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+}
diff --git a/0000_/sam6d_sequences/Tube/Tube_world_pose.json b/0000_/sam6d_sequences/Tube/Tube_world_pose.json
new file mode 100644
index 0000000000000000000000000000000000000000..e6f7ea070b58bf981ccb6b8ae7ba44c9ed843f83
--- /dev/null
+++ b/0000_/sam6d_sequences/Tube/Tube_world_pose.json
@@ -0,0 +1,324 @@
+{
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+  "sources": {
+    "config_path": "/workspace/scripts/configs/default.yaml",
+    "object_poses": "/workspace/data/0000_/experimental_multi_instance/pem/Tube/Tube_poses_per_frame.json",
+    "colmap_reconstruction": "/workspace/data/0000_/reconstruction/sfm_filtered",
+    "sim3_alignment": "/workspace/data/0000_/reconstruction/metric_alignment"
+  }
+}
diff --git a/0000_/sam6d_sequences/TubeHolder/TubeHolder_world_pose.json b/0000_/sam6d_sequences/TubeHolder/TubeHolder_world_pose.json
new file mode 100644
index 0000000000000000000000000000000000000000..4a8550690a2475d697a22839633a63119c2a35ad
--- /dev/null
+++ b/0000_/sam6d_sequences/TubeHolder/TubeHolder_world_pose.json
@@ -0,0 +1,436 @@
+{
+  "object_name": "TubeHolder",
+  "object_path": "/workspace/data/0000_/objects/stl/TubeHolder.stl",
+  "coordinate_system": "world",
+  "translation_unit": "metre",
+  "instance_count": 1,
+  "low_support": false,
+  "instances": [
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diff --git a/0000_/sam6d_sequences/eppendorf_tarto/eppendorf_tarto_world_pose.json b/0000_/sam6d_sequences/eppendorf_tarto/eppendorf_tarto_world_pose.json
new file mode 100644
index 0000000000000000000000000000000000000000..8a7369ec3da2362dc02a7673c2d21bbb3529f85a
--- /dev/null
+++ b/0000_/sam6d_sequences/eppendorf_tarto/eppendorf_tarto_world_pose.json
@@ -0,0 +1,1024 @@
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+        ],
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+      },
+      {
+        "number_of_inliers": 6,
+        "translation": [
+          -0.2407270199495843,
+          0.30148117894226906,
+          0.018291655152994264
+        ],
+        "silhouette_iou_median": 0.8983930411045553,
+        "reason": "silhouette_iou_below_threshold"
+      },
+      {
+        "number_of_inliers": 5,
+        "translation": [
+          -0.2104993277741878,
+          0.176503012451081,
+          0.10721331386809792
+        ],
+        "silhouette_iou_median": 0.5164397827865804,
+        "reason": "silhouette_iou_below_threshold"
+      }
+    ]
+  },
+  "skipped_frames": [
+    {
+      "frame_id": 0,
+      "reason": "no_object_pose"
+    },
+    {
+      "frame_id": 20,
+      "reason": "no_object_pose"
+    }
+  ],
+  "sources": {
+    "config_path": "/workspace/scripts/configs/default.yaml",
+    "object_poses": "/workspace/data/0000_/experimental_multi_instance/pem/eppendorf_tarto/eppendorf_tarto_poses_per_frame.json",
+    "colmap_reconstruction": "/workspace/data/0000_/reconstruction/sfm_filtered",
+    "sim3_alignment": "/workspace/data/0000_/reconstruction/metric_alignment"
+  }
+}
diff --git a/0000_/sam6d_sequences/ransac_run_summary.json b/0000_/sam6d_sequences/ransac_run_summary.json
new file mode 100644
index 0000000000000000000000000000000000000000..a58d5ff5701b8cf35b6f7c0e3db2ac9016a0d479
--- /dev/null
+++ b/0000_/sam6d_sequences/ransac_run_summary.json
@@ -0,0 +1,15 @@
+{
+  "format": "label_factory_multi_instance_pose_fitting_summary_v1",
+  "objects_requested": [
+    "Tube"
+  ],
+  "found_objects": {
+    "Tube": 1
+  },
+  "non_found_objects": [],
+  "objects_succeeded": [
+    "Tube"
+  ],
+  "low_support_objects": [],
+  "objects_excluded": []
+}
diff --git a/README.md b/README.md
index 32897cd3e640101ba184f8c4ccd896981de3804a..7b0c93b76fc52f48699bf32c439f10579cfe9394 100644
--- a/README.md
+++ b/README.md
@@ -1,3 +1,158 @@
 ---
+pretty_name: Label Factory Multi-Task RGB-D Collection
 license: mit
+tags:
+- image
+- video
+- 3d
+- rgb-d
+configs:
+- config_name: depth_estimation
+  data_files:
+  - split: train
+    path: "*/data/depth_estimation/train-*.parquet"
+  - split: validation
+    path: "*/data/depth_estimation/validation-*.parquet"
+  - split: test
+    path: "*/data/depth_estimation/test-*.parquet"
+- config_name: instance_segmentation
+  data_files:
+  - split: train
+    path: "*/data/instance_segmentation/train-*.parquet"
+  - split: validation
+    path: "*/data/instance_segmentation/validation-*.parquet"
+  - split: test
+    path: "*/data/instance_segmentation/test-*.parquet"
+- config_name: object_pose_estimation
+  data_files:
+  - split: train
+    path: "*/data/object_pose_estimation/train-*.parquet"
+  - split: validation
+    path: "*/data/object_pose_estimation/validation-*.parquet"
+  - split: test
+    path: "*/data/object_pose_estimation/test-*.parquet"
+- config_name: object_pose_models
+  data_files:
+  - split: train
+    path: "*/data/object_pose_estimation/models.parquet"
 ---
+
+# ABC-iRobotics/LabWare_Items
+
+Growing multi-task RGB-D collection generated with the Label Factory workflow.
