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Scalar field interpolation/filter methods
Posted: Mon Jun 09, 2025 3:17 am
by gysvanzyl
Hi all,
When interpolating a scalar field from one cloud onto another, is there a way to use the minimum of the n nearest points instead of mean? Or maybe mean minus 2 or 3 standard deviations?
Alternatively, is there a way to apply a filter (similar to Gaussian or bilateral) to a scalar field that will assign a scalar field value to each point that is the minimum of the scalar field values at the n nearest points.
It doesn't look like either of these are possible in CloudCompare directly, but might there be a way to specify a custom function as an interpolator method in the CloudComPy interpolatorParameters class?
Cheers,
Gys
Re: Scalar field interpolation/filter methods
Posted: Tue Jun 10, 2025 8:45 pm
by daniel
Indeed, this is not readily available in CC (apart if you want to use the Rasterize tool, but it only work in 2.5D).
Either you would have to adapt the C++ code, or maybe some Python script with CloudCompy of the Python plugin. But you'll have to ask directly on these projects github page (as they are separate projects, and their maintainers rarely come on this forum).
Re: Scalar field interpolation/filter methods
Posted: Wed Jun 18, 2025 6:58 am
by gysvanzyl
For the record, I did manage to make a working python plugin script to do this. Code could probably be better, but at least this works:
Code: Select all
import pycc
import numpy as np
from scipy.spatial import KDTree
def min_smoothing(radius=0.005):
'''
Creates a new scalar field from the currently displayed one by
setting the value of each point as the minimum of all the values
within a specified radius.
'''
cc = pycc.GetInstance()
entities = cc.getSelectedEntities()
if not entities:
raise RuntimeError("No entities selected")
cloud = entities[0].getAssociatedCloud()
# Get point coordinates
points = np.array([list(cloud.getPoint(i)) for i in range(cloud.size())], dtype=np.float32)
# Get the currently displayed scalar field
SF = cloud.getCurrentDisplayedScalarField()
scalar_values = np.array([SF.getValue(i) for i in range(SF.size())], dtype=np.float32)
# Use a KD-tree to find all neighbors within the specified radius
tree = KDTree(points)
neighbors_list = tree.query_ball_point(points, r=radius) # list of arrays
# Compute the minimum scalar field value among neighbors within radius
min_scalar_values = np.empty(cloud.size(), dtype=np.float32)
for i, neighbors in enumerate(neighbors_list):
if neighbors:
min_scalar_values[i] = np.min(scalar_values[neighbors])
else:
min_scalar_values[i] = np.nan # or assign a default/fallback value
new_sf_name = f"{SF.getName()}_minR_{radius}"
new_sf_index = cloud.addScalarField(new_sf_name)
new_sf = cloud.getScalarField(new_sf_index)
for i, val in enumerate(min_scalar_values):
new_sf.setValue(i, val)
new_sf.computeMinAndMax()
if __name__ == "__main__":
min_smoothing(radius=0.005)
Re: Scalar field interpolation/filter methods
Posted: Sat Jun 21, 2025 8:34 am
by daniel
Thanks for sharing!