cubec/PartProbe3D-preprocessed

PartProbe3D reproduction data

This release supports SS, generative SLAT segmentation, and 32³ SLAT shape training. The default transfer is 271.79 GB, including observations, SS targets, and available shape targets. Default downloads exclude optional and deprecated folders.

Mandatory files

These paths refer to the Hugging Face repository root. Files are mandatory for the task specified in each row. The default command restores training files. The benchmark also requires the separate download with --dataset evaluation.

File or directory	Required task	Contents
training/index.json	Training restoration	Component paths, sizes, monitoring counts, and source identities
training/observations/<dataset>/	Training on the selected dataset	Archive index, sample NPZ files, calibration, object mappings, and scene voxel targets
training/ss/<dataset>/	Direct SS training after target publication	Archive index, native SS latents, and codec provenance
training/shape/32/<dataset>/	SLAT shape training where targets are available	Archive index and object shape targets with explicit exclusions
evaluation/index.json	Benchmark restoration	Archive list and SHA-256 checksums
evaluation/metadata.tar	Benchmark evaluation	Portable manifests, source selections, and dataset documentation
evaluation/picasso-*.tar	Benchmark evaluation	All 100 PICASSO images, masks, previews, and GT meshes
evaluation/messykitchen-*.tar	Benchmark evaluation	All 1,503 MessyKitchens images, masks, previews, and 100 shared GT meshes
evaluation/3dfront-*.tar	Benchmark evaluation	The selected 1,000 3D-FRONT test images, masks, previews, and GT meshes

Restore every archive listed in the selected component's index.json. The evaluation bundle has 33 mandatory archives and requires 29.313 GB. Older checkouts also require the corresponding restore patch under code/.

Download by task

Select a task to avoid unnecessary data transfers. The task catalog records component paths, sizes, and the data revision. The task guide lists downloads for individual datasets.

Task	Required data	Default transfer, GB
eval	RGB, masks and GT meshes	29.313
ss	Observations and native SS targets	265.667
slat-seg	Observations only	221.950
slat-shape	Observations and 32³ object shape targets	233.780
all-training	All published training components	277.497

Use the current project checkout for these commands. Preview the transfer before downloading:

python -m preprocess.huggingface download --repo cubec/PartProbe3D-preprocessed --task ss --dry-run

Select the task required by this server:

python -m preprocess.huggingface download --repo cubec/PartProbe3D-preprocessed --task eval
python -m preprocess.huggingface download --repo cubec/PartProbe3D-preprocessed --task ss
python -m preprocess.huggingface download --repo cubec/PartProbe3D-preprocessed --task slat-seg
python -m preprocess.huggingface download --repo cubec/PartProbe3D-preprocessed --task slat-shape

Select individual datasets with --dataset, using commas for multiple datasets.

python -m preprocess.huggingface download --repo cubec/PartProbe3D-preprocessed --task eval --dataset picasso
python -m preprocess.huggingface download --repo cubec/PartProbe3D-preprocessed --task eval --dataset picasso,messykitchen
python -m preprocess.huggingface download --repo cubec/PartProbe3D-preprocessed --task slat-seg --dataset foundationpose

Task defaults follow the published component inventory. Explicit shape requests require matching object targets before archive transfer.

SS uses the published native targets without another target encoding pass. Segmentation computes its instance codes and image features during training.

Online shape conditioning avoids a separate image-feature cache.

Evaluation metadata excludes the project README. Dataset provenance documents remain in the archive.

Model weights remain separate. Download only the assets required by the selected model.

Old commands without --task still restore all training components.

