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 * Added some empty lines between methods in code and header files.
 *
 * Bugfix: Solved problem in point data saving that the coordinate precision was about 5
 * signs.
 *
 * Sebastian Eichelbaum wrote (18.09.2014, 11:46):
 * >> Hi Andreas
 * >>
 * >> Ich würde dich gern bitten darauf zu achten, dass du zwischen Funktionen
 * >> eine Leerzeile lässt. Das betrifft sowohl die CPP als auch die Header
 * >> files. Als Beispiel: WBmpImage.cpp. Dort hast du nach dem Konstruktor
 * >> eine Leerzeile gemacht, nach dem Destruktor und den folgenden Funktionen
 * >> jedoch nicht.
 * >>
 * >> Ansonsten ist dein Stil sehr sauber. Auch die Doxygen Doku ist sehr
 * >> ordentlich. Super.
 * >>
 * >> Gruß
 * >> Sebastian

 * Modules purged
 *   - Points group selector
 *
 * Altered modules
 *   - Points - Transform: Can now save and load point data using files
 *
 * Modules created:
 *   - Point Groups - Validator
 *       - Is done in order to validate groups against reference groups
 *   - Point Groups - Transform
 *       - Can save and load grouped point data using files
 *       - Also can merge grouped point data sets and subtract point sets
 *       - Still has all features to replace "Point group selector" completely

 * General style Some style improvements.
 *
 * General features:
 *   - Points - Transform: Color equalizer (contrast and offset setting for each color
 *     channel)
 *
 * Performance improvements (in general about 4 times faster)
 *   - Lari/Habib 2014 clustering has now following improvements.
 *       - Removing points from the parameter space kd-tree after adding to a plane -
 *         This change created an additional, temporary parameter domain kd-tree which
 *         is in the beginning just a copy of the original parameter domain kd-tree.
 *         When points are added to a plane, they are removed from that copy. It makes
 *         the algorithm a way faster.
 *       - Distinguishing whether a point belongs to an extent directly in an point
 *         searcher instance works faster than building a list with additional
 *         unnecessary points and filtering afterwards. In that change a new class
 *         inherited from WPointSearcher and overwrote a method which distinguishes
 *         whether points are added or not.
 *       - Another story is counting points of an extent with a searched point as peak
 *         centre. Previously points were put in a list in order to count them
 *         afterwards. This new class was further modified at traversing. So no new
 *         unnecessary list is built up.
 *
 * Bugs fixed:
 *   - Boundary detection problems were fixed. Originally there was a detection that
 *     checked whether a new bound would hit the pre previously detectet bound. But that
 *     seemet practically not to be enough. So every point adding a new bound is checked
 *     whether it hits one of all bound pieces detected. Nevertheless the problem is not
 *     solved completely but there are much less less problems by far.

 * Previously application crashed every time on processing data using Lari/Habib (2014) surface detection
 *
 * In method WMSurfaceDetectionByLari::properties():
 * WPropInt m_cpuThreadCount = m_properties->addProperty( "CPU threads: ", "", 8, m_propCondition );
 *
 * Yes, m_cpuThreadCount is a member variable of the class. Putting "WPropInt" here is
 * definitely not a good idea.

 * Finalized main features
 *
 * New added features:
 *   - Brute Force peak detection clustering
 *   - Modified convex hull boundary detection for separation of spatially disconnected
 *     points.
 *
 * Reached beta version status.

 * Finalized main features
 *
 * New added features:
 *   - Brute Force peak detection clustering
 *   - Modified convex hull boundary detection for separation of spatially disconnected
 *     points.
 *
 * Reached beta version status.

 * Finalized main features
 *
 * New added features:
 *   - Brute Force peak detection clustering
 *   - Modified convex hull boundary detection for separation of spatially disconnected
 *     points.
 *
 * Reached beta version status.

 * Finalized main features
 *
 * New added features:
 *   - Brute Force peak detection clustering
 *   - Modified convex hull boundary detection for separation of spatially disconnected
 *     points.
 *
 * Reached beta version status.

 * Steps done:
 *   - Implemented the plane point clustering approach of Lari/Habib
 *
 * Probable remaining steps that remain to be implemented:
 *   - Applying a modified convex hull boundary detection algorithm to separate
 *     Spatially disconnected planar patches that were detected to belong to the same
 *     Planar formula.

[ADD #371] Lari/Habib approach module can display point's best fitted planes and the parameter domain
 * Added features:
 *   - Displaying the best for each pointfitted plane (calculated using the least
 *     squares method in relation th neighbor points)
 *   - The parameter domain points can be displayed now. Each oof them displays the best
 *   - fitted plane properties
 *
 * Bug fix:
 *   - Fixed the Hessesche normal formula calculation. Previously A, B and C were
 *     normalized just like a normal vector (Actually they depict a normal vector)

 * First module: Creates a sphere or cube of random points of the radius of 2.
 * Second module: Visualizes the least squares process by putting the output plane to
 * the triangle mesh output.
 * Transformed the points cropping module in order to be able to stretch and rotate the
 * point set

 * First module: Creates a sphere or cube of random points of the radius of 2.
 * Second module: Visualizes the least squares process by putting the output plane to
 * the triangle mesh output.
 * Transformed the points cropping module in order to be able to stretch and rotate the
 * point set.

