| --- |
| 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. |
| |
