| --- |
| license: other |
| license_name: hku-academic-non-commercial |
| license_link: https://huggingface.co/datasets/rayruiyang/GeoUG_data#publisher-permitted-use-and-contact |
| language: |
| - en |
| task_categories: |
| - visual-question-answering |
| size_categories: |
| - n<1K |
| pretty_name: GeoUG Spatial RL Data |
| tags: |
| - geoug |
| - spatial-reasoning |
| - reinforcement-learning |
| - metric-depth |
| configs: |
| - config_name: default |
| data_files: |
| - split: train |
| path: train_geoug_rl.parquet |
| --- |
| |
| # GeoUG Spatial RL Data |
|
|
| **Spatial-policy training data with embedded images, camera parameters, and generated-depth caches.** |
|
|
| This repository hosts the 800-sample training release for GeoUG spatial-policy |
| reinforcement learning. All required per-sample training assets are stored in |
| one parquet: `train_geoug_rl.parquet` (2,019,132,360 bytes; 1.88 GiB). |
|
|
| [Project page](https://yangr116.github.io/geoug-project-page/) · |
| [GeoUG-ColdStart](https://huggingface.co/rayruiyang/GeoUG-ColdStart) · |
| [GeoUG-Spatial](https://huggingface.co/rayruiyang/GeoUG-Spatial) · |
| [Download parquet](https://huggingface.co/datasets/rayruiyang/GeoUG_data/resolve/main/train_geoug_rl.parquet) |
|
|
| ## Sources and construction |
|
|
| The release combines spatial questions derived from the training portions of |
| [ScanNet++](https://scannetpp.mlsg.cit.tum.de/scannetpp/) and |
| [nuScenes](https://www.nuscenes.org/nuscenes). Source identifiers are retained |
| in each row for provenance. Original images and source scene annotations belong |
| to their respective dataset creators. GeoUG adds the spatial-policy training |
| examples and packages their camera information and generated-depth caches. |
|
|
| | `source` | Samples | |
| |---|---:| |
| | `scannetpp_train` | 400 | |
| | `nuscenes_train` | 400 | |
|
|
| | Preserved task category | Samples | |
| |---|---:| |
| | `multi_object_closest_among` | 200 | |
| | `camera_closest_among` | 200 | |
| | `multi_object_closer_to` | 200 | |
| | `location_closer_to_camera` | 200 | |
|
|
| Category names are grouping identifiers; use the actual question text to |
| interpret closer/farther wording. Answers may be A, B, C, or D depending on |
| the options. The generated-depth arrays are model-generated training caches, |
| not ground-truth depth labels from the source datasets. |
|
|
| ## Download and inspect a sample |
|
|
| ```python |
| import io |
| import json |
| import numpy as np |
| import pyarrow.parquet as pq |
| from PIL import Image |
| from huggingface_hub import hf_hub_download |
| |
| path = hf_hub_download( |
| repo_id="rayruiyang/GeoUG_data", |
| repo_type="dataset", |
| filename="train_geoug_rl.parquet", |
| ) |
| row = next(pq.ParquetFile(path).iter_batches(batch_size=1)).to_pylist()[0] |
| image = Image.open(io.BytesIO(row["image"])).convert("RGB") |
| depth = np.load(io.BytesIO(row["depth_npy"]), allow_pickle=False) |
| camera = row["camera"] |
| options = json.loads(row["options"]) |
| print(row["question"], row["answer"], depth.shape) |
| ``` |
|
|
| ## Schema |
|
|
| | Field | Meaning | |
| |---|---| |
| | `id`, `image_key` | Question and image identifiers | |
| | `image` | Encoded image bytes | |
| | `question`, `answer`, `options` | Question, target answer, and JSON-encoded option mapping | |
| | `category`, `source` | Task grouping and source provenance | |
| | `illusion`, `illusion_strength` | Construction-time shortcut/illusion diagnostics | |
| | `camera.intrinsics` | 3 × 3 camera intrinsic matrix | |
| | `camera.image_wh` | Width and height associated with the camera calibration | |
| | `depth_npy` | Original NumPy depth-cache file bytes, embedded without precision conversion | |
|
|
| Camera calibration dimensions may differ from the encoded image or depth-map |
| resolution. Use the calibration dimensions when rescaling intrinsics, as done |
| by the GeoUG geometry tools. Do not assume that all three resolutions match. |
|
|
| ## Use for RL training |
|
|
| From the GeoUG code checkout, restore the existing training layout: |
|
|
| ```bash |
| python scripts/pack_data_parquet.py unpack-rl \ |
| --input /absolute/path/to/train_geoug_rl.parquet \ |
| --output /absolute/path/to/rl_train |
| ``` |
|
|
| This creates: |
|
|
| ```text |
| rl_train/ |
| ├── train.parquet |
| ├── camera_table.json |
| └── depth-cache/ |
| ``` |
|
|
| Set `RL_DATASET=/absolute/path/to/rl_train` and |
| `DEPTH_CACHE_DIR=/absolute/path/to/rl_train/depth-cache`, then follow |
| `docs/RL_TRAINING.md`. Use GeoUG-ColdStart as the initialization checkpoint. |
| No separate camera-table or depth-cache download is required. |
|
|
| This release contains **training samples only**. The internal RL evaluation |
| subset, geometric co-training / VLM manifests, and cold start training data |
| are not included. Prepare validation/evaluation inputs separately. |
|
|
| ## File integrity |
|
|
| ```text |
| SHA256: 425c12e2eff223135cc3c159e70ebd2318b4e888f58dc7f4dfd243fc67673476 |
| ``` |
|
|
| ## Publisher, permitted use, and contact |
|
|
| The following notice applies to this GeoUG data release: |
|
|
| It is published by The University of Hong Kong (HKU) and is licensed for non-commercial use only. Its use is strictly limited to academic research and educational purposes. Please contact [**rayyang0116@connect.hku.hk**](mailto:rayyang0116@connect.hku.hk) if you have any concerns regarding the data. |
|
|
| The original datasets are credited in the sources section. This release does |
| not transfer ownership of upstream images or annotations; their original |
| licenses, attribution requirements, and terms of use remain applicable. |
|
|
