< Blob2, Eigen::aligned_allocator< pcl::gpu::people::Blob2 > > > &sorted)

This function also fixes the kinematic chain, we should implement this in a xml or LUT

look if we can't get a more efficient implementation (iterator together with sortBlobs perhaps?)

Member pcl::gpu::people::buildRelations (std::vector< std::vector< Blob2, Eigen::aligned_allocator< pcl::gpu::people::Blob2 > > > &sorted, PersonAttribs::Ptr person_attribs)

This function also fixes the kinematic chain, we should implement this in a xml or LUT

look if we can't get a more efficient implementation (iterator together with sortBlobs perhaps?)

Member pcl::gpu::people::evaluateBlobs (Blob2 &parent, Blob2 &child, int child_nr)

what if child is second link in stead of first link (ea forearm in stead of elbow for arm)

Member pcl::gpu::people::evaluateBlobs (Blob2 &parent, Blob2 &child, int child_nr, PersonAttribs::Ptr person_attribs)

what if child is second link in stead of first link (ea forearm in stead of elbow for arm)

Member pcl::gpu::people::evaluateBlobVector (std::vector< std::vector< Blob2, Eigen::aligned_allocator< Blob2 > > > &sorted, unsigned int parent_label, int child_label, int child_number, PersonAttribs::Ptr person_attribs)

once we have good evaluation function reconsider best_value

Member pcl::gpu::people::evaluateBlobVector (std::vector< std::vector< Blob2, Eigen::aligned_allocator< Blob2 > > > &sorted, unsigned int parent_label, int child_label, int child_number)

once we have good evaluation function reconsider best_value

Member pcl::gpu::people::label_skeleton::smoothLabelImage (cv::Mat &lmap_in, cv::Mat &dmap, cv::Mat &lmap_out)

make the patch size a parameter

make the z-distance a parameter

add a Gaussian contribution function to depth and vote

Member pcl::gpu::people::label_skeleton::sortIndicesToBlob2 (const pcl::PointCloud< pcl::PointXYZ > &cloud_in, unsigned int sizeThres, std::vector< std::vector< Blob2, Eigen::aligned_allocator< Blob2 > > > &sorted, std::vector< std::vector< pcl::PointIndices > > &indices)

implement the eigenvalue evaluation again

do we still need sizeThres?

Member pcl::gpu::people::LUT_max_part_size []

read this from XML file

Member pcl::gpu::people::part_t

implement label 25 to 29

Member pcl::gpu::SeededHueSegmentation::getSearchMethod ()

fix this for a generic search tree

Member pcl::gpu::SeededHueSegmentation::segment (PointIndices &indices_in, PointIndices &indices_out)

what do we do if input isn't a PointXYZ cloud?

Member pcl::IndicesAllocator

Remove with C++20

Member pcl::is_invocable_v

: Remove in C++17

Member pcl::IterativeClosestPoint< PointSource, PointTarget, Scalar >::IterativeClosestPoint (const IterativeClosestPoint &)=delete

: remove deleted ctors and assignments operations after resolving the issue

Member pcl::PointXYZRGBAtoXYZHSV (const PointXYZRGBA &in, PointXYZHSV &out)

include the A parameter but how?

Class pcl::registration::CorrespondenceRejectorFeatures

explain this better.

Member pcl::remove_cvref_t

: Remove in C++17

Member pcl::SampleConsensusModel< PointT >::setRadiusLimits (const double &min_radius, const double &max_radius)

change this to set limits on the entire model

Member pcl::SampleConsensusModelSphere< PointT >::projectPoints (const Indices &inliers, const Eigen::VectorXf &model_coefficients, PointCloud &projected_points, bool copy_data_fields=true) const override

implement this.

Member pcl::search::OrganizedNeighbor< PointT >::nearestKSearch (const PointT &p_q, int k, Indices &k_indices, std::vector< float > &k_sqr_distances) const override

still need to implements this functionality

Member pcl::seededHueSegmentation (const PointCloud< PointXYZRGB > &cloud, const search::Search< PointXYZRGB >::Ptr &tree, float tolerance, PointIndices &indices_in, PointIndices &indices_out, float delta_hue=0.0)

look how to make this templated!

Member pcl::seededHueSegmentation (const PointCloud< PointXYZRGB > &cloud, const search::Search< PointXYZRGBL >::Ptr &tree, float tolerance, PointIndices &indices_in, PointIndices &indices_out, float delta_hue=0.0)

look how to make this templated!

Member pcl::SurfaceNormalModality< PointInT >::computeAndQuantizeSurfaceNormals2 ()

Should also need camera model, or at least focal lengths? Replace distance_threshold with mask?

Magic number 1150 is focal length? This is something like f in SXGA mode, but in VGA is more like 530.

Member pcl::toPCLPointCloud2 (const pcl::PointCloud< PointT > &cloud, pcl::PCLPointCloud2 &msg)

msg.is_bigendian = ?;

Member pcl::visualization::PointCloudColorHandlerHSVField< PointT >::getColor () const override

do this with the point_types_conversion in common, first template it!

© 2009–2012, Willow Garage, Inc.
© 2012–, Open Perception, Inc.
Licensed under the BSD License.
https://pointclouds.org/documentation/todo.html