Fade2.5D Documentation pages v1.86
Delaunay Features
GEOM_FADE25D::EfficientModel Class Reference

EfficientModel (DEPRECATED in favor of the new CloudPrepare class) More...

#include <EfficientModel.h>

Public Member Functions

 EfficientModel (const std::vector< Point2 > &vPoints)
 
void extract (double maxError, std::vector< Point2 > &vEfficientPointsOut)
 Extract a subset of points. More...
 
void zSmoothing (int numIterations, double maxDifferencePerIteration, SmoothingStrategy sms)
 Smoothing. More...
 

Protected Member Functions

void go ()
 
int insertKeepError (double factor, double err, std::vector< Point2 * > &vIn, std::vector< Point2 * > &vNeedlessBigError, std::vector< Point2 * > &vNeedlessSmallError)
 
void insertMinHull ()
 
void part1_extractFC ()
 
void part2_setWeights ()
 
void show (const char *name)
 
void solveCand (Candidate *pCand, double maxErr)
 
void sortVtx (std::vector< Point2 * > &vVtx)
 

Detailed Description

Note
This class is deprecated but is kept for backward compatibility with existing software. Please use the new CloudPrepare class which is much faster and also more memory efficient.

Member Function Documentation

◆ extract()

void GEOM_FADE25D::EfficientModel::extract ( double  maxError,
std::vector< Point2 > &  vEfficientPointsOut 
)

This method extracts a subset of the original point cloud that represents the model more efficiently. Thereby the original and the simplified model cover the same area.

Parameters
maxErroris the maximum height difference between the original points and the simplified model.
[out]vEfficientPointsOutis used to return a subset of the original points that represents the model more efficiently.
Note
When maxError is tiny i.e., below the noise level of the point cloud, then processing can take quite some time. Consider using the zSmoothing() method before.

◆ zSmoothing()

void GEOM_FADE25D::EfficientModel::zSmoothing ( int  numIterations,
double  maxDifferencePerIteration,
SmoothingStrategy  sms 
)

This method should be used before extract(). It adapts the z-values according to the chosen SmoothingStrategy sms.

Parameters
numIterationsNumber of iterations
maxDifferencePerIterationis the maximum change of any z-value
smsis one of SMST_MINIMUM, SMST_MAXIMUM, SMST_MEDIAN, SMST_AVERAGE

The documentation for this class was generated from the following file: