quchenyuan/360x_dataset_HR

360+x Dataset

For more information, please feel free to check our project page.

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.

Download This Repo

You can use the following code to download the entire dataset:

from huggingface_hub import snapshot_download

repo_id = "quchenyuan/360x_dataset_HR"

snapshot_download(repo_id=repo_id, repo_type="dataset", token={your_token})

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 (5760x2880 for panoramic， 2432x1216 binocular videos, and 1920x1080 for third-person front view videos) available for download.

In this repo, we provide the high-resolution version of the dataset. To access the low-resolution version, please visit the official website.

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}
}