select Select a subset of points.
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DESCRIPTION/NOTES
The property 'act' of the point cloud object, i.e. obj.act, is a n-by-1
logical vector defining for each point if it is active (true) or not active
(false). Several selection strategies are provided in this function. All
methods (except 'All') select a subset of the active points, i.e. not active
points don't change their status.
The available selection strategies are:
1 'All' -> Select all points.
2 'None' -> Deactivate all points.
3 'RandomSampling' -> Random sampling of points.
4 'IntervalSampling' -> Select each n-th point, e.g. each 10-th point.
5 'UniformSampling' -> Uniform sampling of points in space.
6 'MaxLeverageSampling' -> Selection of points based on their 'leverages'.
7 'NormalSampling' -> Selection of points based on the normal vector.
8 'Attribute' -> Selection of points based on an attribute.
9 'Limits' -> Selection of points based on coordinate limits.
10 'InPolygon' -> Selection of points inside a 2d polygonal region.
11 'InVoxelHull' -> Selection of points included in a voxel hull.
12 'RangeSearch' -> Selection of points within the range of another
point cloud.
13 'KnnSearch' -> Selection of K nearest neighbors for each point
of another point cloud.
14 'GeoTiff' -> Selection of points based on a GeoTiff file.
15 'Profile' -> Selection of points in a vertical profile.
16 'ByIDs' -> Selection of points by point IDs.
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1 'All'
DESCRIPTION Select all points. No further inputs required.
EXAMPLE pc = pointCloud('Lion.xyz');
pc.select('All');
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2 'None'
DESCRIPTION Deactivate all points. No further inputs required.
EXAMPLE pc = pointCloud('Lion.xyz');
pc.select('None');
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3 'RandomSampling'
DESCRIPTION Random sampling of a percentage of points.
INPUT [percentPoi]
Percentage of points to select.
EXAMPLE Select 5 percent of all points.
pc = pointCloud('Lion.xyz');
pc.select('RandomSampling', 5);
pc.plot;
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4 'IntervalSampling'
DESCRIPTION Select each n-th point based upon ordering of points in obj.X.
INPUT [nthPoi]
Defintion of interval in which points are selected.
EXAMPLE Select each 50-th point.
pc = pointCloud('Lion.xyz');
pc.select('IntervalSampling', 50);
pc.plot;
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5 'UniformSampling'
DESCRIPTION Uniform sampling of points in space. This gives a homogeneous
distribution of the selected points. For this strategy a voxel
structure is derived from the point cloud and the points closest
to each voxel center are selected. Consequently, the mean
sampling distance corresponds to the edge length of the voxels.
INPUT [voxelSize]
Edge lenght of voxels used for the selection of points. Equal to
mean sampling distance in each coordinate direction.
EXAMPLE Select points with a mean sampling distance of 3mm.
pc = pointCloud('Lion.xyz');
pc.select('UniformSampling', 3);
pc.plot('MarkerSize', 5);
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6 'MaxLeverageSampling'
DESCRIPTION This special selection strategy is only relevant if points are
used for the estimation of transformation parameters (e.g. for
the Iterative Closest Point algorithm). For details upon this
selection strategy see [1]. The normal vectors are required for
this option.
INPUT [percentPoi]
Percentage of points to select.
EXAMPLE Select geometrically stable points.
pc = pointCloud('Lion.xyz');
pc.select('UniformSampling', 2);
pc.normals(1);
pc.select('MaxLeverageSampling', 25);
pc.plot('MarkerSize', 5);
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7 'NormalSampling'
DESCRIPTION The aim of this strategy is to select points such that the
distribution of their normals in angular space is as large as
possible. For this the angular space (exposition=x vs. slope=y)
is divided into classes of equal angular extension (e.g.
10 degree x 10 degree), and points are uniformly sampled among
these classes. The normal vectors are required for this option.
INPUT 1 [percentPoi]
Percentage of points to select.
2 ['DeltaAngleExposition' deltaAngleExposition]
Class width on exposition axis (=x).
3 ['DeltaAngleSlope' deltaAngleSlope]
Class width on slope axis (=y).
EXAMPLES 1 Select 5 percent of all points.
pc = pointCloud('Lion.xyz');
pc.select('UniformSampling', 2);
pc.normals(1);
pc.select('NormalSampling', 10);
pc.plot('MarkerSize', 5);
2 Define class widths.
pc = pointCloud('Lion.xyz');
pc.select('UniformSampling', 2);
pc.normals(1);
pc.select('NormalSampling', 10, ...
'DeltaAngleExposition', 10, ...
'DeltaAngleSlope' , 5);
pc.plot('MarkerSize', 5);
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8 'Attribute'
DESCRIPTION Selection of points based on attribute values. The attribute has
to be a field of the structure obj.A, e.g. obj.A.roughness.
INPUTS 1 [attributeName]
Name of attribute as char.
2 [attributeMinMax]
Limits of attribute defined as vector with 2 elements.
EXAMPLE Selection of points based on their roughness attribute.
pc = pointCloud('Lion.xyz', 'Attributes', {'nx' 'ny' 'nz' 'roughness'});
% Note: the imported attributes are saved as fields in the
% structure pc.A, e.g. the roughness in saved in pc.A.roughness.
pc.select('Attribute', 'roughness', [0.01 0.3]);
pc.plot;
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9 'Limits'
DESCRIPTION Selection of points within a selection window. The window is
defined by its coordinate limits in x, y and z.
INPUT 1 [limitsMinMax]
Selection window as 3-by-2 matrix: [minX maxX
minY maxY
minZ maxZ]
2 ['Reduced', reduced]
Logical value which specifies if limits are given in reduced
coordinates (true) or not (false=default).
EXAMPLE Selection of the lions head.
pc = pointCloud('Lion.xyz');
pc.select('Limits', [-Inf -10; -30 20; -10 Inf]);
pc.plot;
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10 'InPolygon'
DESCRIPTION Selection of points inside of a 2d polygonal region.
INPUT [polygon]
Matrix of size n-by-2, where each row contains the x and y
coordinates of one polygon vertex.
EXAMPLE Selection of the lions tail.
pc = pointCloud('Lion.xyz');
pc.select('InPolygon', [45 4; 45 -7; 56 -7; 71 -2; 71 4; 63 6]);
pc.plot;
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11 'InVoxelHull'
DESCRIPTION Selection of points inside of a specified voxel hull. This
method can be used to select points in parts which are common to
another point cloud, i.e. in the overlapping areas of two point
clouds.
INPUT 1 [voxelHull]
Voxel hull of a point cloud as a n-by-3 matrix. For the
computation of a voxel hull the method getVoxelHull is
recommended, see 'help pointCloud.getVoxelHull'.
2 [voxelSize]
Edge length of voxels.
EXAMPLE Two point clouds are given. Select those points of the second
point cloud, which are overlapping to the first point cloud.
pcScan1 = pointCloud('LionScan1.xyz');
pcScan2 = pointCloud('LionScan2.xyz');
pcScan1.plot('Color', 'r');
pcScan2.plot('Color', 'b');
pcScan1.getVoxelHull(2);
pcScan2.select('InVoxelHull', pcScan1.voxelHull, ...
pcScan1.voxelHullVoxelSize);
pcScan2.plot('Color', 'm');
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12 'RangeSearch'
DESCRIPTION Selection of all points within a specified distance from another
point cloud. This method is based upon the official function
'rangesearch'.
INPUT 1 [points]
Point cloud as a n-by-3 matrix.
2 [searchRadius]
Search radius.
3 ['Distance', distance]
String or function handle specifying the distance metric. Run
doc rangesearch for further details.
EXAMPLE First select a subset of points with uniform sampling, then
search all points within the range of 1 from these points.
pc = pointCloud('Lion.xyz');
pc.select('UniformSampling', 5);
points = pc.X(pc.act,:);
pc.select('All');
pc.select('RangeSearch', points, 1);
pc.plot;
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13 'KnnSearch'
DESCRIPTION Selection of K nearest neighbors for each point of another point
cloud. This method is based upon the official function
'knnsearch'.
INPUT 1 [points]
Point cloud as a n-by-3 matrix.
2 ['K', k]
Number of nearest neighbors to search.
3 ['Distance', distance]
String or function handle specifying the distance metric. Run
doc rangesearch for further details.
OUTPUT [Distances]
Distances to nearest neighbors n-by-k matrix.
EXAMPLE First select a subset of points with uniform sampling, then
search the 500 nearest neighbors of these points.
pc = pointCloud('Lion.xyz');
pc.select('UniformSampling', 10);
points = pc.X(pc.act,:);
pc.select('All');
pc.select('KnnSearch', points, 'K', 500);
pc.plot;
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14 'GeoTiff'
DESCRIPTION Selection of points within specific cells of a geotiff file. For
this strategy the Mapping Toolbox is required.
INPUTS 1 [source]
Source of geotiff file. Two possibilities are offered:
1 Path to a geotiff file defined as char, e.g. 'C:\map.tif'.
2 Cell containing objects A and R (see doc geotiffread),
e.g. {A R}.
2 [minMax]
Limits of cell values in which the points should be selected
as vector with 2 elements.
3 ['RedPoi', redPoi]
Optional reduction point of geotiff file specified as vector
with 3 elements.
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15 'Profile'
DESCRIPTION Selection of points within a vertical profile. The profile
is defined by start point (x/y), end point (x,y) and width.
INPUTS 1 [lineStart]
Starting point of profile defined as vector with 2 elements:
[startX startY].
2 [lineEnd]
Ending point of profile defined as vector with 2 elements:
[endX endY].
3 [lineWidth]
Width of profile.
OUTPUT [Azimuth]
Azimuth of profile. The azimuth can be used for the
visualization of the profile with view(azimuth, 0).
EXAMPLE Profile through point cloud.
pc = pointCloud('Lion.xyz');
az = pc.select('Profile', [ 100 0], [-100 0], 2);
pc.plot; view(az,0);
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16 'ByIDs'
DESCRIPTION Selection of points by point IDs. For this, the point IDs have
to be saved as point attribute 'id'.
INPUTS [IDs2select]
Vector of point IDs to select.
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REFERENCES
[1] Glira, P., Pfeifer, N., Ressl, C., Briese, C. (2015): A correspondence
framework for ALS strip adjustments based on variants of the ICP
algorithm. In: Journal for Photogrammetry, Remote Sensing and
Geoinformation Science (PFG) 2015(04), pp. 275-289.
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philipp.glira@gmail.com
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