Publish README.md

README.md

@@ -1,498 +1,57 @@
 ---
-language:
-- en
+pretty_name: PartProbe3D compact reproduction data
 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
-- config_name: foundationpose
-  data_files:
-  - split: test
-    path: catalog/foundationpose/test.jsonl
-  - split: train
-    path: catalog/foundationpose/train.jsonl
 ---
 
-# PartProbe3D preprocessed datasets
+# PartProbe3D compact training data
 
-Saved 64³ training and evaluation inputs for PartProbe3D preserve calibrated observations and voxel targets.
-The 3D-FRONT, SAGE-10k, and YCB-V bundles also retain posed meshes.
-FoundationPose contains an export for training without meshes.
-Targets describe triangle surfaces, including hidden surfaces and overlapping object channels.
+The default data supports SS, generative SLAT segmentation, and 32³ SLAT shape training.
+The required transfer is **166.32 GB**, including both datasets and object shape targets.
+Default downloads exclude optional and deprecated folders.
 
-| 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 |
-| foundationpose | train | 42,256 | 42,256 |
-| foundationpose | test | 4,692 | 4,692 |
+**Publication status:** required training data passed verification.
+The publisher continues to upload some optional components.
+Legacy migration will follow these uploads.
+The final dataset card will list every optional component.
 
