metadata

license: apache-2.0
task_categories:
  - visual-question-answering
language:
  - en
tags:
  - spatial-reasoning
  - multimodal
pretty_name: Spatial457
size_categories:
  - 10K<n<100K

Spatial457: A Diagnostic Benchmark for 6D Spatial Reasoning of Large Multimodal Models

Xingrui Wang¹, Wufei Ma¹, Tiezheng Zhang¹, Celso M. de Melo², Jieneng Chen¹, Alan Yuille¹

¹ Johns Hopkins University ² DEVCOM Army Research Laboratory

🌐 Project Page • 📄 Paper • 🤗 Dataset • 💻 Code

Spatial457 Teaser

🧠 Introduction

Spatial457 is a diagnostic benchmark designed to evaluate 6D spatial reasoning in large multimodal models (LMMs). It systematically introduces four core spatial capabilities:

🧱 Multi-object understanding
🧭 2D spatial localization
📦 3D spatial localization
🔄 3D orientation estimation

These are assessed across five difficulty levels and seven diverse question types, ranging from simple object queries to complex reasoning about physical interactions.

📂 Dataset Structure

The dataset is organized as follows:

Spatial457/
├── images/                     # RGB images used in VQA tasks
├── questions/                  # JSONs for each subtask
│   ├── L1_single.json
│   ├── L2_objects.json
│   ├── L3_2d_spatial.json
│   ├── L4_occ.json
│   └── ...
├── Spatial457.py               # Hugging Face dataset loader script
├── README.md                   # Documentation

Each JSON file contains a list of VQA examples, where each item includes:

"image_filename": image file name used in the question
"question": natural language question
"answer": boolean, string, or number
"program": symbolic program (optional)
"question_index": unique identifier

This modular structure supports scalable multi-task evaluation across levels and reasoning types.

🛠️ Dataset Usage

You can load the dataset directly using the Hugging Face 🤗 datasets library:

🔹 Load a specific subtask (e.g., L1_single)

from datasets import load_dataset

dataset = load_dataset("RyanWW/Spatial457", name="L5_6d_spatial", split="validation", data_dir=".")

Each example is a dictionary like:

{
  'image': <PIL.Image.Image>,
  'image_filename': 'superCLEVR_new_000001.png',
  'question': 'Is the large red object in front of the yellow car?',
  'answer': 'True',
  'program': [...],
  'question_index': 100001
}

🔹 Other available configurations

[
  "L1_single", "L2_objects", "L3_2d_spatial",
  "L4_occ", "L4_pose", "L5_6d_spatial", "L5_collision"
]

You can swap name="..." in load_dataset(...) to evaluate different spatial reasoning capabilities.

📚 Citation

@inproceedings{wang2025spatial457,
  title     = {Spatial457: A Diagnostic Benchmark for 6D Spatial Reasoning of Large Multimodal Models},
  author    = {Wang, Xingrui and Ma, Wufei and Zhang, Tiezheng and de Melo, Celso M and Chen, Jieneng and Yuille, Alan},
  booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
  year      = {2025},
  url       = {https://arxiv.org/abs/2502.08636}
}