_3d

Detailed Description

Classes
class	cv::RegionGrowing3D
	Region Growing algorithm in 3D point cloud. More...
class	cv::SACSegmentation
	Sample Consensus algorithm segmentation of 3D point cloud model. More...

Enumerations
enum	cv::SacMethod { cv::SAC_METHOD_RANSAC }
	type of the robust estimation algorithm More...
enum	cv::SacModelType {   cv::SAC_MODEL_PLANE ,   cv::SAC_MODEL_SPHERE }

Functions
int	cv::farthestPointSampling (OutputArray sampled_point_flags, InputArray input_pts, float sampled_scale, float dist_lower_limit=0, RNG *rng=nullptr)
int	cv::farthestPointSampling (OutputArray sampled_point_flags, InputArray input_pts, int sampled_pts_size, float dist_lower_limit=0, RNG *rng=nullptr)
	Point cloud sampling by Farthest Point Sampling(FPS).
void	cv::normalEstimate (OutputArray normals, OutputArray curvatures, InputArray input_pts, InputArrayOfArrays nn_idx, int max_neighbor_num=0)
	Estimate the normal and curvature of each point in point cloud from NN results.
void	cv::randomSampling (OutputArray sampled_pts, InputArray input_pts, float sampled_scale, RNG *rng=nullptr)
void	cv::randomSampling (OutputArray sampled_pts, InputArray input_pts, int sampled_pts_size, RNG *rng=nullptr)
	Point cloud sampling by randomly select points.
int	cv::voxelGridSampling (OutputArray sampled_point_flags, InputArray input_pts, float length, float width, float height)
	Point cloud sampling by Voxel Grid filter downsampling.

Enumeration Type Documentation

SacMethod

enum cv::SacMethod

#include <opencv2/geometry/segment.hpp>

type of the robust estimation algorithm

Enumerator
SAC_METHOD_RANSAC  Python: cv.SAC_METHOD_RANSAC	The RANSAC algorithm described in [fischler1981random].

SacModelType

enum cv::SacModelType

#include <opencv2/geometry/segment.hpp>

Enumerator
SAC_MODEL_PLANE  Python: cv.SAC_MODEL_PLANE	The 3D PLANE model coefficients in list [a, b, c, d], corresponding to the coefficients of equation \( ax + by + cz + d = 0 \).
SAC_MODEL_SPHERE  Python: cv.SAC_MODEL_SPHERE	The 3D SPHERE model coefficients in list [center_x, center_y, center_z, radius], corresponding to the coefficients of equation \( (x - center\_x)^2 + (y - center\_y)^2 + (z - center\_z)^2 = radius^2 \).

Function Documentation

farthestPointSampling() [1/2]

int cv::farthestPointSampling	(	OutputArray	sampled_point_flags,
		InputArray	input_pts,
		float	sampled_scale,
		float	dist_lower_limit = 0,
		RNG *	rng = nullptr )

#include <opencv2/geometry/segment.hpp>

This is an overloaded member function, provided for convenience. It differs from the above function only in what argument(s) it accepts.

Parameters

[out]	sampled_point_flags	Flags of the sampled point, (pass in std::vector<int> or std::vector<char> etc.) sampled_point_flags[i] is 1 means i-th point selected, 0 means it is not selected.
	input_pts	Original point cloud, vector of Point3 or Mat of size Nx3/3xN.
	sampled_scale	Range (0, 1), the percentage of the sampled point cloud to the original size, that is, sampled size = original size * sampled_scale.
	dist_lower_limit	Sampling is terminated early if the distance from the farthest point to S is less than dist_lower_limit, default 0.
	rng	Optional random number generator used for selecting seed point for FPS; if it is nullptr, theRNG () is used instead.

Returns

The number of points actually sampled.

farthestPointSampling() [2/2]

int cv::farthestPointSampling	(	OutputArray	sampled_point_flags,
		InputArray	input_pts,
		int	sampled_pts_size,
		float	dist_lower_limit = 0,
		RNG *	rng = nullptr )

#include <opencv2/geometry/segment.hpp>

Point cloud sampling by Farthest Point Sampling(FPS).

FPS Algorithm:

• Input: Point cloud C, sampled_pts_size, dist_lower_limit
• Initialize: Set sampled point cloud S to the empty set
• Step:
  1. Randomly take a seed point from C and take it from C to S;
  2. Find a point in C that is the farthest away from S and take it from C to S; (The distance from point to set S is the smallest distance from point to all points in S)
  3. Repeat step 2 until the farthest distance of the point in C from S is less than dist_lower_limit, or the size of S is equal to sampled_pts_size.
• Output: Sampled point cloud S

Parameters

[out]	sampled_point_flags	Flags of the sampled point, (pass in std::vector<int> or std::vector<char> etc.) sampled_point_flags[i] is 1 means i-th point selected, 0 means it is not selected.
	input_pts	Original point cloud, vector of Point3 or Mat of size Nx3/3xN.
	sampled_pts_size	The desired point cloud size after sampling.
	dist_lower_limit	Sampling is terminated early if the distance from the farthest point to S is less than dist_lower_limit, default 0.
	rng	Optional random number generator used for selecting seed point for FPS; if it is nullptr, theRNG () is used instead.