-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.
+## Restore the tools
 
-## 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 |
-| foundationpose | True | True | 83.19 | 83.19 |
-
-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.
-The FoundationPose export contains 46,948 views in 86 verified archives.
-
-
-## FoundationPose on another server
-
-Use [code revision `92ca3ee`](https://github.com/cube-c/PartProbe3D/commit/92ca3eea1dbec6e162e246858eb7bdb7f7fd0701) or a compatible later revision on `slat-shape`.
-The FoundationPose bundle preserves original RGB, depth, visible masks, cameras, object mapping, and complete per-object voxel occupancy at 64³.
-Its source manifest has SHA-256 `2c4dcd1cd1e2e56a0eb08f2f92da320f299729c8ce7a2f52828c272cbc1fa0ad`.
-The extracted files occupy 87.82 GB before filesystem overhead.
-The [verified inventory](https://huggingface.co/datasets/cubec/PartProbe3D-preprocessed/blob/073988c5b5e206570da67f57aada4a2f0ad94e95/bundles/foundationpose/index.json) contains 86 archives totaling 89.33 GB.
-The export excludes raw archives, meshes, model weights, image features, SLAT latents, and GT occlusion relations.
-
-The loaders resolve input paths relative to the restored dataset.
-Historical absolute paths remain provenance entries.
-SS preparation creates the union of complete occupancy for objects with supplied masks and excludes other object voxels.
-
-### Restore the data
-
-Use Python 3.12 and a compatible PyTorch/CUDA installation.
-Run these commands from the repository root.
-
-1. Install the project dependencies.
-
-   ```bash
-   python -m pip install -e '.[preprocess,ss-multiobj-depth,tracking]'
-   ```
-
-2. Restore the prepared observations and voxel targets.
-
-   ```bash
-   python -m preprocess.huggingface download \
-     --repo cubec/PartProbe3D-preprocessed --dataset foundationpose --cleanup \
-     --revision 073988c5b5e206570da67f57aada4a2f0ad94e95
-   ```
-
-3. Initialize the pinned Pixal3D source.
-
-   ```bash
-   git submodule update --init third_party/Pixal3D
-   ```
-
-`--cleanup` retains extracted data and removes each verified temporary archive.
-Allow at least 100 GB for data restoration, plus space for weights and model features.
-Use `--revision <Hub commit>` to retain one dataset snapshot.
-
-### Acquire SS weights
-
-All required weights for the following SS configuration are available from Hugging Face.
-The commands pin revisions with the same configuration and weight bytes as the verified local files.
-DINOv3 requires approved access to its [gated model repository](https://huggingface.co/facebook/dinov3-vitl16-pretrain-lvd1689m).
-
-1. Authenticate with an account that can download DINOv3.
-
-   ```bash
-   hf auth login
-   ```
-
-2. Download the native SS flow and decoder from [Pixal3D](https://huggingface.co/TencentARC/Pixal3D).
-
-   ```bash
-   hf download TencentARC/Pixal3D \
-     --revision b0cb2e1b794cab9aa0ac38a95d794a4d9337437f \
-     --local-dir checkpoints/Pixal3D \
-     --include 'ckpts/ss_flow_img_dit_1_3B_64_bf16.*' 'ckpts/ss_dec_conv3d_16l8_fp16.*'
-   ```
-
-3. Download the SS encoder and paired decoder from [TRELLIS](https://huggingface.co/microsoft/TRELLIS-image-large).
-
-   ```bash
-   hf download microsoft/TRELLIS-image-large \
-     --revision 25e0d31ffbebe4b5a97464dd851910efc3002d96 \
-     --local-dir checkpoints/TRELLIS \
-     --include 'ckpts/ss_enc_conv3d_16l8_fp16.*' 'ckpts/ss_dec_conv3d_16l8_fp16.safetensors'
-   ```
-
-4. Download DINOv3.
-
-   ```bash
-   hf download facebook/dinov3-vitl16-pretrain-lvd1689m \
-     --revision ea8dc2863c51be0a264bab82070e3e8836b02d51 \
-     --local-dir checkpoints/DINOv3 --include config.json model.safetensors
-   ```
-
-### Prepare features and train SS
-
-The [FoundationPose configuration](https://github.com/cube-c/PartProbe3D/blob/92ca3eea1dbec6e162e246858eb7bdb7f7fd0701/ss_multiobj_depth/configs/foundationpose.json) uses GT depth, `depth_embedding` voxel conditioning, global cross-attention, and rank-8 NoRA.
-It creates features under `outputs/ss_multiobj_depth/foundationpose/features` and preserves the saved training/test splits.
-It starts a new run with W&B online by default.
-Online logging requires W&B authentication.
-The setting `training.wandb_mode="disabled"` permits a local run without W&B.
-
-1. Prepare local features and encoded SS targets.
-
-   ```bash
-   python -m ss_multiobj_depth.prepare --config ss_multiobj_depth/configs/foundationpose.json
-   ```
-
-2. Start training.
-
-   ```bash
-   python -m ss_multiobj_depth.train --config ss_multiobj_depth/configs/foundationpose.json
-   ```
-
-This configuration needs no source archives, meshes, or additional dataset downloads.
-The `occlusion_object` variant additionally requires a matching GT occlusion cache.
-That cache requires mesh geometry during preparation and is absent from this FoundationPose bundle.
-A matching GT occlusion overlay can transfer separately without another download of the observation archives.
-New SLAT shape encoding and mesh-based evaluation also require geometry or separately transferred targets.
-Exact resume of an existing run requires its matching checkpoint, feature manifests, source hashes, and settings.
-
-
-### FoundationPose previews
-
-These previews show saved inputs and GT voxel targets. They use no learned checkpoint.
-
-![FoundationPose train RGB](previews/foundationpose/train-rgb.png)
-
-![FoundationPose train masks](previews/foundationpose/train-overlay.png)
-
-[GT occupancy, train (GLB)](previews/foundationpose/train-occupancy.glb)
-
-![FoundationPose test RGB](previews/foundationpose/test-rgb.png)
-
-![FoundationPose test masks](previews/foundationpose/test-overlay.png)
-
-[GT occupancy, test (GLB)](previews/foundationpose/test-occupancy.glb)
-
-The [publication report](https://github.com/cube-c/PartProbe3D/blob/139c539/reports/huggingface_foundationpose_training_20261019.md) records checks, dataset identities, and limitations.
-
-## GT occlusion relations
-
-Separate archives under `precomputed_occlusion/` preserve the completed GT occlusion caches.
-The caches contain directed `[front, back]` relations from calibrated visible masks and posed GT mesh silhouettes.
-Each archive preserves the original matrices, completion records, and manifest bytes.
-
-| Dataset | Training views | Test views | Archives | Size, MB |
-| --- | ---: | ---: | ---: | ---: |
-| 3D-FRONT | 39,930 | 10,066 | 3 | 240.01 |
-| SAGE-10k | 17,456 | 1,952 | 3 | 103.45 |
-| Total | 57,386 | 12,018 | 6 | 343.46 |
-
-MB means decimal megabytes.
-The inventories at `precomputed_occlusion/<dataset>/index.json` record archive sizes and SHA-256 checksums.
-They also preserve the source policy and checksum of the matching extent manifest.
-
-### Download GT occlusion caches
-
-Restore the matching extent datasets before these overlays.
-Run these commands from the repository root.
-
-```bash
-python -m preprocess.huggingface download \
-  --repo cubec/PartProbe3D-preprocessed --bundle-root precomputed_occlusion \
-  --dataset 3dfront --revision 88cdf8f0e880a33e7cafa580ee9722776f16aa28 --cleanup
-python -m preprocess.huggingface download \
-  --repo cubec/PartProbe3D-preprocessed --bundle-root precomputed_occlusion \
-  --dataset sage --revision 88cdf8f0e880a33e7cafa580ee9722776f16aa28 --cleanup
-```
-
-The commands restore `datasets/<dataset>/extent/point_cloud/occlusion/`.
-The downloader verifies each archive and refuses to replace different existing files.
-The dataset reader checks the extent manifest identity before reading GT relations.
-
-SS training with occlusion-aware object cross-attention reads these relations.
-SLAT shape tokens already have instance identities and require no occlusion matrix.
-Frozen image features and SLAT shape latents remain separate artifacts.
-
-
-### Publish GT occlusion caches
-
-Package the verified caches from the prepared extent roots:
-
-```bash
-python -m preprocess.huggingface pack --artifact-kind gt-occlusion \
-  --dataset 3dfront=datasets/3dfront/extent/point_cloud \
-  --dataset sage=datasets/sage/extent/point_cloud \
-  --output outputs/huggingface/gt_occlusion
-python -m preprocess.huggingface upload \
-  --repo cubec/PartProbe3D-preprocessed --bundle-root precomputed_occlusion \
-  --dataset 3dfront --dataset sage --output outputs/huggingface/gt_occlusion
-```
-
-Packaging checks every matrix checksum and its completion record.
-The uploader publishes complete inventories after it verifies all remote archives.
-
-## 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:
+Use the project checkout at Git revision `aac7b493e1e5e10512857a2582233c99fedda9c5`.
+Apply the supplied restore patch:
 
 ```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
+hf download cubec/PartProbe3D-preprocessed code/reproduction.patch --repo-type dataset --local-dir outputs/huggingface/bootstrap
+git apply --check outputs/huggingface/bootstrap/code/reproduction.patch
+git apply outputs/huggingface/bootstrap/code/reproduction.patch
+python -m pip install -e '.[preprocess,ss-multiobj-depth,slat-segvigen,slat-multiobj-shape]'
 ```
 
