license: cc-by-nc-4.0
task_categories:
- question-answering
language:
- en
size_categories:
- 10M<n<100M
pretty_name: SpatialForge-10M
SpatialForge-10M
SpatialForge: Bootstrapping 3D-Aware Spatial Reasoning from Open-World 2D Images
📑 Paper
Zishan Liu, Ruoxi Zang, Yanglin Zhang, Wei Liu, Yin Zhang, Jian Yao, Jiayin Zheng, Zhengzhe Liu
Lingnan University · XPENG Robotics
📦 SpatialForge-10M
A large-scale vision-language dataset designed for 3D-aware spatial perception and reasoning from open-world 2D images.
SpatialForge-10M contains over 10 million QA pairs generated from 2.8 million curated real-world images, covering both low-level spatial perception and high-level spatial reasoning tasks. The dataset is constructed from diverse open-world image sources including Objects365, Pixmo and OpenImages, enabling broad visual diversity across indoor, outdoor, egocentric, and internet-scale scenes.
SpatialForge-10M is designed for:
- Spatial reasoning pretraining
- Multitask VLM supervision
- 3D-aware perception learning
- Grounding and referring research
- Camera-centric and human-centric reasoning
- Spatial instruction tuning
The dataset supports a unified QA format suitable for training modern multimodal large language models such as Qwen-VL, InternVL, LLaVA, and related architectures.
📌 Important Notice
This release contains the full SpatialForge-10M annotations, including all question-answer pairs and task splits. You will need to obtain the corresponding images directly from the source dataset.
Key Features
- ✅ 10M+ spatial QA pairs spanning perception and reasoning tasks
- ✅ 2.8M curated open-world images from diverse visual domains
- ✅ Covers both object-centric and human-centric spatial reasoning
- ✅ Includes grounding, referring, counting, depth reasoning, and perspective understanding
- ✅ Designed for scalable VLM pretraining and instruction tuning
- ✅ Bounding boxes are normalized to [0, 1000], following the format used in Qwen3-VL pretraining
🧠 Task Overview
SpatialForge-10M contains six major spatial tasks divided into two categories: Perception and Relation Reasoning.
| Level | Task | Description | Count |
|---|---|---|---|
| Perception | Grounding | Localize objects from textual descriptions → bbox prediction | 3.6M |
| Perception | Referring | Generate object descriptions from regions/bboxes | 3.6M |
| Perception | Counting | Count objects satisfying semantic conditions | 495k |
| Relation | Near-Far | Determine relative depth comapring between objects | 2.6M |
| Relation | Left-Right | Infer camera-centric horizontal spatial relations | 93k |
| Relation | Perspective | Human-centric viewpoint and perspective reasoning | 8k |
| Total | 10.2M |
🌍 Open-World Data Sources
SpatialForge-10M is bootstrapped from large-scale public image datasets:
- Objects365
- OpenImages
- Pixmo
These datasets provide rich scene diversity across:
- Indoor environments
- Outdoor scenes
- Human-object interactions
- Crowded object layouts
- Real-world internet imagery
Our pipeline automatically constructs spatial supervision signals from 2D images while preserving geometric consistency and viewpoint awareness.
@article{liu2026spatialforge,
title={SpatialForge: Bootstrapping 3D-Aware Spatial Reasoning from Open-World 2D Images},
author={Liu, Zishan and Zang, Ruoxi and Zhang, Yanglin and Liu, Wei and Zhang, Yin and Yao, Jian and Zheng, Jiayin and Liu, Zhengzhe},
journal={arXiv preprint arXiv:2605.11462},
year={2026}
}