rayruiyang/GeoUG_data

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 · GeoUG-ColdStart · GeoUG-Spatial · Download parquet

Sources and construction

The release combines spatial questions derived from the training portions of ScanNet++ and 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

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:

python scripts/pack_data_parquet.py unpack-rl \
  --input /absolute/path/to/train_geoug_rl.parquet \
  --output /absolute/path/to/rl_train

This creates:

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

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 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.