Include encoded SS targets for foundationpose in standard restoration

README.md

@@ -12,7 +12,7 @@ pretty_name: PartProbe3D compact reproduction data
 # PartProbe3D reproduction data
 
 This release supports SS, generative SLAT segmentation, and 32³ SLAT shape training.
-The default transfer is **201.74 GB**, including observations, SS targets, and available shape targets.
+The default transfer is **271.79 GB**, including observations, SS targets, and available shape targets.
 Default downloads exclude optional and deprecated folders.
 
 <!-- partprobe3d-required-files:start -->
@@ -42,19 +42,13 @@ Older checkouts also require the corresponding restore patch under `code/`.
 
 ## Restore tools
 
-Use the project checkout at Git revision `aac7b493e1e5e10512857a2582233c99fedda9c5`.
-Apply the supplied restore patch before using the compact data.
-The patch preserves unrelated files and adds the compact readers and download commands.
+Use project Git revision `32d92a1e1b9e99eafaac9dda13c43e676a7aeeb3` or later.
+This revision includes the restore tools and the online SS defaults.
 
 ```bash
-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]'
 ```
 
-The [source manifest](code/manifest.json) records the patch checksum and required Git revision.
-
 ## Default download
 
 Run this command from the patched project root:
@@ -63,7 +57,8 @@ Run this command from the patched project root:
 python -m preprocess.huggingface download --repo cubec/PartProbe3D-preprocessed
 ```
 
-The command restores 3D-FRONT, SAGE-10k, and their 32³ object shape targets.
+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.
@@ -73,9 +68,11 @@ Add `--keep-archives` only when you need temporary transfer files.
 | --- | ---: | ---: | ---: | ---: |
 | 3dfront | 39,930 | 39,930 | 16 | 123.01 |
 | sage | 17,456 | 17,453 | 16 | 78.73 |
-| foundationpose | 42,256 | Unavailable | 16 | 61.76 |
+| foundationpose | 42,256 | Unavailable | 16 | 70.05 |
 
-FoundationPose requires explicit selection and supports SS and segmentation.
+The default includes FoundationPose for SS and segmentation.
+FoundationPose shape targets remain unavailable.
+Use this command to restore FoundationPose alone:
 
 ```bash
 python -m preprocess.huggingface download --repo cubec/PartProbe3D-preprocessed --dataset foundationpose
@@ -127,13 +124,12 @@ python -m preprocess.huggingface download --repo cubec/PartProbe3D-preprocessed
 ## Train all three stages
 
 Obtain the configured model weights separately.
-Run SS preparation before SS training.
+The restored SS targets support online SS training directly.
 
 ```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 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
@@ -205,6 +201,7 @@ Model weights remain separate dependencies.
 | --- | ---: | ---: |
 | 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:
 
@@ -212,7 +209,7 @@ Use the current project checkout and download the selected datasets:
 python -m preprocess.huggingface download --repo cubec/PartProbe3D-preprocessed
 ```
 
-Publication continues for the remaining datasets. The listed datasets already include SS targets.
+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.

training/index.json

@@ -3,7 +3,8 @@
   "complete": true,
   "default_datasets": [
     "3dfront",
-    "sage"
+    "sage",
+    "foundationpose"
   ],
   "monitor_views_per_dataset": 16,
   "datasets": {
@@ -67,15 +68,28 @@
     },
     "foundationpose": {
       "required": [
-        "training/observations/foundationpose"
+        "training/observations/foundationpose",
+        "training/ss/foundationpose"
       ],
-      "bytes": 61758310400,
+      "bytes": 70050703360,
       "source_manifest_sha256": "2c4dcd1cd1e2e56a0eb08f2f92da320f299729c8ce7a2f52828c272cbc1fa0ad",
       "resolution": 64,
       "source_views": 46948,
       "views": 42272,
       "monitor_views": 16,
-      "training_views": 42256
+      "training_views": 42256,
+      "ss_targets": {
+        "component": "training/ss/foundationpose",
+        "bytes": 8292392960,
+        "views": 46948,
+        "latent_shape": [
+          8,
+          16,
+          16,
+          16
+        ],
+        "target_scope": "masked_objects_complete_occupancy"
+      }
     }
   },
   "optional": {