README.md

---
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.

1. Restore the prepared 3D-FRONT extent dataset.

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

2. Restore the prepared SAGE-10k extent dataset.

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

3. Restore the 3D-FRONT shape targets.

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

4. Restore the SAGE-10k shape targets.

   ```bash
   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.

1. Obtain the configured Pixal3D, DINOv3, and NAF weights separately.
2. Resolve the three failed SAGE-10k object targets.
3. Prepare frozen conditioning features and immutable object targets.
4. 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.

```bash
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:

```bash
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:

```bash
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:

```bash
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:

```bash
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:

```bash
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.

## Input and geometry previews

### sage: test

View: `0078d732045c32e0e3c2`.

![RGB](previews/sage/test-rgb.png)

![Prompt masks](previews/sage/test-overlay.png)

[Posed prompted GT geometry (GLB)](previews/sage/test-geometry.glb)

### sage: train

View: `0002c10ce97a86f789fd`.

![RGB](previews/sage/train-rgb.png)

![Prompt masks](previews/sage/train-overlay.png)

[Posed prompted GT geometry (GLB)](previews/sage/train-geometry.glb)

### 3dfront: test

View: `0001acce61c945bf4470`.

![RGB](previews/3dfront/test-rgb.png)

![Prompt masks](previews/3dfront/test-overlay.png)

[Posed prompted GT geometry (GLB)](previews/3dfront/test-geometry.glb)

### 3dfront: train

View: `0000b002995b4131d534`.

![RGB](previews/3dfront/train-rgb.png)

![Prompt masks](previews/3dfront/train-overlay.png)

[Posed prompted GT geometry (GLB)](previews/3dfront/train-geometry.glb)

### ycbv: test

View: `003c09039641a2b2efa1`.

![RGB](previews/ycbv/test-rgb.png)

![Prompt masks](previews/ycbv/test-overlay.png)

[Posed prompted GT geometry (GLB)](previews/ycbv/test-geometry.glb)