Datasets:
suvadityamuk HF Staff
docs: document 16 excluded non-Google models (OpenRobotics/LeDSantos)
b944bf0 verified | license: cc-by-4.0 | |
| pretty_name: Google Scanned Objects (WebDataset) | |
| tags: | |
| - 3d | |
| - mesh | |
| - glb | |
| - webdataset | |
| - google-scanned-objects | |
| - trellis | |
| task_categories: | |
| - image-to-3d | |
| - text-to-3d | |
| size_categories: | |
| - 1K<n<10K | |
| configs: | |
| - config_name: default | |
| data_files: | |
| - split: train | |
| path: data/*.tar | |
| # Google Scanned Objects (WebDataset) | |
| A `load_dataset`-ready [WebDataset](https://github.com/webdataset/webdataset) | |
| packaging of the **Google Scanned Objects** dataset: high-quality 3D scans of common | |
| household items. Each object provides the original mesh/material/texture, five | |
| render thumbnails, structured metadata, and a **normalized GLB** produced with | |
| [`trimesh`](https://github.com/mikedh/trimesh) for direct use in 3D pipelines | |
| (e.g. TRELLIS-2 fine-tuning). | |
| - Objects: **1030** | |
| - Shards: **43** (`data/gso-train-*.tar`) | |
| - License: **Creative Commons Attribution 4.0 International** (cc-by-4.0) | |
| ## Usage | |
| ```python | |
| from datasets import load_dataset | |
| ds = load_dataset("suvadityamuk/google-scanned-objects", split="train") | |
| sample = ds[0] | |
| print(sample["json"]["name"], sample["json"]["category"]) | |
| sample["texture.png"] # PIL.Image (diffuse texture) | |
| sample["thumbnail_0.jpg"] # PIL.Image (render) | |
| glb_bytes = sample["glb"] # bytes of a self-contained, normalized .glb | |
| ``` | |
| Streaming works too (no full download): | |
| ```python | |
| ds = load_dataset("suvadityamuk/google-scanned-objects", split="train", streaming=True) | |
| sample = next(iter(ds)) | |
| ``` | |
| > GLB is a recognized mesh extension in the `datasets` WebDataset builder, so | |
| > `glb` is loaded as a `Mesh` feature and rendered in the dataset viewer. | |
| > A recent `datasets` version is required for the `Mesh` feature. | |
| ## Per-sample fields | |
| | field | type | description | | |
| |-------|------|-------------| | |
| | `obj` | bytes | original `model.obj` mesh | | |
| | `mtl` | bytes | original `model.mtl` material | | |
| | `texture.png` | Image | diffuse texture | | |
| | `glb` | Mesh | normalized, self-contained GLB (see below) | | |
| | `thumbnail_0.jpg` .. `thumbnail_4.jpg` | Image | 5 render thumbnails | | |
| | `json` | dict | metadata + `glb_processing` stats | | |
| The `json` field contains: `object_id`, `name`, `description`, `version`, | |
| `category`, `annotations` (e.g. `brand`, `gtin`, `sku`), `author`, `license`, | |
| `license_id`, `license_url`, `copyright`, `source`, `num_thumbnails_original`, | |
| and `glb_processing`. | |
| ## GLB normalization (trimesh) | |
| Produced deterministically per object with `trimesh==4.12.2`: | |
| 1. Copy `materials/textures/texture.png` next to the OBJ so the MTL's | |
| `map_Kd texture.png` resolves and the diffuse map binds. | |
| 2. `trimesh.load(obj, process=False, force="mesh")` — preserves UVs, flattens | |
| to a single mesh. | |
| 3. Translate the axis-aligned bounding-box center to the origin. | |
| 4. Uniformly scale so the longest AABB edge is `1.0` (fits a unit cube). | |
| 5. Preserve UVs (no vertex merge); recompute normals only if missing. | |
| 6. Export a self-contained `.glb` with the texture embedded. | |
| Every object's exact transform (`applied_translation`, `applied_scale`), | |
| mesh stats (`vertices`, `faces`, `is_watertight`, `surface_area_normalized`, | |
| `orig_extents`, `final_extents`, `texture_size`, `glb_size_bytes`) and any | |
| `warnings` are recorded in `json["glb_processing"]` and aggregated in | |
| [`stats/processing_log.jsonl`](./stats/processing_log.jsonl). | |
| ## Excluded objects | |
| This dataset contains only the **1,030 objects authored by Google** (Fuel owner | |
| `GoogleResearch`) in the "Scanned Objects by Google Research" collection. That | |
| Fuel collection's listing also includes 16 models owned by other authors; they | |
| are **intentionally excluded** because they are not Google scans, use a | |
| different file structure (`.dae`/`.obj`, optional textures, 2-6 thumbnails), and | |
| carry different licenses (CC-BY-4.0 and CC0-1.0): | |
| - `OpenRobotics` (13): Bed, Cardboard box, Door handle, Fridge, Hinged door, | |
| KitchenCountertop, LampAndStand, Plastic Cup, Shower, SquareShelf, | |
| Standard Toilet, Vanity, WhiteCabinet | |
| - `LeDSantos` (3): ada_lovelace_poster, garage_door, toilet_paper | |
| These appear as `status: "failed"` rows in | |
| [`stats/processing_log.jsonl`](./stats/processing_log.jsonl) (the raw downloader | |
| requested them under the wrong owner, yielding invalid archives). | |
| ## License & attribution | |
| This dataset is a repackaging of the Google Scanned Objects dataset by Google LLC, | |
| licensed under [Creative Commons Attribution 4.0 International](https://creativecommons.org/licenses/by/4.0/) (`cc-by-4.0`). | |
| > "Google Scanned Objects", Copyright 2020 Google LLC, licensed under CC BY 4.0. | |
| You must give appropriate credit when using this data. See the `LICENSE` file | |
| and each object's `json["copyright"]` / `json["license"]` fields. | |