+Every capture is stored in its own zero-padded directory (`0000`, `0001`,
+`0002`, ...). Uploading a new capture appends it to the collection without
+replacing earlier directories. Task configurations aggregate compatible
+Parquet shards from every numbered directory.
+
+## Training configurations
+
+- `depth_estimation`: RGB input, metric depth target, intrinsics and depth units
+- `instance_segmentation`: RGB input, instance-mask target, boxes and annotations
+- `object_pose_estimation`: RGB-D input, masks, camera transforms and object poses
+- `object_pose_models`: one STL payload per object type and numbered dataset
+
+Every configuration provides deterministic `train`, `validation`, and `test`
+splits. Adding another numbered capture appends rows through the wildcard paths;
+it does not replace earlier datasets.
+
+## Loading all or several numbered datasets
+
+The configuration automatically combines every uploaded directory (`0000`,
+`0001`, `0002`, ...). Filter `dataset_id` to use selected captures:
+
+```python
+from datasets import load_dataset
+
+poses = load_dataset("ABC-iRobotics/LabWare_Items", "object_pose_estimation")
+selected_ids = set(["0000", "0001", "0004"])
+for split_name in poses:
+    poses[split_name] = poses[split_name].filter(
+        lambda row: row["dataset_id"] in selected_ids
+    )
+```
+
+## Depth-estimation example
+
+```python
+from datasets import load_dataset
+import numpy as np
+
+depth_dataset = load_dataset("ABC-iRobotics/LabWare_Items", "depth_estimation")
+sample = depth_dataset["train"][0]
+rgb = sample["image"].convert("RGB")
+depth_native = np.asarray(sample["depth"])
+depth_mm = depth_native.astype(np.float32) * sample["depth_scale"]
+depth_m = depth_mm / 1000.0
+intrinsics_3x3 = np.asarray(sample["camera_intrinsics"]).reshape(3, 3)
+```
+
+## Instance-segmentation example
+
+```python
+from datasets import load_dataset
+import json
+import numpy as np
+
+instances = load_dataset("ABC-iRobotics/LabWare_Items", "instance_segmentation")
+sample = instances["validation"][0]
+rgb = sample["image"].convert("RGB")
+instance_mask = np.asarray(sample["instance_mask"])
+annotations = json.loads(sample["annotations_json"])
+boxes_xywh = np.asarray(sample["boxes_xywh"])
+instance_names = sample["instance_names"]
+```
+
+## Object-pose-estimation example
+
+```python
+from datasets import load_dataset
+import numpy as np
+
+poses = load_dataset("ABC-iRobotics/LabWare_Items", "object_pose_estimation")
+sample = poses["test"][0]
+rgb = sample["image"].convert("RGB")
+depth = np.asarray(sample["depth"])
+instance_mask = np.asarray(sample["instance_mask"])
+world_from_camera = np.asarray(sample["world_from_camera"]).reshape(4, 4)
+camera_from_world = np.asarray(sample["camera_from_world"]).reshape(4, 4)
+world_from_objects = np.asarray(sample["world_from_objects"]).reshape(-1, 4, 4)
+camera_from_objects = np.asarray(sample["camera_from_objects"]).reshape(-1, 4, 4)
+object_names = sample["object_names"]
+
+# Resolve the STL models referenced by this pose sample.
+models = load_dataset("ABC-iRobotics/LabWare_Items", "object_pose_models")["train"]
+required_keys = set(sample["object_model_keys"])
+models = models.filter(lambda row: row["object_model_key"] in required_keys)
+first_model = models[0]
+with open(first_model["stl_file_name"], "wb") as file:
+    file.write(first_model["stl"])
+```
+
+## Collection contents
+
+Each numbered directory contributes:
+
+- RGB images and aligned 16-bit metric depth maps
+- camera intrinsics and metric world-coordinate camera poses
+- instance masks, boxes, RLE annotations and visualization overlays
+- per-frame and consolidated object poses
+- metric scene reconstruction and object STL models
+- task-specific train, validation and test Parquet shards
+
+## Latest generated contribution
+
+- Directory: `0000_/`
+- RGB-D frames: 250
+- Annotated object instances: 5
+- Selected upload files: 1538
+
+Depth images are stored in millimetres. Pose conventions and coordinate-system
+metadata are recorded in `0000_/sam6d_scene/annotations.json` and
+`0000_/reconstruction/metric_alignment/camera_poses_metric.json`.
+
+## Limitations
+
+Annotations are automatically generated and must be independently reviewed for
+the intended downstream use. Reconstruction, depth-alignment, segmentation and
+pose-estimation errors may remain.