DeepSDFStruct.sampling#

Functions

move(t_mesh, new_center)

noisy_sample(t_mesh, std, count)

process_single_geometry(args)

random_points(count)

random points in a unit sphere centered at (0, 0, 0)

random_points_cube(count, box_size)

random points in a cube with size box_size centered at (0, 0, 0)

random_sample_sdf(sdf, bounds, n_samples[, ...])

sample_mesh_surface(sdf, mesh, n_samples, stds)

Sample noisy points around a mesh surface and evaluate them with a signed distance function (SDF).

Classes

DataSetInfo

SDFSampler(outdir, splitdir, dataset_name[, ...])

SampledSDF(samples, distances)

SphereParameters

class DeepSDFStruct.sampling.DataSetInfo#

Bases: TypedDict

class_name: str#
dataset_name: str#
class DeepSDFStruct.sampling.SDFSampler(outdir, splitdir, dataset_name, unify_multipatches=True)#

Bases: object

add_class(geom_list, class_name)#
Return type:

None

Parameters:

  • geom_list (list)

  • class_name (str)

get_SDF_list(n_faces=100)#
Return type:

list[SDFBase]

get_sdf_from_geometry(geometry, n_faces, unify_multipatches=True, threshold=1e-05)#
Return type:

SDFBase

Parameters:

  • n_faces (int)

  • unify_multipatches (bool)

  • threshold (float)

process_geometries(sampling_strategy='uniform', n_faces=100, n_samples=100000.0, unify_multipatches=True, compute_mechanical_properties=True, show=False)#
Parameters:

n_samples (int)

sample_sdf(sdf, show=False, n_samples=100000.0, sampling_strategy='uniform', box_size=None, stds=[0.0025, 0.00025])#
Parameters:

n_samples (int)

write_json(json_fname)#
class DeepSDFStruct.sampling.SampledSDF(samples, distances)#

Bases: object

Parameters:

  • samples (torch._VariableFunctionsClass.tensor)

  • distances (torch._VariableFunctionsClass.tensor)

create_gus_plottable()#
distances: tensor#
samples: tensor#
split_pos_neg()#
property stacked#
class DeepSDFStruct.sampling.SphereParameters#

Bases: TypedDict

cx: float#
cy: float#
cz: float#
r: float#
DeepSDFStruct.sampling.move(t_mesh, new_center)#
DeepSDFStruct.sampling.noisy_sample(t_mesh, std, count)#
DeepSDFStruct.sampling.process_single_geometry(args)#
DeepSDFStruct.sampling.random_points(count)#

random points in a unit sphere centered at (0, 0, 0)

DeepSDFStruct.sampling.random_points_cube(count, box_size)#

random points in a cube with size box_size centered at (0, 0, 0)

DeepSDFStruct.sampling.random_sample_sdf(sdf, bounds, n_samples, type='uniform', device='cpu', dtype=torch.float32)#
DeepSDFStruct.sampling.sample_mesh_surface(sdf, mesh, n_samples, stds, device='cpu', dtype=torch.float32)#

Sample noisy points around a mesh surface and evaluate them with a signed distance function (SDF).

This function uses trimesh.sample to generate surface samples and perturbs them with Gaussian noise of varying standard deviations, and queries the SDF at those points.

Parameters:

  • sdf (SDFBase) – A callable SDF object that takes 3D points and returns signed distances.

  • mesh (gus.Faces) – A mesh object containing the vertices.

  • n_samples (int) – Number of mesh vertices to sample

  • stds (list[float]) – Standard deviations for Gaussian noise added to sampled vertices. - Typical values: [0.05, 0.0015]. - Larger values spread samples farther from the surface; smaller values keep them closer.

  • device (str, optional) – Torch device to place tensors on (e.g., “cpu” or “cuda”).

  • dtype (torch.dtype, optional) – Data type for generated tensors (default: torch.float32).

Returns:

An object containing:

  • samples (torch.Tensor): The perturbed sample points of shape (n_samples * len(stds), 3).

  • distances (torch.Tensor): The corresponding SDF values at those sample points.

Return type:

SampledSDF