add ocdit copyright-safe subset

README.md

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+---
+license: cc-by-4.0
+pretty_name: ocdit
+tags:
+- bop
+- 6d-pose
+- synthetic
+- webdataset
+task_categories:
+- object-detection
+- image-segmentation
+size_categories:
+- 1M<n<10M
+---
+
+# ocdit
+
+This dataset was generated as part of the ICCV 2025 publication *Conditional Latent Diffusion Models for Zero-Shot Instance Segmentation* (see [Citation](#citation)).
+
+Synthetic, BOP-format training data for unseen object computer vision tasks, such as 6D pose estimation, instance segmentation, and detection. Scenes are physically rendered with BlenderProc; every object
+instance carries a 6D pose, amodal + visible masks, bounding boxes, and a visibility fraction.
+Alongside the scenes we ship the source **meshes** (`.glb`) and precomputed
+**templates** (`.h5`) so the data is self-contained for training. 
+
+Object identity is **name-based**: the string `obj_id` in each annotation is the
+object label, and it matches the mesh and template filename stems exactly
+(e.g. `obj_id: "11pro_SL_TRX_FG"` ↔ `meshes/gso/11pro_SL_TRX_FG.glb` ↔
+`templates/gso/11pro_SL_TRX_FG.h5`).
+
+## Structure
+
+```
+.
+├── shards/                     # WebDataset tar shards (BOP samples)
+│   ├── gso.random/             # shard-NNNNNN.tar
+│   ├── gso.flying/
+│   ├── oo3d.random/
+│   └── oo3d.flying/
+├── meshes/                     # source geometry, one file per object
+│   ├── gso/<label>.glb
+│   └── oo3d/<label>.glb
+└── templates/                  # precomputed reference views, one file per object
+    ├── gso/<label>.h5
+    └── oo3d/<label>.h5
+```
+
+Splits are named `<source>.<scene_type>`:
+
+- **source** — `gso` or `oo3d` (the object bank).
+- **scene_type** — `random` (objects dropped onto a plane) or `flying`
+  (hdri rendered objects suspended in free space).
+
+| split | shards | samples | size |
+|-------|-------:|--------:|-----:|
+| `gso.random`  | 1000 | ~1.0M | 299 GB |
+| `gso.flying`  |  500 | ~0.5M | 110 GB |
+| `oo3d.random` | 1000 | ~1.0M | 288 GB |
+| `oo3d.flying` |  490 | ~0.49M | 102 GB |
+
+| assets | gso | oo3d |
+|--------|----:|-----:|
+| meshes (`.glb`) | 1029 | 5672 |
+| templates (`.h5`) | 1029 | 5672 |
+
+Each shard holds 1000 samples (RGB frames). Sample keys are
+`{scene_id:06d}_{frame_id:06d}` and are globally unique within a split.
+Per sample, a shard contains:
+
+| file | content |
+|------|---------|
+| `<key>.rgb.jpg`        | RGB image, 640×480 |
+| `<key>.depth.png`      | 16-bit depth (see `depth_scale` in the camera file) |
+| `<key>.camera.json`    | `cam_K`, `cam_R_w2c`, `cam_t_w2c`, `depth_scale` |
+| `<key>.gt.json`        | per-instance `cam_R_m2c`, `cam_t_m2c` (mm), string `obj_id` |
+| `<key>.gt_info.json`   | per-instance `bbox_obj`, `bbox_visib`, `px_count_*`, `visib_fract` |
+| `<key>.mask.json`      | amodal masks (RLE) |
+| `<key>.mask_visib.json`| visible masks (RLE) |
+
+This is the BOP `train_pbr` schema, packed as WebDataset. Translations are in
+millimeters; `cam_K` is the 3×3 intrinsics in row-major order.
+
+> **Note — pixel-accurate visible masks.** `mask_visib` (and the derived `visib_fract`) are computed directly from Blender's native instance segmentation rather than BlenderProc's BopWriterUtility default depth-thresholding, which can assign a pixel to multiple objects and leaves artifacts where foreground depth changes sharply. This dataset was rendered with the fix from BlenderProc PR [#1229](https://github.com/DLR-RM/BlenderProc/pull/1229) (see issue [#1228](https://github.com/DLR-RM/BlenderProc/issues/1228)); the PR was not yet merged upstream at publication time.
