I have a mesh which is already normalized to the unit sphere(mesh normalized by dividing the vertices with unit cube opposite corners diagonal distance, which is basically diameter(constant 50cm) of a circle). Now, I am trying to use
sample_sdf_near_surface(mesh). To get points and sdfs correctly, I have to comment out(disable) code _mesh = scale_to_unitsphere(mesh) because the input mesh is already normalized to unit sphere.
Is my understanding correct? or is there any other things that I have to take care after commenting out mesh scaling line of code??
similarly, I run mesh_to_voxels(mesh, 64) to get voxels with sdf values by commenting out _mesh = scale_to_unitsphere(mesh) line of code.
Could you please confirm if I am using this code right?
while sampling points for creating voxels in function _get_rasterpoints(), grid points are sampled between -1 and 1 range. This means that we are sampling points that lie outside the unit sphere, right?
_boundingradius is this the radius of circle that encloses mesh(what is the purpose of this)?
I am asking this question becuase, I used water tight mesh to generate voxels and then reconstruct mesh using mcubes(like in example script). But I observed unnecessary reconstructions.
Dear Authors,
I have few questions :
I have a mesh which is already normalized to the unit sphere(mesh normalized by dividing the vertices with unit cube opposite corners diagonal distance, which is basically diameter(constant 50cm) of a circle). Now, I am trying to use
while sampling points for creating voxels in function _get_rasterpoints(), grid points are sampled between -1 and 1 range. This means that we are sampling points that lie outside the unit sphere, right?
_boundingradius is this the radius of circle that encloses mesh(what is the purpose of this)? I am asking this question becuase, I used water tight mesh to generate voxels and then reconstruct mesh using mcubes(like in example script). But I observed unnecessary reconstructions.
Looking forward to hearing your opinion on this.
Thank you for nice library.