-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 [source manifest](code/manifest.json) records the patch checksum and required Git revision.
 
-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:
+## Download training data
 
 ```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
+python -m preprocess.huggingface download --repo cubec/PartProbe3D-preprocessed
 ```
 
-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`.
+The command restores 3D-FRONT, SAGE-10k, and their object shape targets.
+It pins one Hub revision and verifies every archive.
+It removes temporary archives after extraction.
+Model weights remain separate dependencies.
+Use the original saved training configurations and matching pretrained weights.
 
-![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)
+| Dataset | SS/segmentation training views | Shape training views | Monitoring views | Transfer, GB |
+| --- | ---: | ---: | ---: | ---: |
+| 3dfront | 39,930 | 39,930 | 16 | 91.760 |
+| sage | 17,456 | 17,453 | 16 | 74.558 |
 
-![Prompt masks](previews/ycbv/test-overlay.png)
+The first 16 test views per dataset support existing training monitors.
+Remaining test observations, mesh geometry, and evaluation datasets require explicit optional downloads.
+Shape training preserves the three existing SAGE exclusions.
+SS and segmentation retain those views.
+The final card will include the optional download commands.
 
-[Posed prompted GT geometry (GLB)](previews/ycbv/test-geometry.glb)
+An unfiltered Hub snapshot download includes all folders.
+Use the project downloader for minimal transfer.