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We are working on a project that uses point cloud curvatures to identify concave regions. On the githut site we found the following exmaples, but after we import the pcu, it seems that the method "mesh_mean_and_gaussian_curvatures" does not exist. Can you advise what is the right way to use pcu to estimate curvature today?
The sample referred is as :
`
import point_cloud_utils as pcu
v is a #v by 3 NumPy array of vertices
f is an #f by 3 NumPy array of face indexes into v
v, f = pcu.load_mesh_vfc("my_model.ply")
Compute principal min/max curvature magnitudes (k1, k2) and directions (d1, d2)
We are working on a project that uses point cloud curvatures to identify concave regions. On the githut site we found the following exmaples, but after we import the pcu, it seems that the method "mesh_mean_and_gaussian_curvatures" does not exist. Can you advise what is the right way to use pcu to estimate curvature today?
The sample referred is as :
`
import point_cloud_utils as pcu
v is a #v by 3 NumPy array of vertices
f is an #f by 3 NumPy array of face indexes into v
v, f = pcu.load_mesh_vfc("my_model.ply")
Compute principal min/max curvature magnitudes (k1, k2) and directions (d1, d2)
using the one ring of each vertex
k1, k2, d1, d2 = pcu.mesh_principal_curvatures(v, f)
Compute principal min/max curvature magnitudes (k1, k2) and directions (d1, d2)
using a radius. This method is much more robust but requires tuning the radius
k1, k2, d1, d2 = pcu.mesh_principal_curvatures(v, f, r=0.1)
Compute Mean (kh) and Gaussian (kg) curvatures using the one ring of each vertex
kh, kg = pcu.mesh_mean_and_gaussian_curvatures(v, f)
Compute Mean (kh) and Gaussian (kg) curvatures using using a radius.
This method is much more robust but requires tuning the radius
kh, kg = pcu.mesh_mean_and_gaussian_curvatures(v, f, r=0.1)
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