ACloudViewer  3.9.4
A Modern Library for 3D Data Processing
Public Types | Public Member Functions | Public Attributes | List of all members
Parameters Struct Reference

Algorithm parameters. More...

Public Types

enum  BoundaryType { FREE , DIRICHLET , NEUMANN , COUNT }
 Boundary types. More...
 

Public Member Functions

 Parameters ()=default
 Default initializer. More...
 

Public Attributes

BoundaryType boundary = NEUMANN
 Boundary type for the finite elements. More...
 
int depth = 8
 
float finestCellWidth = 0.0f
 
float scale = 1.1f
 
float samplesPerNode = 1.5f
 
float pointWeight = 2.0f
 
int iters = 8
 The number of solver iterations. More...
 
bool density = false
 
bool withColors = true
 
float colorPullFactor = 32.0f
 Data pull factor. More...
 
float normalConfidence = 0.0
 Normal confidence exponent. More...
 
float normalConfidenceBias = 0.0
 Normal confidence bias exponent. More...
 
bool linearFit = false
 
int threads = 1
 
int fullDepth = 5
 The depth beyond which the octree will be adapted. More...
 
int baseDepth = 0
 Coarse MG solver depth. More...
 
int baseVCycles = 1
 Coarse MG solver v-cycles. More...
 
float cgAccuracy = 1.0e-3f
 This flag specifies the accuracy cut-off to be used for CG. More...
 

Detailed Description

Algorithm parameters.

Definition at line 43 of file ecvPoissonReconDlg.cpp.

Member Enumeration Documentation

◆ BoundaryType

enum Parameters::BoundaryType

Boundary types.

Enumerator
FREE 
DIRICHLET 
NEUMANN 
COUNT 

Definition at line 48 of file ecvPoissonReconDlg.cpp.

Constructor & Destructor Documentation

◆ Parameters()

Parameters::Parameters ( )
default

Default initializer.

Member Data Documentation

◆ baseDepth

int Parameters::baseDepth = 0

Coarse MG solver depth.

Definition at line 140 of file ecvPoissonReconDlg.cpp.

◆ baseVCycles

int Parameters::baseVCycles = 1

Coarse MG solver v-cycles.

Definition at line 143 of file ecvPoissonReconDlg.cpp.

◆ boundary

BoundaryType Parameters::boundary = NEUMANN

Boundary type for the finite elements.

Definition at line 51 of file ecvPoissonReconDlg.cpp.

Referenced by ecvPoissonReconDlg::adjustParams(), doReconstruct(), and ecvPoissonReconDlg::updateParams().

◆ cgAccuracy

float Parameters::cgAccuracy = 1.0e-3f

This flag specifies the accuracy cut-off to be used for CG.

Definition at line 146 of file ecvPoissonReconDlg.cpp.

◆ colorPullFactor

float Parameters::colorPullFactor = 32.0f

Data pull factor.

If withColors is rue, this floating point value specifies the relative importance of finer color estimates over lower ones.

Definition at line 107 of file ecvPoissonReconDlg.cpp.

◆ density

bool Parameters::density = false

If this flag is enabled, the sampling density is written out with the vertices

Definition at line 97 of file ecvPoissonReconDlg.cpp.

Referenced by ecvPoissonReconDlg::adjustParams(), doReconstruct(), ecvPoissonReconDlg::start(), and ecvPoissonReconDlg::updateParams().

◆ depth

int Parameters::depth = 8

The maximum depth of the tree that will be used for surface reconstruction Running at depth d corresponds to solving on a 2^d x 2^d x 2^d. Note that since the reconstructor adapts the octree to the sampling density, the specified reconstruction depth is only an upper bound.

Definition at line 59 of file ecvPoissonReconDlg.cpp.

Referenced by ecvPoissonReconDlg::adjustParams(), ecvPoissonReconDlg::doComputation(), doReconstruct(), ecvPoissonReconDlg::start(), and ecvPoissonReconDlg::updateParams().