Restore tools

Use project Git revision 6e6c5cceec2a6e750513aa7d51ed3e585e6604ff or later. This revision includes the restore tools and the online SS defaults.

python -m pip install -e '.[preprocess,ss-multiobj-depth,slat-segvigen,slat-multiobj-shape]'

Default download

Run this command from the patched project root:

python -m preprocess.huggingface download --repo cubec/PartProbe3D-preprocessed

The command restores observations and SS targets for 3D-FRONT, SAGE-10k, and FoundationPose. It also restores available 32³ object shape targets. It verifies each archive and removes temporary archives after restoration. The downloader pins one Hub revision before reading any archive. Add --revision <Hub commit> to select a fixed release. Add --keep-archives only when you need temporary transfer files.

Dataset	SS/segmentation training views	Shape training views	Monitoring views	Transfer, GB
3dfront	39,930	39,930	16	123.01
sage	17,456	17,453	16	78.73
foundationpose	42,256	42,256	16	75.75

The default includes FoundationPose for SS and segmentation. FoundationPose shape targets remain unavailable. Use this command to restore FoundationPose alone:

python -m preprocess.huggingface download --repo cubec/PartProbe3D-preprocessed --dataset foundationpose

Contents

Sample NPZ files retain RGB, metric depth, visible labels, complete object occupancy, and the original transforms. Calibration, scene voxel targets, object mappings, and original extent manifests remain available. The compact export omits separate RGB, masks, depth copies, overlays, and duplicate voxel exports. Every training view remains available to SS and segmentation. The first 16 test views per dataset support existing eight-view and sixteen-view training monitors. All other test observations require an optional download.

Object shape arrays retain their original values and identities. Shape manifests explicitly exclude the three failed SAGE views used by the existing experiment. SS and segmentation retain those views. The export omits combined scene latents because the current shape objective uses object targets. Model weights and image-feature caches remain separate dependencies.

Optional files

These files support additional datasets, test views, and target generation. The mandatory evaluation bundle already includes its RGB images, masks, and GT meshes. Some SS configurations require the matching optional occlusion component.

File or directory	Purpose	Transfer, GB
optional/evaluation/ycbv/	YCB-V mesh evaluation	1.670
optional/geometry/3dfront/	Mesh scoring and new target encoding	6.252
optional/geometry/sage/	Mesh scoring and new target encoding	303.693
optional/held_out/3dfront/	Remaining test observations	22.440
optional/held_out/foundationpose/	Remaining test observations	6.825
optional/held_out/sage/	Remaining test observations	8.183
optional/occlusion/3dfront/	GT relations for SS object attention	0.240
optional/occlusion/sage/	GT relations for SS object attention	0.103

Restore observations before geometry, occlusion, or test overlays. YCB-V evaluation includes its own observations and geometry. Select each optional component explicitly:

python -m preprocess.huggingface download --repo cubec/PartProbe3D-preprocessed --dataset sage --optional geometry
python -m preprocess.huggingface download --repo cubec/PartProbe3D-preprocessed --dataset 3dfront --optional occlusion
python -m preprocess.huggingface download --repo cubec/PartProbe3D-preprocessed --dataset sage --optional held-out
python -m preprocess.huggingface download --repo cubec/PartProbe3D-preprocessed --dataset ycbv --optional evaluation

Train all three stages

Obtain the configured model weights separately. The restored SS targets support online SS training directly.

export CUDA_VISIBLE_DEVICES=4,6
export CUDA_DEVICE_ORDER=PCI_BUS_ID
python -m ss_multiobj_depth.train
python -m slat_segvigen.train --config slat_segvigen/configs/object_mean_image_nora_coverage_gate.json
python -m preprocess.slat.prepare --config preprocess/slat/configs/front3d_sage_32_assigned_object.json --stage features --devices cuda:0 cuda:1
python -m slat_multiobj_shape.train --config slat_multiobj_shape/configs/front3d_sage_32_assigned_object.json

Use the saved configuration and pretrained assets for a specific experiment. Feature preparation creates local caches without meshes or another dataset download. For lower local storage, generate an online shape configuration:

python -m preprocess.reproduction configure-shape --output outputs/reproduction/shape_online.json
python -m slat_multiobj_shape.train --config outputs/reproduction/shape_online.json

The generated configuration computes image conditioning during training and avoids separate image-feature caches. It preserves the selected preset's model and optimization settings. Use the original cached preset when reproducing a cached-feature experiment. Data and decoder provenance remain in the restored manifests. Exact checkpoint resume requires the original matching feature and data identities.

