---
license: cc-by-nc-sa-4.0
---

# <i>360+x</i> Dataset

For more information, please feel free to check our [project page](https://x360dataset.github.io/).

## Overview

360+x dataset introduces a unique panoptic perspective to scene understanding, differentiating itself from traditional
datasets by offering multiple viewpoints and modalities, captured from a variety of scenes

### Key Features:

- **Multi-viewpoint Captures:** Includes 360° panoramic video, third-person front view video, egocentric monocular
  video, and egocentric binocular video.
- **Rich Audio Modalities:** Features normal audio and directional binaural delay.
- **2,152 multi-model videos**  captured by 360 cameras and Spectacles camera (8579k frames in total) Captured in 17
  cities across 5 countries, covering 28 scenes ranging from Artistic Spaces to Natural Landscapes.
- **Action Temporal Segmentation:** Provides labels for 38 action instances for each video pair.

## About This Repo

This repository stores the pretrained models of the 360+x dataset. For more code information, please check our [official
code repository](https://github.com/x360dataset/x360dataset-kit).

## Dataset Details

### Project Description

- **Developed by:** Hao Chen, Yuqi Hou, Chenyuan Qu, Irene Testini, Xiaohan Hong, Jianbo Jiao
- **Funded by:** the Ramsay Research Fund, and the Royal Society Short Industry Fellowship
- **License:** Creative Commons Attribution-NonCommercial-ShareAlike 4.0

### Sources
- **Repository:** Coming Soon
- **Paper:** https://arxiv.org/abs/2404.00989

## Dataset Statistics

- **Total Videos:** 2,152, split between 464 videos captured using 360 cameras and 1,688 with Spectacles cameras.
- **Scenes:** 15 indoor and 13 outdoor, totaling 28 scene categories.
- **Short Clips:** The videos have been segmented into 1,380 shorter clips, each approximately 10 seconds long, totaling
  around 67.78 hours.
- **Frames:** 8,579k frames across all clips.

## Dataset Structure

Our dataset offers a comprehensive collection of panoramic videos, binocular videos, and third-person videos, each pair
of videos accompanied by annotations. Additionally, it includes features extracted using I3D, VGGish, and ResNet-18.
Given the high-resolution nature of our dataset (5760x2880 for panoramic and binocular videos, 1920x1080 for
third-person front view videos), the overall size is considerably large. To accommodate diverse research needs and
computational resources, we also provide a lower-resolution version of the dataset (640x320 for panoramic and binocular
videos, 569x320 for third-person front view videos) available for download.

<b>In this repo, we provide the lower-resolution version of the dataset. To access the high-resolution version, please
visit the <a href="https://x360dataset.github.io/">official website</a>.</b>




## BibTeX
```
@inproceedings{chen2024x360,
  title={360+x: A Panoptic Multi-modal Scene Understanding Dataset},
  author={Chen, Hao and Hou, Yuqi and Qu, Chenyuan and Testini, Irene and Hong, Xiaohan and Jiao, Jianbo},
  booktitle={Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition},
  year={2024}
}
```