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PyVista Volume Processing

pyvista_volume_processing: Volumetric and grid processing via PyVista: isosurface extraction (contouring), slicing, thresholding, geometry extraction, and cell filtering.
Methods pv_contour, pv_slice, pv_slice_orthogonal, pv_threshold_volume, pv_extract_geometry, pv_extract_cells
Input files .vti, .vtk, .vtu, .ply, .obj, .stl
Bundled datasets to try open3d_bunny, open3d_icp_demo, kitti_raw_2011_09_26_drive_0001, medshapenet, c3vd_endoscopy_splats, hf_objaverse_glb_shard
CLI pardesline run pyvista_volume_processing.<method> input.ext [--param value]

How it works

PyVista volume processing wraps VTK filters for extracting information from volumetric and grid datasets:

Contour/Isosurface extracts surfaces at scalar isovalues using marching cubes. Slice creates a cross-section along any plane. Threshold selects cells within a scalar range. Extract Geometry converts a volume to its surface representation. Extract Cells selects cells by type.

Methods

pv_contour

Marching cubes extracts a surface mesh at scalar isovalue \(c\): \(\{\mathbf{x} : f(\mathbf{x}) = c\}\).

import pardesline as pl

obj = pl.load("input.vti")
result = obj.run("pyvista_volume_processing.pv_contour")  # catalog defaults
result.provenance[-1]  # the step, every parameter used, duration, version

Output: .ply. Script: python -m pardesline.ops.pyvista_volume_processing.contour --input ... --output ....

Defaults validated on: MSD and RIRE volumes through Volume.to_mesh (examples/hippocampus_mask_to_mesh, examples/spleen_ct_threshold_segmentation); generated grids in tests/test_ops_smoke.py

Parameter Type Default Range What it does Why this default, when to change it
n_contours int 5 1 to 50 Number of isosurface levels. 5 evenly spaced iso-levels show the structure of an unknown field; a label map or a known threshold uses one level through Volume.to_mesh.
scalars string "" Scalar array name (empty = active scalars). Empty uses the active scalars (the image intensity).
compute_normals (advanced) bool false Compute point normals on the output surface. Off: normals are recomputed later by the mesh steps that need them.
compute_gradients (advanced) bool false Compute gradients at the isosurface. Off: not needed for a surface.
compute_scalars (advanced) bool true Preserve the contoured scalar on the output. On: the contoured value is kept on the surface for colouring.
method (advanced) enum contour contour, marching_cubes, flying_edges Contouring algorithm. contour is VTK's generic filter; marching_cubes and flying_edges are faster on image data and give the same surface.
preference (advanced) enum point point, cell Prefer point or cell scalars. point: image intensities are point data.

pv_slice

Intersects the dataset with a plane \(\mathbf{n} \cdot (\mathbf{x} - \mathbf{o}) = 0\) to produce a cross-section.

import pardesline as pl

obj = pl.load("input.vti")
result = obj.run("pyvista_volume_processing.pv_slice")  # catalog defaults
result.provenance[-1]  # the step, every parameter used, duration, version

Output: .ply. Script: python -m pardesline.ops.pyvista_volume_processing.slice --input ... --output ....

Defaults validated on: RIRE and MSD volumes (README demo slices); generated grids in tests/test_ops_smoke.py

Parameter Type Default Range What it does Why this default, when to change it
normal_x float 1.0 -1.0 to 1.0 Slice normal X. 1: an x-normal (sagittal in LPS) slice by default.
normal_y float 0.0 -1.0 to 1.0 Slice normal Y. 0, see normal_x.
normal_z float 0.0 -1.0 to 1.0 Slice normal Z. 0, see normal_x.
origin_x float 0.0 -10000000000.0 to 10000000000.0 Slice origin X (0 = center). 0 means the volume centre.
origin_y float 0.0 -10000000000.0 to 10000000000.0 Slice origin Y (0 = center). 0 means the centre.
origin_z float 0.0 -10000000000.0 to 10000000000.0 Slice origin Z (0 = center). 0 means the centre.
generate_triangles (advanced) bool false Triangulate the slice output. Off: the slice stays a polygon mesh of quads.
contour (advanced) bool false Generate contour edges of the slice. Off: no contour edges.

pv_slice_orthogonal

Creates three orthogonal slices through the dataset at specified X, Y, Z positions.

import pardesline as pl

obj = pl.load("input.vti")
result = obj.run("pyvista_volume_processing.pv_slice_orthogonal")  # catalog defaults
result.provenance[-1]  # the step, every parameter used, duration, version

Output: .ply. Script: python -m pardesline.ops.pyvista_volume_processing.slice_orthogonal --input ... --output ....

