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cubec
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PartProbe3D-preprocessed

Name: PartProbe3D-preprocessed
Creator: cubec

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PartProbe3D preprocessed datasets

Saved 64³ training and evaluation inputs for PartProbe3D. These archives preserve RGB, visible object masks, depth and validity, camera calibration, posed meshes, scene occupancy and independent per-object voxel occupancy. Targets describe mesh triangle surfaces, including hidden surfaces and overlapping object channels.

Dataset	Split	Views	Scenes
sage	test	1,952	1,000
sage	train	17,456	8,931
3dfront	test	10,066	935
3dfront	train	39,930	3,703
ycbv	test	900	900

3D-FRONT uses the pinned last 1,000 MIDI rooms for testing and all other rooms for training; all views of a room stay together. There is no validation split. YCB-V is evaluation only. The viewer contains metadata indices; binary training artifacts are in the archives.

Upload status

Dataset	Packaging complete	Upload complete	Packaged GiB	Transferred GiB
sage	False	False	302.08	302.08
3dfront	True	True	273.53	273.53
ycbv	True	True	3.60	3.60

SAGE-10k keeps every room and view of a source layout in the same split, using the saved SHA-256 layout split with a 10% test fraction and no validation split. The completed extent dataset retains 19,408 training/evaluation views; 592 source captures were rejected by target preparation.

Archive inventories and SHA-256 checksums are in bundles/<dataset>/index.json. FoundationPose currently has raw archives only. Diagnostic subsets, raw downloads, model weights and model-specific feature caches are not included.

Restore on another server

Use the PartProbe3D checkout containing preprocess/huggingface.py and install its preprocessing dependencies plus huggingface-hub. Run from the repository root:

python -m preprocess.huggingface download --repo cubec/PartProbe3D-preprocessed --dataset sage --cleanup
python -m preprocess.huggingface download --repo cubec/PartProbe3D-preprocessed --dataset 3dfront --cleanup
python -m preprocess.huggingface download --repo cubec/PartProbe3D-preprocessed --dataset ycbv --cleanup

Downloads resume through Hugging Face. Restoration verifies every archive SHA-256 and refuses to replace different existing data. --revision <commit> pins a release. The extracted roots are datasets/<dataset>/extent/point_cloud. --cleanup downloads and restores one archive at a time, deleting each only after checksum verification and successful extraction. Temporary archives use datasets/<dataset>/archives/huggingface/. Without --cleanup, all downloaded archives are retained for reuse.

The loaders resolve sample, camera, geometry and target paths relative to the dataset root. Historical absolute file and raw asset paths remain provenance; loading saved samples and geometry does not require those paths or raw assets.

Prepare the model-specific features on the destination server, then train:

python -m ss_multiobj_depth.prepare --config ss_multiobj_depth/configs/front3d.json
python -m ss_multiobj_depth.train --config ss_multiobj_depth/configs/front3d.json

The mixed 32³ SLAT training config reads the saved 64³ datasets and pools grids online:

python -m slat_segmentation.train --config slat_segmentation/configs/front3d_sage_32.json

Provenance

Original manifests, artifact completion markers, checksums, identities and split policies are retained byte-for-byte inside the archives. Generation settings and code hashes are also available under metadata/<dataset>/conversion_policy.json. No model checkpoint was used for this upload; no checkpoint weights are included. Source datasets retain their original licenses and access terms.