DeepSDFStruct.SDF#

Functions

get_equidistant_grid_sample(bounds, grid_spacing)

Generates an equidistant 3D grid of points within the given bounding box.

point_segment_distance(P1, P2, query_points)

Calculates the minimum distance from one or more query points to one or more line segments defined by endpoints P1 and P2.

union(D[, k])

D: np.array of shape (num_points, num_geometries) k: smoothness parameter

Classes

BoxSDF([box_size, center])

CapBorderDict

A dictionary type describing boundary conditions ("caps") for each axis direction (x, y, z).

CapType

NegatedCallable(obj)

SDFBase([deformation_spline, ...])

Abstract base class for Signed Distance Functions with optional deformation and parametrization.

SDFfromDeepSDF(model[, max_batch])

SDFfromLineMesh(line_mesh, thickness[, ...])

SDFfromMesh(mesh[, dtype, flip_sign, scale, ...])

SummedSDF(obj1, obj2)

TransformedSDF(sdf[, rotation, translation, ...])

Generic SDF wrapper that applies a transformation to the input queries.
class DeepSDFStruct.SDF.BoxSDF(box_size=1, center=tensor([0, 0, 0]))#

Bases: DeepSDFStruct.SDF.SDFBase

Parameters:

  • box_size (float)

  • center (torch._VariableFunctionsClass.tensor)

class DeepSDFStruct.SDF.CapBorderDict#

Bases: TypedDict

A dictionary type describing boundary conditions (“caps”) for each axis direction (x, y, z).

Each key (x0, x1, y0, y1, z0, z1) corresponds to one boundary face of a 3D domain, and maps to a dictionary with two fields:

  • cap (int): Type of cap applied (e.g., -1 = none, 1 = active).

  • measure (float): Numerical measure associated with the cap (e.g., thickness, scaling factor, tolerance).

Example

>>> caps: CapBorderDict = {
...     "x0": {"cap": 1, "measure": 0.02},
...     "x1": {"cap": 1, "measure": 0.02},
...     "y0": {"cap": 1, "measure": 0.02},
...     "y1": {"cap": 1, "measure": 0.02},
...     "z0": {"cap": 1, "measure": 0.02},
...     "z1": {"cap": 1, "measure": 0.02},
... }
x0: CapType = {'cap': -1, 'measure': 0}#
x1: CapType = {'cap': -1, 'measure': 0}#
y0: CapType = {'cap': -1, 'measure': 0}#
y1: CapType = {'cap': -1, 'measure': 0}#
z0: CapType = {'cap': -1, 'measure': 0}#
z1: CapType = {'cap': -1, 'measure': 0}#
class DeepSDFStruct.SDF.CapType#

Bases: TypedDict

cap: int#
measure: float#
class DeepSDFStruct.SDF.NegatedCallable(obj)#

Bases: DeepSDFStruct.SDF.SDFBase

class DeepSDFStruct.SDF.SDFBase(deformation_spline=None, parametrization=None, cap_border_dict=None, cap_outside_of_unitcube=False)#

Bases: abc.ABC

Abstract base class for Signed Distance Functions with optional deformation and parametrization.

Parameters:
property cap_border_dict#
property deformation_spline#
property parametrization#
plot_slice(*args, **kwargs)#
class DeepSDFStruct.SDF.SDFfromDeepSDF(model, max_batch=32768)#

Bases: DeepSDFStruct.SDF.SDFBase

Parameters:

model (DeepSDFStruct.deep_sdf.models.DeepSDFModel)

set_latent_vec(latent_vec)#

Set conditioning parameters for the model (e.g., latent code).

Parameters:

latent_vec (torch.Tensor)

class DeepSDFStruct.SDF.SDFfromLineMesh(line_mesh, thickness, smoothness=0)#

Bases: DeepSDFStruct.SDF.SDFBase

Parameters:

line_mesh (gustaf.edges.Edges)

line_mesh: Edges#
class DeepSDFStruct.SDF.SDFfromMesh(mesh, dtype=<class 'numpy.float32'>, flip_sign=False, scale=True, threshold=1e-05)#

Bases: DeepSDFStruct.SDF.SDFBase

class DeepSDFStruct.SDF.SummedSDF(obj1, obj2)#

Bases: DeepSDFStruct.SDF.SDFBase

Parameters:
class DeepSDFStruct.SDF.TransformedSDF(sdf, rotation=None, translation=None, scale=None)#

Bases: DeepSDFStruct.SDF.SDFBase

Generic SDF wrapper that applies a transformation to the input queries. Transformation can be rotation, translation, or scaling.

Parameters:

sdf (DeepSDFStruct.SDF.SDFBase)

DeepSDFStruct.SDF.get_equidistant_grid_sample(bounds, grid_spacing, dtype=torch.float32, device='cpu')#

Generates an equidistant 3D grid of points within the given bounding box.

Parameters:

  • bounds (torch.Tensor) – Tensor of shape (2,3), [[xmin, ymin, zmin], [xmax, ymax, zmax]]

  • grid_spacing (float) – Approximate spacing between points along each axis.

Returns:

points – Tensor of shape (N,3) containing all grid points.

Return type:

torch.Tensor

DeepSDFStruct.SDF.point_segment_distance(P1, P2, query_points)#

Calculates the minimum distance from one or more query points to one or more line segments defined by endpoints P1 and P2.

Parameters:

  • P1 (np.ndarray) – Array of shape (M, 2) or (2,) representing first endpoints of segments.

  • P2 (np.ndarray) – Array of shape (M, 2) or (2,) representing second endpoints of segments.

  • query_points (np.ndarray) – Array of shape (N, 2) or (2,) representing query point(s).

Returns:

Array of shape (N,) with the minimum distance from each query point

to the closest segment.

Return type:

np.ndarray

DeepSDFStruct.SDF.union(D, k=0)#

D: np.array of shape (num_points, num_geometries) k: smoothness parameter