def sample_points_from_meshes(
meshes,
num_samples: int = 10000,
return_normals: bool = False,
return_textures: bool = False,
) -> Union[torch.Tensor, Tuple[torch.Tensor, torch.Tensor], Tuple[
torch.Tensor, torch.Tensor, torch.Tensor], ]:
"""
Convert a batch of meshes to a batch of pointclouds by uniformly sampling
points on the surface of the mesh with probability proportional to the
face area.
Args:
meshes: A Meshes object with a batch of N meshes.
num_samples: Integer giving the number of point samples per mesh.
return_normals: If True, return normals for the sampled points.
return_textures: If True, return textures for the sampled points.
Returns:
3-element tuple containing
- **samples**: FloatTensor of shape (N, num_samples, 3) giving the
coordinates of sampled points for each mesh in the batch. For empty
meshes the corresponding row in the samples array will be filled with 0.
- **normals**: FloatTensor of shape (N, num_samples, 3) giving a normal vector
to each sampled point. Only returned if return_normals is True.
For empty meshes the corresponding row in the normals array will
be filled with 0.
- **textures**: FloatTensor of shape (N, num_samples, C) giving a C-dimensional
texture vector to each sampled point. Only returned if return_textures is True.
For empty meshes the corresponding row in the textures array will
be filled with 0.
Note that in a future releases, we will replace the 3-element tuple output
with a `Pointclouds` datastructure, as follows
.. code-block:: python
Pointclouds(samples, normals=normals, features=textures)
"""
if meshes.isempty():
raise ValueError("Meshes are empty.")
verts = meshes.verts_packed()
if not torch.isfinite(verts).all():
raise ValueError("Meshes contain nan or inf.")
if return_textures and meshes.textures is None:
raise ValueError("Meshes do not contain textures.")
faces = meshes.faces_packed()
mesh_to_face = meshes.mesh_to_faces_packed_first_idx()
num_meshes = len(meshes)
num_valid_meshes = torch.sum(meshes.valid) # Non empty meshes.
# Initialize samples tensor with fill value 0 for empty meshes.
samples = torch.zeros((num_meshes, num_samples, 3), device=meshes.device)
# Only compute samples for non empty meshes
with torch.no_grad():
areas, _ = mesh_face_areas_normals(verts,
faces) # Face areas can be zero.
max_faces = meshes.num_faces_per_mesh().max().item()
areas_padded = packed_to_padded(areas, mesh_to_face[meshes.valid],
max_faces) # (N, F)
# TODO (gkioxari) Confirm multinomial bug is not present with real data.
sample_face_idxs = areas_padded.multinomial(
num_samples, replacement=True) # (N, num_samples)
sample_face_idxs += mesh_to_face[meshes.valid].view(
num_valid_meshes, 1)
# Get the vertex coordinates of the sampled faces.
face_verts = verts[faces]
v0, v1, v2 = face_verts[:, 0], face_verts[:, 1], face_verts[:, 2]
# Randomly generate barycentric coords.
w0, w1, w2 = _rand_barycentric_coords(num_valid_meshes, num_samples,
verts.dtype, verts.device)
# Use the barycentric coords to get a point on each sampled face.
a = v0[sample_face_idxs] # (N, num_samples, 3)
b = v1[sample_face_idxs]
c = v2[sample_face_idxs]
samples[
meshes.
valid] = w0[:, :, None] * a + w1[:, :, None] * b + w2[:, :, None] * c
if return_normals:
# Initialize normals tensor with fill value 0 for empty meshes.
# Normals for the sampled points are face normals computed from
# the vertices of the face in which the sampled point lies.
normals = torch.zeros((num_meshes, num_samples, 3),
device=meshes.device)
vert_normals = (v1 - v0).cross(v2 - v1, dim=1)
vert_normals = vert_normals / vert_normals.norm(
dim=1, p=2, keepdim=True).clamp(min=sys.float_info.epsilon)
vert_normals = vert_normals[sample_face_idxs]
normals[meshes.valid] = vert_normals
if return_textures:
