Publish README.md

README.md

@@ -1,25 +1,25 @@
 ---
-pretty_name: PartProbe3D compact reproduction data
+language:
+- en
 tags:
 - 3d
+- depth
 - segmentation
+- voxel-occupancy
+pretty_name: PartProbe3D compact reproduction data
 ---
 
-# PartProbe3D compact training data
+# PartProbe3D reproduction data
 
-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.
+This release supports SS, generative SLAT segmentation, and 32³ SLAT shape training.
+The default transfer is **166.32 GB**, including both training datasets and object shape targets.
 Default downloads exclude optional and deprecated folders.
 
-**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.
-
-## Restore the tools
+## Restore tools
 
 Use the project checkout at Git revision `aac7b493e1e5e10512857a2582233c99fedda9c5`.
-Apply the supplied restore patch:
+Apply the supplied restore patch before using the compact data.
+The patch preserves unrelated files and adds the compact readers and download commands.
 
 ```bash
 hf download cubec/PartProbe3D-preprocessed code/reproduction.patch --repo-type dataset --local-dir outputs/huggingface/bootstrap
@@ -30,28 +30,116 @@ python -m pip install -e '.[preprocess,ss-multiobj-depth,slat-segvigen,slat-mult
 
 The [source manifest](code/manifest.json) records the patch checksum and required Git revision.
 
-## Download training data
+## Default download
+
+Run this command from the patched project root:
 
 ```bash
 python -m preprocess.huggingface download --repo cubec/PartProbe3D-preprocessed
 ```
 
-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.
+The command restores 3D-FRONT, SAGE-10k, and their 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 | 91.760 |
-| sage | 17,456 | 17,453 | 16 | 74.558 |
+| 3dfront | 39,930 | 39,930 | 16 | 91.76 |
+| sage | 17,456 | 17,453 | 16 | 74.56 |
+| foundationpose | 42,256 | Unavailable | 16 | 61.76 |
+
+FoundationPose requires explicit selection and supports SS and segmentation.
+
+```bash
+python -m preprocess.huggingface download --repo cubec/PartProbe3D-preprocessed --dataset foundationpose
+```
 
-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.
+## 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 final card will include the optional download commands.
+The export omits combined scene latents because the current shape objective uses object targets.
+Model weights and image-feature caches remain separate dependencies.
+
+## Optional components
+
+| Component | Purpose | Transfer, GB |
+| --- | --- | ---: |
+| `evaluation/ycbv` | YCB-V mesh evaluation | 1.670 |
+| `geometry/3dfront` | Mesh scoring and new target encoding | 6.252 |
+| `geometry/sage` | Mesh scoring and new target encoding | 303.693 |
+| `held_out/3dfront` | Remaining test observations | 22.440 |
+| `held_out/foundationpose` | Remaining test observations | 6.825 |
+| `held_out/sage` | Remaining test observations | 8.183 |
+| `occlusion/3dfront` | GT relations for SS object attention | 0.240 |
+| `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:
+
+```bash
+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
+```
 
-An unfiltered Hub snapshot download includes all folders.
+## Train all three stages
+
+Obtain the configured model weights separately.
+Run SS preparation before SS training.
+
+```bash
+export CUDA_VISIBLE_DEVICES=4,6
+export CUDA_DEVICE_ORDER=PCI_BUS_ID
+python -m ss_multiobj_depth.prepare --config ss_multiobj_depth/configs/front3d_sage.json
+python -m ss_multiobj_depth.train --config ss_multiobj_depth/configs/front3d_sage.json
+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:
+
+```bash
+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](https://huggingface.co/datasets/cubec/PartProbe3D-preprocessed/tree/d13e577a7e71291ed94fa40e4297f54106577fbd) 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.
+
+![3D-FRONT RGB](previews/3dfront/train-rgb.png)
+
+![3D-FRONT visible masks](previews/3dfront/train-overlay.png)
+
+[Posed object geometry](previews/3dfront/train-geometry.glb)