Returns

The number of points actually sampled.

normalEstimate()

void cv::normalEstimate	(	OutputArray	normals,
		OutputArray	curvatures,
		InputArray	input_pts,
		InputArrayOfArrays	nn_idx,
		int	max_neighbor_num = 0 )

#include <opencv2/geometry/segment.hpp>

Estimate the normal and curvature of each point in point cloud from NN results.

Normal estimation by PCA:

• Input: Nearest neighbor points of a specific point: \( pt\_set \)
• Step:
  1. Calculate the \( mean(\bar{x},\bar{y},\bar{z}) \) of \( pt\_set \);
  2. A 3x3 covariance matrix \( cov \) is obtained by \( mean^T \cdot mean \);
  3. Calculate the eigenvalues( \( λ_2 \ge λ_1 \ge λ_0 \)) and corresponding eigenvectors( \( v_2, v_1, v_0 \)) of \( cov \);
  4. \( v0 \) is the normal of the specific point, \( \frac{λ_0}{λ_0 + λ_1 + λ_2} \) is the curvature of the specific point;
• Output: Normal and curvature of the specific point.

Parameters

[out]	normals	Normal of each point, support vector<Point3f> and Mat of size Nx3.
[out]	curvatures	Curvature of each point, support vector<float> and Mat.
	input_pts	Original point cloud, support vector<Point3f> and Mat of size Nx3/3xN.
	nn_idx	Index information of nearest neighbors of all points. The first nearest neighbor of each point is itself. Support vector<vector<int>>, vector<Mat> and Mat of size NxK. If the information in a row is [0, 2, 1, -5, -1, 4, 7 ... negative number], it will use only non-negative indexes until it meets a negative number or bound of this row i.e. [0, 2, 1].
	max_neighbor_num	The maximum number of neighbors want to use including itself. Setting to a non-positive number or default will use the information from nn_idx.

randomSampling() [1/2]

void cv::randomSampling	(	OutputArray	sampled_pts,
		InputArray	input_pts,
		float	sampled_scale,
		RNG *	rng = nullptr )

#include <opencv2/geometry/segment.hpp>

This is an overloaded member function, provided for convenience. It differs from the above function only in what argument(s) it accepts.

Parameters

sampled_pts	Point cloud after sampling. Support cv::Mat(size * sampled_scale, 3, CV_32F), std::vector<cv::Point3f>.
input_pts	Original point cloud, vector of Point3 or Mat of size Nx3/3xN.
sampled_scale	Range (0, 1), the percentage of the sampled point cloud to the original size, that is, sampled size = original size * sampled_scale.
rng	Optional random number generator used for cv::randShuffle; if it is nullptr, theRNG () is used instead.

randomSampling() [2/2]

void cv::randomSampling	(	OutputArray	sampled_pts,
		InputArray	input_pts,
		int	sampled_pts_size,
		RNG *	rng = nullptr )

#include <opencv2/geometry/segment.hpp>

Point cloud sampling by randomly select points.

Use cv::randShuffle to shuffle the point index list, then take the points corresponding to the front part of the list.

Parameters

sampled_pts	Point cloud after sampling. Support cv::Mat(sampled_pts_size, 3, CV_32F), std::vector<cv::Point3f>.
input_pts	Original point cloud, vector of Point3 or Mat of size Nx3/3xN.
sampled_pts_size	The desired point cloud size after sampling.
rng	Optional random number generator used for cv::randShuffle; if it is nullptr, theRNG () is used instead.

voxelGridSampling()

int cv::voxelGridSampling	(	OutputArray	sampled_point_flags,
		InputArray	input_pts,
		float	length,
		float	width,
		float	height )

#include <opencv2/geometry/segment.hpp>

Point cloud sampling by Voxel Grid filter downsampling.

Creates a 3D voxel grid (a set of tiny 3D boxes in space) over the input point cloud data, in each voxel (i.e., 3D box), all the points present will be approximated (i.e., downsampled) with the point closest to their centroid.

Parameters

[out]	sampled_point_flags	Flags of the sampled point, (pass in std::vector<int> or std::vector<char> etc.) sampled_point_flags[i] is 1 means i-th point selected, 0 means it is not selected.
	input_pts	Original point cloud, vector of Point3 or Mat of size Nx3/3xN.
	length	Grid length.
	width	Grid width.
	height	Grid height.

Returns

The number of points actually sampled.