public float GetDistsQuantile(float quantile)
{
float[] d = Dists.Data;
int[] indices = Enumerable.Range(0, d.Length).ToArray();
int n = (int)(d.Length * quantile);
QuickSelect.Select(indices, 0, d.Length - 1, n, i => d[i]);
return(d[indices[n]]);
}
/// <summary>
/// This method essentially recurses, building a k-d tree of the points between first and last
/// Once the number of points is smaller than knn, it uses that set of points to compute a surface normal
/// From that set of points,
/// </summary>
void buildNew(BuildData data, int first, int last, Vector3 minValues, Vector3 maxValues)
{
int count = last - first;
if (count <= knn)
{
// compute for this range
fuseRange(data, first, last);
// TODO: make another filter that creates constant-density clouds,
// typically by stopping recursion after the median of the bounding cuboid
// is below a threshold, or that the number of points falls under a threshold
return;
}
// find the largest dimension of the box
int cutDim = MaxDim(maxValues - minValues);
// compute number of elements
int rightCount = count / 2;
int leftCount = count - rightCount;
Debug.Assert(last - rightCount == first + leftCount);
// select the cut point and partition the indices around it
var pts = data.points;
QuickSelect.Select(data.indices, first, last - 1, first + leftCount, i => GetAt(data.points[i].point, cutDim));
// get value
int cutIndex = data.indices[first + leftCount];
float cutVal = GetAt(data.points[cutIndex].point, cutDim);
// update bounds for left
Vector3 leftMaxValues = SetAt(maxValues, cutDim, cutVal);
// update bounds for right
Vector3 rightMinValues = SetAt(minValues, cutDim, cutVal);
// recurse
buildNew(data, first, first + leftCount, minValues, leftMaxValues);
buildNew(data, first + leftCount, last, rightMinValues, maxValues);
}
