/// <summary>
/// Algorithm based on ignoring points with less neighbours int thresholdNeighboursCount = 10; float thresholdDistance = 15e-5f;
/// at given distance.
/// </summary>
/// <param name="source"></param>
/// <param name="threshold"></param>
/// <returns></returns>
public static PointCloud ByLessNeighbours(PointCloud source, float thresholdDistance, int thresholdNeighboursCount)
{
PointCloud pcResult = new PointCloud();
KDTreeKennell kdTree = new KDTreeKennell();
kdTree.Build(source);
VertexKDTree[] resultArray = new VertexKDTree[source.Count];
try
{
List <Vector3> listV = new List <Vector3>();
List <Vector3> listC = new List <Vector3>();
System.Threading.Tasks.Parallel.For(0, source.Count, i =>
{
VertexKDTree vSource = new VertexKDTree(source.Vectors[i], source.Colors[i], i);
int neighboursCount = 0;
kdTree.FindClosestPoints_Radius(vSource, thresholdDistance, ref neighboursCount);
if (neighboursCount >= thresholdNeighboursCount)
{
resultArray[i] = vSource;
}
});
for (int i = 0; i < source.Count; i++)
{
if (resultArray[i] != null)
{
listV.Add(resultArray[i].Vector);
listC.Add(resultArray[i].Color);
}
}
pcResult.Vectors = listV.ToArray();
pcResult.Colors = listC.ToArray();
pcResult.SetDefaultIndices();
}
catch (Exception err)
{
System.Windows.Forms.MessageBox.Show("Error in KDTreeKennnellRemoveDuplicates: " + err.Message);
}
return(pcResult);
}
public static PointCloud CreateCube24()
{
Vector3[] vArray = new Vector3[] {
new Vector3(1, 1, -1), new Vector3(-1, 1, -1), new Vector3(-1, 1, 1), new Vector3(1, 1, 1),
new Vector3(1, -1, 1), new Vector3(-1, -1, 1), new Vector3(-1, -1, -1), new Vector3(1, -1, -1),
new Vector3(1, 1, 1), new Vector3(-1, 1, 1), new Vector3(-1, -1, 1), new Vector3(1, -1, 1),
new Vector3(1, -1, -1), new Vector3(-1, -1, -1), new Vector3(-1, 1, -1), new Vector3(1, 1, -1),
new Vector3(-1, 1, 1), new Vector3(-1, 1, -1), new Vector3(-1, -1, -1), new Vector3(-1, -1, 1),
new Vector3(1, 1, -1), new Vector3(1, 1, 1), new Vector3(1, -1, 1), new Vector3(1, -1, -1)
};
PointCloud pc = new PointCloud(vArray, null, null, null, null, null);
pc.SetDefaultIndices();
return(pc);
}
/// <summary>
/// tree.RemoveOutliersByDeviation(PointCloudDirty, 10, 1.3f);
/// </summary>
/// <param name="source"></param>
/// <param name="numberOfNeighbours"></param>
/// <param name="stdMultiplier"></param>
/// <returns></returns>
public static PointCloud ByStandardDeviation(PointCloud source, int numberOfNeighbours, float stdDeviationMultiplier, out PointCloud pcOutliersMarkedRed)
{
PointCloud pcResult = new PointCloud();
//the outliers are marked red
pcOutliersMarkedRed = source.Clone();
float meanDistance, standardDeviation;
float[] distances;
//1. get standard deviation, meanDistance and array of distances
StandardDeviation(source, numberOfNeighbours, out meanDistance, out standardDeviation, out distances);
int numberRemoved = 0;
VertexKDTree[] resultArray = new VertexKDTree[source.Count];
VertexKDTree[] outliers = new VertexKDTree[source.Count];
try
{
List <Vector3> listV = new List <Vector3>();
List <Vector3> listC = new List <Vector3>();
//2. distance threshold: deviation plus the overall mean distance
float distanceThreshold = meanDistance + standardDeviation;
//3. remove all points according to the distance threshold
for (int i = 0; i < source.Count; i++)
{
VertexKDTree vSource = new VertexKDTree(source.Vectors[i], source.Colors[i], i);
if (distances[i] > distanceThreshold)
{
pcOutliersMarkedRed.Colors[i] = new Vector3(1, 0, 0);
numberRemoved++;
continue;
}
else
{
resultArray[i] = vSource;
}
}
List <Vector3> listOutliers = new List <Vector3>();
List <Vector3> listOutliersColors = new List <Vector3>();
//build resulting cloud and outliers
for (int i = 0; i < source.Count; i++)
{
if (resultArray[i] != null)
{
listV.Add(resultArray[i].Vector);
listC.Add(resultArray[i].Color);
}
}
pcResult.Vectors = listV.ToArray();
pcResult.Colors = listC.ToArray();
pcResult.SetDefaultIndices();
System.Diagnostics.Debug.WriteLine("Outliers: Mean distance---" + meanDistance.ToString("G") + " ---- standard deviation ---" + standardDeviation.ToString("G") + "---Number of outliers: " + numberRemoved.ToString());
}
catch (Exception err)
{
System.Windows.Forms.MessageBox.Show("Error in KDTreeKennnellRemoveDuplicates: " + err.Message);
}
return(pcResult);
}