◆ finestCellWidth

float Parameters::finestCellWidth = 0.0f

The target width of the finest level octree cells (ignored if depth is specified)

Definition at line 63 of file ecvPoissonReconDlg.cpp.

Referenced by ecvPoissonReconDlg::doComputation(), doReconstruct(), and ecvPoissonReconDlg::updateParams().

◆ fullDepth

int Parameters::fullDepth = 5

The depth beyond which the octree will be adapted.

The parameters below are accessible via the command line but are not described in the official documentation At coarser depths, the octree will be complete, containing all 2^d x 2^d x 2^d nodes.

Definition at line 137 of file ecvPoissonReconDlg.cpp.

◆ iters

int Parameters::iters = 8

The number of solver iterations.

Number of Gauss-Seidel relaxations to be performed at each level of the octree hierarchy.

Definition at line 93 of file ecvPoissonReconDlg.cpp.

◆ linearFit

bool Parameters::linearFit = false

Enabling this flag has the reconstructor use linear interpolation to estimate the positions of iso-vertices.

Definition at line 124 of file ecvPoissonReconDlg.cpp.

Referenced by ecvPoissonReconDlg::adjustParams(), doReconstruct(), and ecvPoissonReconDlg::updateParams().

◆ normalConfidence

float Parameters::normalConfidence = 0.0

Normal confidence exponent.

Exponent to be applied to a point's confidence to adjust its weight. (A point's confidence is defined by the magnitude of its normal.)

Definition at line 113 of file ecvPoissonReconDlg.cpp.

◆ normalConfidenceBias

float Parameters::normalConfidenceBias = 0.0

Normal confidence bias exponent.

Exponent to be applied to a point's confidence to bias the resolution at which the sample contributes to the linear system. (Points with lower confidence are biased to contribute at coarser resolutions.)

Definition at line 120 of file ecvPoissonReconDlg.cpp.

◆ pointWeight

float Parameters::pointWeight = 2.0f

The importance that interpolation of the point samples is given in the formulation of the screened Poisson equation. The results of the original (unscreened) Poisson Reconstruction can be obtained by setting this value to 0.

Definition at line 87 of file ecvPoissonReconDlg.cpp.

Referenced by ecvPoissonReconDlg::adjustParams(), doReconstruct(), and ecvPoissonReconDlg::updateParams().

◆ samplesPerNode

float Parameters::samplesPerNode = 1.5f

The minimum number of sample points that should fall within an octree node as the octree construction is adapted to sampling density. This parameter specifies the minimum number of points that should fall within an octree node. For noise-free samples, small values in the range [1.0 - 5.0] can be used. For more noisy samples, larger values in the range [15.0 - 20.0] may be needed to provide a smoother, noise-reduced, reconstruction.

Definition at line 80 of file ecvPoissonReconDlg.cpp.

Referenced by ecvPoissonReconDlg::adjustParams(), doReconstruct(), and ecvPoissonReconDlg::updateParams().

◆ scale

float Parameters::scale = 1.1f

The ratio between the diameter of the cube used for reconstruction and the diameter of the samples' bounding cube. Specifies the factor of the bounding cube that the input samples should fit into.

Definition at line 70 of file ecvPoissonReconDlg.cpp.

Referenced by doReconstruct().

◆ threads

int Parameters::threads = 1

This parameter specifies the number of threads across which the solver should be parallelized

Definition at line 128 of file ecvPoissonReconDlg.cpp.

Referenced by ecvPoissonReconDlg::doComputation().

◆ withColors

bool Parameters::withColors = true

This flag tells the reconstructor to read in color values with the input points and extrapolate those to the vertices of the output.

Definition at line 101 of file ecvPoissonReconDlg.cpp.

Referenced by ecvPoissonReconDlg::adjustParams(), ecvPoissonReconDlg::start(), and ecvPoissonReconDlg::updateParams().

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