Defaults validated on: MSD hippocampus MRI (examples/hippocampus_mask_to_mesh); generated grids in tests/test_ops_smoke.py

Parameter Type Default Range What it does Why this default, when to change it
x float 0.0 -10000000000.0 to 10000000000.0 X position for YZ slice (0 = center). 0 means the centre of the volume.
y float 0.0 -10000000000.0 to 10000000000.0 Y position for XZ slice (0 = center). 0 means the centre.
z float 0.0 -10000000000.0 to 10000000000.0 Z position for XY slice (0 = center). 0 means the centre.
generate_triangles (advanced) bool false Triangulate the slice output. Off: the slice stays a polygon mesh of quads, which renders and saves smaller.
contour (advanced) bool false Generate contour edges of the slices. Off: no contour edges are generated on the slice.

pv_threshold_volume

Selects cells with scalar values in \([l, u]\), preserving the volumetric structure.

import pardesline as pl

obj = pl.load("input.vti")
result = obj.run("pyvista_volume_processing.pv_threshold_volume")  # catalog defaults
result.provenance[-1]  # the step, every parameter used, duration, version

Output: .vtu. Script: python -m pardesline.ops.pyvista_volume_processing.threshold_volume --input ... --output ....

Defaults validated on: generated grids in tests/test_ops_smoke.py

Parameter Type Default Range What it does Why this default, when to change it
lower float 0.0 -10000000000.0 to 10000000000.0 Lower scalar bound. 0 with upper 1 is a normalised placeholder; use Hounsfield units on CT.
upper float 1.0 -10000000000.0 to 10000000000.0 Upper scalar bound. 1, see lower.
scalars (advanced) string "" Scalar array name (empty = active scalars). Empty uses the active scalars.
invert (advanced) bool false Invert the threshold selection. Off: keep the inside of the range.
continuous (advanced) bool false Use continuous cell range for thresholding. Off: a cell is kept on its own scalar value, not on the range it spans.
preference (advanced) enum cell cell, point Prefer cell or point scalars. cell: VTK's default.
all_scalars (advanced) bool false Require all scalar components to satisfy the range. Off: a cell is kept if any of its points pass, which keeps boundaries intact.

pv_extract_geometry

Extracts the boundary surface of a volume as a polygonal mesh.

import pardesline as pl

obj = pl.load("input.vti")
result = obj.run("pyvista_volume_processing.pv_extract_geometry")  # catalog defaults
result.provenance[-1]  # the step, every parameter used, duration, version

Output: .ply. Script: python -m pardesline.ops.pyvista_volume_processing.extract_geometry --input ... --output ....

Defaults validated on: generated grids in tests/test_ops_smoke.py

Parameter Type Default Range What it does Why this default, when to change it
extent (advanced) string "" Optional crop extent 'xmin,xmax,ymin,ymax,zmin,zmax' (empty = full dataset). Empty keeps the whole dataset; a six-number extent crops in index space.

pv_extract_cells

Selects cells matching a specified cell type (tetrahedral, hexahedral, etc.).

import pardesline as pl

obj = pl.load("input.vti")
result = obj.run("pyvista_volume_processing.pv_extract_cells")  # catalog defaults
result.provenance[-1]  # the step, every parameter used, duration, version

Output: .vtu. Script: python -m pardesline.ops.pyvista_volume_processing.extract_cells --input ... --output ....

Defaults validated on: generated grids in tests/test_ops_smoke.py

Parameter Type Default Range What it does Why this default, when to change it
cell_type string tetrahedral Cell type to extract: tetrahedral, hexahedral, wedge, pyramid, triangle, quad. tetrahedral is the cell type of simulation meshes (VTU), the usual reason to extract by type.
invert (advanced) bool false Invert selection (extract all cells NOT of this type). Off: keep the named type.

Generated from src/pardesline/catalog/data/pyvista_volume_processing.json and _defaults.json on 2026-10-10.