Deprecated uploads

_deprecated/20261020/ contains superseded bundles and catalogs. The project downloader never selects these paths. An unfiltered Hugging Face snapshot download includes every folder. Use the project downloader for minimal transfer. The original release remains available by revision.

Source terms and previews

Source dataset terms continue to apply to these derived observations. The prepared data does not replace access agreements for original source assets.

Posed object geometry

Required evaluation dataset

The required evaluation dataset contains 2,603 images with visible masks and GT object meshes. It includes all 100 PICASSO images, all 1,503 MessyKitchens images, and 1,000 random 3D-FRONT test views. The PartProbe3D benchmark requires this dataset. Benchmark inference and scoring require geometry meshes. Model comparisons preserve native geometry and omit independent texture generation. The transfer is 29.313 GB. MessyKitchens shares 100 GT scene meshes across its views. MessyKitchens uses reviewed GT-assisted SAM3 masks for all 1,503 views. The bundle retains exact independent masks, registration records, and all 13 audit flags. Native SAM3 quality resolves overlap for evaluation labels, with smaller object IDs breaking ties. See the mask release for the pinned revision and migration paths. Default training downloads exclude this bundle.

Use the current project checkout for the task commands.

python -m preprocess.huggingface download --repo cubec/PartProbe3D-preprocessed --task eval

Encoded SS targets

The standard download includes encoded SS targets for the datasets listed below. These targets remove GPU encoding from the restore procedure. They retain the original cache manifests, latent tensors, and monitoring targets. Their native latent shape is 8×16×16×16. Model weights remain separate dependencies.

Dataset	Target views	Transfer, GB
3dfront	49,996	31.249
sage	19,408	4.175
foundationpose	46,948	8.292

Use the current project checkout and download the selected datasets:

python -m preprocess.huggingface download --repo cubec/PartProbe3D-preprocessed --task ss

SS training can then use the default online configuration without a preparation command. The downloader checks archive hashes before extraction. The training loader checks dataset and frozen codec identities. The image encoders compute features during training.

Voxel-aligned 32³ scene latents

These optional caches contain one token per saved reference voxel. Overlapping objects share that token and retain separate membership labels. The latent_valid flag identifies cells with a native encoder output. Invalid cells retain their reference coordinates and contain zero latent values. The archives preserve the original NPZ files, calibration transforms, and codec provenance.

Dataset	Training views	Transfer, GB
3dfront	39,930	4.590
sage	17,456	1.176
foundationpose	42,256	5.046

Restore the matching observation bundles before reading scene targets. The slat-seg task downloads those observations without SS or object latent targets.

python -m preprocess.huggingface download --repo cubec/PartProbe3D-preprocessed --task slat-seg

Restore the selected scene caches:

python -m preprocess.huggingface download --repo cubec/PartProbe3D-preprocessed --dataset 3dfront --bundle-root optional/scene/32 --cleanup
python -m preprocess.huggingface download --repo cubec/PartProbe3D-preprocessed --dataset sage --bundle-root optional/scene/32 --cleanup
python -m preprocess.huggingface download --repo cubec/PartProbe3D-preprocessed --dataset foundationpose --bundle-root optional/scene/32 --cleanup

The restored caches reside under datasets/<dataset>/slat/32/scene_targets/. dataloader.slat_scene.SceneTargetDataset reads them with their matching saved observations. Raw meshes and source datasets are unnecessary for this reader. Scene downloads remain separate from object targets and default task downloads.

Deprecated MessyKitchens masks

Historical MessyKitchens mask archives now reside under _deprecated/messykitchen/original_masks/evaluation. Current evaluation downloads exclude these archives.