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import mathutils
# create a kd-tree from a mesh
from bpy import context
obj = context.object
mesh = obj.data
size = len(mesh.vertices)
kd = mathutils.kdtree.KDTree(size)
for i, v in enumerate(mesh.vertices):
kd.insert(v.co, i)
kd.balance()
# Find the closest point to the center
co_find = (0.0, 0.0, 0.0)
co, index, dist = kd.find(co_find)
print("Close to center:", co, index, dist)
# 3d cursor relative to the object data
co_find = obj.matrix_world.inverted() @ context.scene.cursor.location
# Find the closest 10 points to the 3d cursor
print("Close 10 points")
for (co, index, dist) in kd.find_n(co_find, 10):
print(" ", co, index, dist)
# Find points within a radius of the 3d cursor
print("Close points within 0.5 distance")
for (co, index, dist) in kd.find_range(co_find, 0.5):
print(" ", co, index, dist)
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