# fragment data are of shape NxHxWxK. Here H=S, W=1 & K=1.
pix_to_face = sample_face_idxs.view(len(meshes), num_samples, 1,
1) # NxSx1x1
bary = torch.stack((w0, w1, w2),
dim=2).unsqueeze(2).unsqueeze(2) # NxSx1x1x3
# zbuf and dists are not used in `sample_textures` so we initialize them with dummy
dummy = torch.zeros((len(meshes), num_samples, 1, 1),
device=meshes.device,
dtype=torch.float32) # NxSx1x1
fragments = MeshFragments(pix_to_face=pix_to_face,
zbuf=dummy,
bary_coords=bary,
dists=dummy)
textures = meshes.sample_textures(fragments) # NxSx1x1xC
textures = textures[:, :, 0, 0, :] # NxSxC
# return
# TODO(gkioxari) consider returning a Pointclouds instance [breaking]
if return_normals and return_textures:
return samples, normals, textures
if return_normals: # return_textures is False
return samples, normals
if return_textures: # return_normals is False
return samples, textures
return samples
def sample_points_from_meshes(
meshes,
num_samples: int = 10000,
return_normals: bool = False
) -> Union[torch.Tensor, Tuple[torch.Tensor, torch.Tensor]]:
"""
Convert a batch of meshes to a pointcloud by uniformly sampling points on
the surface of the mesh with probability proportional to the face area.
Args:
meshes: A Meshes object with a batch of N meshes.
num_samples: Integer giving the number of point samples per mesh.
return_normals: If True, return normals for the sampled points.
eps: (float) used to clamp the norm of the normals to avoid dividing by 0.
Returns:
2-element tuple containing
- **samples**: FloatTensor of shape (N, num_samples, 3) giving the
coordinates of sampled points for each mesh in the batch. For empty
meshes the corresponding row in the samples array will be filled with 0.
- **normals**: FloatTensor of shape (N, num_samples, 3) giving a normal vector
to each sampled point. Only returned if return_normals is True.
For empty meshes the corresponding row in the normals array will
be filled with 0.
"""
if meshes.isempty():
raise ValueError("Meshes are empty.")
verts = meshes.verts_packed()
if not torch.isfinite(verts).all():
raise ValueError("Meshes contain nan or inf.")
faces = meshes.faces_packed()
mesh_to_face = meshes.mesh_to_faces_packed_first_idx()
num_meshes = len(meshes)
num_valid_meshes = torch.sum(meshes.valid) # Non empty meshes.
# Intialize samples tensor with fill value 0 for empty meshes.
samples = torch.zeros((num_meshes, num_samples, 3), device=meshes.device)
# Only compute samples for non empty meshes
with torch.no_grad():
areas, _ = mesh_face_areas_normals(verts,
faces) # Face areas can be zero.
max_faces = meshes.num_faces_per_mesh().max().item()
areas_padded = packed_to_padded(areas, mesh_to_face[meshes.valid],
max_faces) # (N, F)
# TODO (gkioxari) Confirm multinomial bug is not present with real data.
sample_face_idxs = areas_padded.multinomial(
num_samples, replacement=True) # (N, num_samples)
sample_face_idxs += mesh_to_face[meshes.valid].view(
num_valid_meshes, 1)
# Get the vertex coordinates of the sampled faces.
face_verts = verts[faces.long()]
v0, v1, v2 = face_verts[:, 0], face_verts[:, 1], face_verts[:, 2]
# Randomly generate barycentric coords.
w0, w1, w2 = _rand_barycentric_coords(num_valid_meshes, num_samples,
verts.dtype, verts.device)
# Use the barycentric coords to get a point on each sampled face.
a = v0[sample_face_idxs] # (N, num_samples, 3)
b = v1[sample_face_idxs]
c = v2[sample_face_idxs]
samples[
meshes.
valid] = w0[:, :, None] * a + w1[:, :, None] * b + w2[:, :, None] * c
if return_normals:
# Intialize normals tensor with fill value 0 for empty meshes.
# Normals for the sampled points are face normals computed from
# the vertices of the face in which the sampled point lies.
normals = torch.zeros((num_meshes, num_samples, 3),
device=meshes.device)
vert_normals = (v1 - v0).cross(v2 - v1, dim=1)
vert_normals = vert_normals / vert_normals.norm(
dim=1, p=2, keepdim=True).clamp(min=sys.float_info.epsilon)
vert_normals = vert_normals[sample_face_idxs]
normals[meshes.valid] = vert_normals
return samples, normals
else:
return samples
