Stanford armadillo (Open3D demo mesh)

pl.load("open3d_armadillo") loads this dataset. The Stanford armadillo as shipped by open3d.data.ArmadilloMesh.

import pardesline as pl

ds = pl.load("open3d_armadillo")
print(ds.describe())  # roles, frames, units, labels, licence, citation
s = ds[0]
obj = s.mesh  # fetched now, cached under ~/.cache/pardesline/datasets/open3d_armadillo

Provenance and licence

Source	open3d.data demo download
Scheme	open3d
Size of the whole source	7 MB
Licence	not a standard licence: see the note
Redistribution	unknown
Licence note	Stanford 3D Scanning Repository: free to use with acknowledgement; commercial use TO CONFIRM with Stanford.
Terms	https://graphics.stanford.edu/data/3Dscanrep/
Cite	Krishnamurthy V., Levoy M. Fitting smooth surfaces to dense polygon meshes. SIGGRAPH 1996.
Samples grouped by	file
Samples listed in the manifest	1

Roles (what each sample contains)

Role	Type	Frame	Units	Labels / layout	Pattern or file
mesh	mesh	from the file header	from the file header		ArmadilloMesh.ply Triangle mesh (units not stated by the file)

Standardized layout

Files are fetched on first access into the dataset cache and stay in their original format; ds.export(folder) writes every sample as standard files (PLY for points and meshes, MetaImage for volumes and label maps) with a .pardesline.json sidecar carrying frame, units, labels, licence and provenance, plus a manifest for the folder. Label maps become exact surfaces with to_mesh(label=k), DICOM series become one volume, raw arrays are decoded through the layout above.

ds = pl.load("open3d_armadillo").take(2)
ds.export("./standardized")        # PLY / MetaImage + sidecars + manifest.json

Algorithms and models that run on it

• mesh roles: mesh.to_points(num_points), mesh.decimate(), mesh.smooth(), mesh.clean(), mesh.to_open3d() ; catalog modules: point_cloud_processing, mesh_processing, voxel_to_mesh, sdf, point_cloud_reconstruction, registration, deformable_registration, point_cloud_segmentation, stereo_3d_reconstruction, lidar_scene_detection, lidar_camera_fusion, features_extraction, collision_detection, pyvista_mesh_processing, pyvista_pointcloud_processing, pyvista_volume_processing, pyvista_analysis

Related

• Datasets catalog · How to download a medical imaging dataset in Python · Add a dataset
• Tags: mesh, demo

_{Generated from src/pardesline/data/manifests/open3d_armadillo.json on 2026-10-10.}