Datasets:
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
num_examples: 501
download_size: 106490728
dataset_size: 246230145
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 Omni3D-Bench contains 500 challenging (image, question, answer) tuples of diverse, real-world scenes sourced from Omni3D for complex 3D spatial reasoning.
View samples from the dataset here.
The dataset is released under the Creative Commons Non-Commercial 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.
@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}
}