[FIX #373] Due to the update change of #373 there was a minor update bug: When new data was connected, no update took place.

 * This approach describes the surface detection of Zahra Lari and Ayman Habib (2014).
 * Paper: "An adaptive approach for the segmentation and extraction of planar
 * andlinear/cylindrical features from laser scanning data", ISPRS Journal of
 * Photogrammetry and Remote Sensing, 2014
 *
 * Eventually the process covers also the linear and cylindrical feature segmentation
 * but in our requirements only the planar segmentation is required. Steps:
 *   - Original laser point cloud as input data
 *   - PCA-based classification of planar features
 *   - Selection of appropriate representation models for the detected planar features
 *   - Local point density estimation along the detected planar features
 *   - Precise estimation of the planar features segmentation attributes
 *   - Parameter-domain segmentation and extraction of planar features
 *   - Boundary tracking for resolving the parameter-domain segmentation ambiguities of
 *     planar features
 *   - Extracted planar features in spatial domain
 *
 * Estimations (Based on the paper because I haven't completely impelemented yet):
 *   - Fair segmentation results
 *   - Relatively slow
 *
 * Implementation progress:
 *   - Done to the step "Precise estimation of the planar features segmentation
 *     attributes".
 *
 *
 * Other updates:
 *   - Renamed module "Pooints - Crop" to "Points - Transform". It optained the feature
 *     to translate and to rotate the point set.
 *   - Wew code refactoring.

 * Currently the module Surface detection by PCL only gives the ability to detect
 * surfaces using point clouds (WDataSetPoints) what is a method of a fair quality.
 *
 * Some few changes across other modules:
 *   - Improvement on the octree neighbor search algorithm. Now you can look for
 *     neighbors using the neighborship of 6, 18 or 26
 *   - Very first steps on the surface detection algorithm "An adaptive approach for the
 *     segmentation and extraction of planar and linear/cylindrical features from laser
 *     scanning data" of Lari/Habib (2014).
 *   - Added an unidimensional kd tree structures with some processing methods such as
 *     looking for nearest nghbors.

 * I changed in the WPrincipalComponentAnalysis analyzeData(const vector<WPosition>) to
 * analyzeData(const vector<WPosition>*) in order to avoid unnecessary copies. Therefore
 * some classes had to be changed to remain compilable.

 * Added a new outline method to the wall detection. Voxels or properties of its
 * input point data can be displayed using rhombs. They display the wall node group,
 * Eigen Vectors, Eigen Values and the mean coordinate.
 *
 * Applied fixes:
 * The wall detection failed by bleeding through several surfaces. The problem was
 * that no angle condition was applied during merging wall groups.
 * Also small cleanups were applied on the code.

 * The algorithm is intended to grouup voxels that have surfaces with the same
 * normal vector. This feature helps to remove small groups. Obviously trees
 * by nature have points that lead the Principal Component Analysis to
 * surfaces that have very different Eigen Vectors of the smallest Eigen Value
 * that is the normal vector in the case of surfaces.
 *
 * Other feature: Removing voxels by a point count per voxel.

 * The new module firstly puts point data into a voxel grid. For each of these areas
 * the Principal Component Analysis is run. For each voxel the Eigen Vector of the
 * smallest Eigen Value is taken. In the case of surface kins data it's the normal
 * vector. In the last steps voxels with the similar vector are grouped.
 * Currently the region growing often bleeds over through many surfaces.

 * This algorithm puts input point coulds to a voxel grid and applies the Principal
 * Component Analysis to each of these areas. It's calculated how isotropic voxels are.
 * Either planar voxels have the blue color and isotropic ones are red. There's a
 * feature to cut avay voxels from the output triangle mesh for the view. You can either
 * set the minimal point count per voxel or the quotient of the smallest Eigen value over
 * the biggest one as the maximal isotropic level criteria.

[CHANGE] Many improvements over time. Outlining, Stratified and regular samplers, shader improvements and shader debugging code,...

[ADD] Global Boy coloring, as well as custom colors in StartEnd Coloring. This enables illumination for singe color as well.

Following bugfixes new features:
  - Points group selector plugin: Can output selected buildings as
    WDataSetPointsGrouped in order to be able to view data in other plug-ins
  - Elevation image export plugin: Features to switch elevation display
    in triangle mesh height and color.
  - Fixes in documentation and few code cleanup.

The buildings detection plugin generally was a mix of many things. These things of
it were split up into these plutins:
  - Elevation image export
  - Points group selector which transforms building groups into a voxel structure
    and exports it to a triangle mesh.

Added features:
  - Elevation image can be output in a triangle mesh
  - Elevation image (file and triangle mesh output) and building outline got colored
    building outline

Code style:
  - Fixed prooblems with documentation (make doc)
  - Purged last code style (make stylecheck)