---
language:
- en
license: cc-by-nc-4.0
size_categories:
- 1K<n<10K
task_categories:
- visual-question-answering
dataset_info:
  features:
  - name: image_index
    dtype: string
  - name: image
    dtype: image
  - name: q_index
    dtype: int64
  - name: question
    dtype: string
  - name: answer
    dtype: string
  - name: answer_type
    dtype: string
  splits:
  - name: train
    num_bytes: 246230145.0
    num_examples: 501
  download_size: 106490728
  dataset_size: 246230145.0
configs:
- config_name: default
  data_files:
  - split: train
    path: data/train-*
---

# Omni3D-Bench
This repository contains the Omni3D-Bench dataset introduced in the paper [Visual Agentic AI for Spatial Reasoning with a Dynamic API](https://huggingface.co/papers/2502.06787) Omni3D-Bench contains 500 challenging (image, question, answer) tuples of diverse, real-world scenes sourced from [Omni3D](https://github.com/facebookresearch/omni3d) for complex 3D spatial reasoning.

View samples from the dataset [here](https://glab-caltech.github.io/vadar/omni3d-bench.html).

The dataset is released under the [Creative Commons Non-Commercial](https://creativecommons.org/licenses/by-nc/4.0/deed.en) license. 

## Usage

The benchmark can be accessed with the following code:

```
from datasets import load_dataset
dataset = load_dataset("dmarsili/Omni3D-Bench")
```

We additionally provide a `.zip` file including all the images and annotations.

## Annotations

Samples in Omni3D-Bench consist of images, questions, and ground-truth answers. Samples can be loaded as python dictonaries in the following format:

```
<!-- annotations.json -->
{
    "questions": [
        {
            "image_index"               : str, image ID
            "question_index"            : str, question ID
            "image"                     : PIL Image, image for query
            "question"                  : str, query
            "answer_type"               : str, expected answer type - {int, float, str}
            "answer"                    : str|int|float, ground truth response to the query
        },
        {
            ...
        },
        ...
    ]
}
```

## Citation

If you use the Omni3D-Bench dataset in your research, please use the following BibTeX entry.
```bibtex
@inproceedings{marsili2025visual,
  title={Visual agentic ai for spatial reasoning with a dynamic api},
  author={Marsili, Damiano and Agrawal, Rohun and Yue, Yisong and Gkioxari, Georgia},
  booktitle={Proceedings of the Computer Vision and Pattern Recognition Conference},
  pages={19446--19455},
  year={2025}
}
```