Module keypoints

Detailed Description

Overview

The pcl_keypoints library contains implementations of two point cloud keypoint detection algorithms. Keypoints (also referred to as interest points) are points in an image or point cloud that are stable, distinctive, and can be identified using a well-defined detection criteron. Typically, the number of interest points in a point cloud will be much smaller than the total number of points in the cloud, and when used in combination with local feature descriptors at each keypoint, the keypoints and descriptors can be used to form a compact—yet descriptive—representation of the original data.

Requirements

• common
• search
• kdtree
• octree
• range_image
• features
• filters

Classes
class	pcl::HarrisKeypoint3D< PointInT, PointOutT, NormalT >
	HarrisKeypoint3D uses the idea of 2D Harris keypoints, but instead of using image gradients, it uses surface normals. More...
class	pcl::Keypoint< PointInT, PointOutT >
	Keypoint represents the base class for key points. More...
class	pcl::NarfKeypoint
	NARF (Normal Aligned Radial Feature) keypoints. More...
class	pcl::SIFTKeypoint< PointInT, PointOutT >
	SIFTKeypoint detects the Scale Invariant Feature Transform keypoints for a given point cloud dataset containing points and intensity. More...
class	pcl::UniformSampling< PointInT >
	UniformSampling assembles a local 3D grid over a given PointCloud, and downsamples + filters the data. More...

Functions
std::ostream &	pcl::operator<< (std::ostream &os, const NarfKeypoint::Parameters &p)

Function Documentation

std::ostream& pcl::operator<< ( std::ostream &  os, const NarfKeypoint::Parameters &  p  )

inline

Definition at line 191 of file narf_keypoint.h.