+
+## Loading the shards
+
+Shards decode with the BOP toolkit's WebDataset helper. Iterate the tars with
+[`webdataset`](https://github.com/webdataset/webdataset) and decode each sample
+with `bop_toolkit_lib.dataset.bop_webdataset.decode_sample` (RGB is JPEG, so
+pass `rgb_suffix=".jpg"`):
+
+```python
+import glob
+import webdataset as wds
+from bop_toolkit_lib.dataset import bop_webdataset
+
+shards = sorted(glob.glob("shards/gso.random/shard-*.tar"))
+ds = wds.WebDataset(shards, shardshuffle=True)
+
+for sample in ds:
+    data = bop_webdataset.decode_sample(
+        sample,
+        decode_camera=True,
+        decode_rgb=True,
+        decode_depth=True,
+        decode_gt=True,
+        decode_gt_info=True,
+        decode_mask=True,
+        decode_mask_visib=True,
+        rgb_suffix=".jpg",
+    )
+    scene_id, view_id = str(sample["__key__"]).split("_")
+    rgb = data["im_rgb"]                 # (H, W, 3) uint8
+    labels = [ann["obj_id"] for ann in data["gt"]]          # string object names
+    poses = [(ann["cam_R_m2c"], ann["cam_t_m2c"]) for ann in data["gt"]]
+    visib = [i["visib_fract"] for i in data["gt_info"]]
+```
+
+## Loading the templates
+
+Each `templates/<source>/<label>.h5` holds a single `images` dataset: rows of
+**JPEG-encoded bytes**, one per reference view. Rows are ordered
+`[0, 42)` = 42 icosphere views (canonical viewpoints on a sphere) followed by
+`[42, 512)` = in-the-wild crops. Decode a row with PIL:
+
+```python
+import io
+import h5py
+import numpy as np
+from PIL import Image
+
+with h5py.File("templates/gso/11pro_SL_TRX_FG.h5", "r") as f:
+    images = f["images"]                # (N,) variable-length JPEG bytes
+    icosphere = [np.array(Image.open(io.BytesIO(images[i]))) for i in range(42)]
+    wild      = [np.array(Image.open(io.BytesIO(images[i]))) for i in range(42, images.shape[0])]
+```
+
+`h5py` does not support fancy indexing, so read with sorted integer indices or a
+contiguous slice.
+
+## Loading the meshes
+
+`meshes/<source>/<label>.glb` is the source geometry for object `<label>`, in
+glTF binary. Load with any glTF reader, e.g.:
+
+```python
+import trimesh
+mesh = trimesh.load("meshes/gso/11pro_SL_TRX_FG.glb")
+```
+
+Pose annotations (`cam_R_m2c`, `cam_t_m2c`) map this mesh's model coordinates
+into the camera frame; translations are in millimeters.
+
+## Citation
+```bibtex
+@inproceedings{ulmer2025conditional,
+  title={Conditional Latent Diffusion Models for Zero-Shot Instance Segmentation},
+  author={Ulmer, Maximilian and Boerdijk, Wout and Triebel, Rudolph and Durner, Maximilian},
+  booktitle={Proceedings of the IEEE/CVF International Conference on Computer Vision},
+  pages={24360--24369},
+  year={2025}
+}
+```
+
+## License
+This dataset is released under the [`CC BY 4.0`](https://creativecommons.org/licenses/by/4.0/). This subset bundles two upstream object banks, each retaining its own license:
+
+| source | upstream | license |
+|--------|----------|---------|
+| `gso`  | Google Scanned Objects | CC-BY-4.0 |
+| `oo3d` | OmniObject3D | CC BY 4.0 |
+
+By using this data you agree to the terms of the respective upstream datasets.

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