EgoXtreme Dataset

A Dataset for Robust Object Pose Estimation in Egocentric Views
under Extreme Conditions
Taegyoon Yoon1 Yegyu Han1 Seojin Ji1 Jaewoo Park1 Sojeong Kim1 Taein Kwon2 Hyung-Sin Kim1
1Seoul National University    2VGG, University of Oxford
CVPR 2026
Paper Dataset (Train/Val) Dataset (Test) Code

Dataset Overview

Teaser Scene

EgoXtreme is a novel, large-scale dataset designed for robust egocentric 6D object pose estimation under extreme conditions. Specifically, 8 illumination conditions are used across three scenarios, and smoke is included in specific scenes. These conditions, combined with severe motion blur, make accurate 6D object pose estimation extremely challenging.

Dataset Download

To download the train and validation dataset, please download the data here.

To download the test dataset (without GT), please download here.


For detailed information of the data format and structure, please check our GitHub repository.

Citation

@inproceedings{egoxtreme2026,
  title={EgoXtreme: A Dataset for Robust Object Pose Estimation in Egocentric Views under Extreme Conditions},
  author={Yoon, Taegyoon and Han, Yegyu and Ji, Seojin and Park, Jaewoo and Kim, Sojeong and Kwon, Taein and Kim, Hyung-Sin},
  booktitle={Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
  year={2026}
}