Datasets:

cubec
/

PartProbe3D-preprocessed

language:
  - en
tags:
  - 3d
  - depth
  - segmentation
  - voxel-occupancy
pretty_name: PartProbe3D preprocessed datasets
configs:
  - config_name: sage
    data_files:
      - split: test
        path: catalog/sage/test.jsonl
      - split: train
        path: catalog/sage/train.jsonl
  - config_name: 3dfront
    data_files:
      - split: test
        path: catalog/3dfront/test.jsonl
      - split: train
        path: catalog/3dfront/train.jsonl
  - config_name: ycbv
    data_files:
      - split: test
        path: catalog/ycbv/test.jsonl

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	True	True	475.80	475.80
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. The extent archives exclude diagnostic subsets, raw downloads, model weights, and model-specific feature caches. FoundationPose currently has raw archives only.

SLAT shape: upload and download specification

The repository contains prepared extent datasets and separate SLAT-shape targets. Combined 32³ SLAT finetuning requires both artifact types for both training datasets. The 6.84 GB SLAT upload contains latent targets and metadata. It excludes RGB observations, depth maps, masks, and frozen image features.

The sizes and coverage below describe data snapshot 0e4623fb0c8d9bad7cbe1dfb161a993ac165b6aa. GB means decimal gigabytes.

Bundle	Contents	Training archives and metadata, GB	All archives, GB
`bundles/3dfront`	Calibrated observations, extent geometry, and occupancy targets	234.67	293.70
`bundles/sage`	Calibrated observations, extent geometry, and occupancy targets	460.59	510.88
`precomputed_slat/3dfront`	32³ object latents and available scene latents	5.57	5.57
`precomputed_slat/sage`	32³ object latents and recorded failures	1.27	1.27
Total	Both datasets and both artifact types	702.10	811.42

Training requires each extent bundle's metadata.tar and train-*.tar archives. Evaluation also requires its test-*.tar archives. The current download command restores every archive in a bundle, including available test archives. It has no split filter. The four commands below therefore download about 811.42 GB.

Download and restore

Use the updated PartProbe3D checkout with latent generation in preprocess/slat/. Install its preprocessing dependencies and huggingface-hub. Authenticate with a Hugging Face account that can access this dataset repository. Run these commands from the repository root.

Restore the prepared 3D-FRONT extent dataset.

python -m preprocess.huggingface download \
  --repo cubec/PartProbe3D-preprocessed --bundle-root bundles \
  --dataset 3dfront --revision 0e4623fb0c8d9bad7cbe1dfb161a993ac165b6aa --cleanup

Restore the prepared SAGE-10k extent dataset.

python -m preprocess.huggingface download \
  --repo cubec/PartProbe3D-preprocessed --bundle-root bundles \
  --dataset sage --revision 0e4623fb0c8d9bad7cbe1dfb161a993ac165b6aa --cleanup

Restore the 3D-FRONT shape targets.

python -m preprocess.huggingface download \
  --repo cubec/PartProbe3D-preprocessed --bundle-root precomputed_slat \
  --dataset 3dfront --revision 0e4623fb0c8d9bad7cbe1dfb161a993ac165b6aa --cleanup

Restore the SAGE-10k shape targets.

python -m preprocess.huggingface download \
  --repo cubec/PartProbe3D-preprocessed --bundle-root precomputed_slat \
  --dataset sage --revision 0e4623fb0c8d9bad7cbe1dfb161a993ac165b6aa --cleanup

Dataset	Prepared observations and extent targets	SLAT targets
3D-FRONT	`datasets/3dfront/extent/point_cloud/`	`datasets/3dfront/slat/32/targets/`
SAGE-10k	`datasets/sage/extent/point_cloud/`	`datasets/sage/slat/32/targets/`

The updated downloader maps historical SLAT archive paths under outputs/ to these dataset locations. It preserves target bytes and verifies every archive's size and SHA-256 checksum. It refuses to replace different existing files. --cleanup retains one temporary archive at a time and removes it after successful restoration. Temporary archives reside under datasets/<dataset>/archives/huggingface/. Extracted files and future feature caches need additional disk space.

Preserve the original extent manifests without edits. The SLAT manifests bind their source datasets through manifest checksums. Raw dataset archives, rendering again, and SS feature caches are unnecessary for this workflow.

Complete preparation before training

The published targets cover 39,930 3D-FRONT training views and 17,456 SAGE-10k training views. Three SAGE-10k views retain one failed object target each. These failures block strict combined training until object encoding succeeds. The snapshot contains scene latents for 6,720 3D-FRONT views and no SAGE-10k scenes. The current object loss does not require completed scene encoding.

Obtain the configured Pixal3D, DINOv3, and NAF weights separately.
Resolve the three failed SAGE-10k object targets.
Prepare frozen conditioning features and immutable object targets.
Start finetuning after feature preparation succeeds.

The TRELLIS.2 shape encoder and paired decoder are also required when regenerating shape targets. Image features and immutable training snapshots are not included in the uploaded target bundles. The commands below do not automatically schedule scene encoding or training during feature preparation.

python -m preprocess.slat.prepare \
  --config preprocess/slat/configs/front3d_sage_32.json \
  --stage features --devices cuda:1 cuda:2
python -m slat_multiobj_shape.train \
  --config slat_multiobj_shape/configs/front3d_sage_32.json

Feature preparation writes object_targets/ and object_features/ under each dataset's slat/32/ directory. It retains clean GT depth for fresh augmentation during training. Testing uses MoGe-3 depth and calibration. Training results reside under outputs/, and model checkpoints reside under checkpoints/.

Upload contract

bundles/<dataset>/index.json describes prepared extent archives. precomputed_slat/<dataset>/index.json describes SLAT target archives. Each inventory records archive sizes, SHA-256 checksums, and the restore destination. SLAT inventories also record source manifests, codec provenance, coverage, and encoding failures.

An inventory's complete=true means packaging covers the snapshot files. The uploader publishes a complete inventory after every archive is present with matching size. It also compares remote SHA-256 checksums when Hub file metadata provides them. source_complete separately records whether source preprocessing finished. An upload can therefore finish while source object or scene encoding remains incomplete. The upload preserves failure records and pending scene statuses.

Package shape targets with this command:

python -m preprocess.huggingface pack \
  --artifact-kind slat-shape-targets \
  --dataset 3dfront=datasets/3dfront/slat/32/targets \
  --dataset sage=datasets/sage/slat/32/targets \
  --output outputs/huggingface/slat_targets_publish

Publish those archives to an existing dataset repository with this command:

python -m preprocess.huggingface upload \
  --repo cubec/PartProbe3D-preprocessed \
  --bundle-root precomputed_slat --dataset 3dfront --dataset sage \
  --output outputs/huggingface/slat_targets_publish

The target upload excludes worker jobs, raw assets, model weights, and frozen image feature caches. Use a fresh packaging directory when source manifests change. Publish matching extent and target snapshots before documenting a new reproducible data revision.

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. The extent upload does not include model weights. SLAT target inventories record the frozen encoder provenance. Source datasets retain their original licenses and access terms.