Table of contents
5.1 Point cloud filtering scheme in PCL
5.2 Filter base class correlation
(1) Class pcl::Filter< PointT >
(2) Class pcl::Clipper3D< PointT >
(3) Class pcl::FilterIndices< PointT >
5.3 Classification of PCL filtering algorithms
(2) Sampling class (mostly used for registration or other post-processing of point clouds)
5.1 Point cloud filtering scheme in PCL
Reference article: Introduction to concepts and algorithms related to PCL filter module_setleafsize_天天地 mango’s blog-CSDN blog
PCL summarizes several situations that require point cloud filtering. These situations are as follows:
(1) The point cloud data density is irregular and needs to be smoothed.
(2) Outliers need to be removed due to problems such as occlusion.
(3) A large amount of data requires downsampling.
(4) Noise data needs to be removed.
The corresponding method is as follows:
(1) Filter and remove points according to specific given rules.
(2) Modify some attributes of points through common filtering algorithms.
(3) Downsample the data.
5.2 Filter base class correlation
(1) Class pcl::Filter< PointT >
pcl::Filter<PointT> is the interface for almost all filtering algorithms. In the pcl::Filter<PointT> base class, the most important function is void filter (PointCloud &output) . In our commonly used pcl statistical filtering, voxel Filtering, radius filtering, etc. will all call the filter function of the base class.
The implementation of the filter() function is as follows:
inline void
filter (PointCloud &output)
{
if (!initCompute ())
return;
if (input_.get () == &output) // cloud_in = cloud_out
{
PointCloud output_temp;
applyFilter (output_temp);
output_temp.header = input_->header;
output_temp.sensor_origin_ = input_->sensor_origin_;
output_temp.sensor_orientation_ = input_->sensor_orientation_;
pcl::copyPointCloud (output_temp, output);
}
else
{
output.header = input_->header;
output.sensor_origin_ = input_->sensor_origin_;
output.sensor_orientation_ = input_->sensor_orientation_;
applyFilter (output);
}
deinitCompute ();
}The applyFilter() function is called in filter. The applyFilter() function here is the specific implementation of each filtering algorithm. applyFilter() is a virtual function. It is a pure virtual function in the base class: virtual void applyFilter (PointCloud &output) = 0;, Specific methods are implemented in each filter derived class. Under normal circumstances, a derived class can call the member functions of the base class. It is worth noting here that the base class in turn calls the member functions of the derived class through pure virtual functions.
(2) Class pcl::Clipper3D< PointT >
Class Clipper3D is the base class for 3D space clipping objects.
(3) Class pcl::FilterIndices< PointT >
Class FilterIndices is a base class for eliminating point sets that meet certain limits.
5.3 Classification of PCL filtering algorithms
Reference article: pcl filter module architecture analysis and algorithm summary_pcl filter algorithm_xinxiangwangzhi_'s blog-CSDN blog
PCL filtering algorithms are mainly divided into three categories: filtering type, sampling type, spatial clipper, and others
(1) Filter class
◆ Bilateral filtering , fast bilateral filtering, omp accelerated fast bilateral filtering (bilateral filtering in pcl requires the use of intensity information)
bilateral.hpp
fast_bilateral.hpp
fast_bilateral_omp.hpp◆ Radius filtering
radius_outlier_removal.hpp◆ Median filter
median_filter.hpp◆ Statistical filtering
statistical_outlier_removal.hpp◆ Straight-through filtering, conditional filtering
These two principles are consistent, and they are filtered based on the attributes of points (filed) such as xyz normal line curvature histogram attributes, etc.
passthrough.hpp
conditional_removal.hpp◆ Model filtering
Set a specific model as a filter and fit it according to the set model (such as line and surface) to extract the point cloud of the model.
model_outlier_removal.hpp◆ Projection filtering
Set a specific geometric model as a filter and project the point cloud to the model
project_inliers.hpp◆ Morphological filtering
Open and close point clouds
morphological_filter.hpp◆ minimum z value sampling
Create a two-dimensional grid for the point cloud and select the point with the smallest z value in the grid, which can be used to analyze multi-echo information and extract the ground surface
grid_minimum.hpp◆ Local maximum z value sampling
Search the point cloud for radius and delete the point with the largest z value among the nearest neighbors, which can be used to extract the ground
local_maximum.hpp
(2) Sampling class (mostly used for registration or other post-processing of point clouds)
◆ Voxel sampling
4 types of voxel filtering
approximate_voxel_grid.hpp
voxel_grid.hpp
voxel_grid_covariance.hpp ["The Three-Dimensional Normal-Distributions Transform an Efficient Representation for Registration"]
voxel_grid_label.cpp◆ Uniform sampling
uniform_sampling.hpp◆ Random sampling
random_sample.hpp◆ Voxel normal sampling
Divide the input space into grids until each grid contains at most N points, and randomly sample points within each grid. A normal is calculated using N points of each grid. All points sampled within the raster have normals equal to this normal.
sampling_surface_normal.hpp◆ Normal Space Uniform Sampling (NSS)
Arrange the point cloud in normal space and then uniformly sample the point cloud
normal_space.hpp[“Efficient variants of the ICP algorithm”]◆ Point cloud sampling based on 6D covariance
The algorithm incrementally adds points to the generated point cloud while trying to keep all 6 eigenvalues of the covariance matrix as close to each other as possible
covariance_sampling.hpp["Geometrically Stable Sampling for the ICP Algorithm"]◆ Pyramid sampling
A multiscale representation of organized point clouds using an iterative smoothing downsampling algorithm.
pyramid.hpp(3) Space clipper
◆ Cone cutter
Frustum culling filters points within a frustum given by the camera pose and field of view.
frustum_culling.hpp◆ Affine transformation 3D bounding box cropper
It is necessary to give an affine transformation matrix, perform affine transformation on the unit bounding box of the origin, and obtain the point cloud in the bounding box after affine transformation.
box_clipper3D.hpp◆ Flat cutter
plane_clipper3D.hpp◆ Bounding box clipper
Get the point cloud within the specified bounding box
crop_box.hpp◆ Convex Hull Cutter
First, build a convex hull to obtain a point cloud on or outside a 3D closed surface or a 2D closed polygon.
crop_hull.hpp(4) Others
◆ Normal optimization
Each normal is updated with the (weighted) average of all normals in its neighborhood to optimize the estimated normals.
normal_refinement.hpp◆ Point cloud convolution
convolution.hpp
convolution_3d.hpp◆ Extract index point cloud
extract_indices.hpp◆ Estimate the occluded area
voxel_grid_occlusion_estimation.hpp◆ Remove ghost points that appear on edge discontinuities
shadowpoints.hpp