Seeing in the Dark: Benchmarking Egocentric 3D Vision with the Oxford Day-and-Night Dataset
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arXiv
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| Format: | Preprint |
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2025
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| _version_ | 1866915325312761856 |
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| author | Wang, Zirui Bian, Wenjing Li, Xinghui Tao, Yifu Wang, Jianeng Fallon, Maurice Prisacariu, Victor Adrian |
| author_facet | Wang, Zirui Bian, Wenjing Li, Xinghui Tao, Yifu Wang, Jianeng Fallon, Maurice Prisacariu, Victor Adrian |
| contents | We introduce Oxford Day-and-Night, a large-scale, egocentric dataset for novel view synthesis (NVS) and visual relocalisation under challenging lighting conditions. Existing datasets often lack crucial combinations of features such as ground-truth 3D geometry, wide-ranging lighting variation, and full 6DoF motion. Oxford Day-and-Night addresses these gaps by leveraging Meta ARIA glasses to capture egocentric video and applying multi-session SLAM to estimate camera poses, reconstruct 3D point clouds, and align sequences captured under varying lighting conditions, including both day and night. The dataset spans over 30 $\mathrm{km}$ of recorded trajectories and covers an area of 40,000 $\mathrm{m}^2$, offering a rich foundation for egocentric 3D vision research. It supports two core benchmarks, NVS and relocalisation, providing a unique platform for evaluating models in realistic and diverse environments. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2506_04224 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Seeing in the Dark: Benchmarking Egocentric 3D Vision with the Oxford Day-and-Night Dataset Wang, Zirui Bian, Wenjing Li, Xinghui Tao, Yifu Wang, Jianeng Fallon, Maurice Prisacariu, Victor Adrian Computer Vision and Pattern Recognition We introduce Oxford Day-and-Night, a large-scale, egocentric dataset for novel view synthesis (NVS) and visual relocalisation under challenging lighting conditions. Existing datasets often lack crucial combinations of features such as ground-truth 3D geometry, wide-ranging lighting variation, and full 6DoF motion. Oxford Day-and-Night addresses these gaps by leveraging Meta ARIA glasses to capture egocentric video and applying multi-session SLAM to estimate camera poses, reconstruct 3D point clouds, and align sequences captured under varying lighting conditions, including both day and night. The dataset spans over 30 $\mathrm{km}$ of recorded trajectories and covers an area of 40,000 $\mathrm{m}^2$, offering a rich foundation for egocentric 3D vision research. It supports two core benchmarks, NVS and relocalisation, providing a unique platform for evaluating models in realistic and diverse environments. |
| title | Seeing in the Dark: Benchmarking Egocentric 3D Vision with the Oxford Day-and-Night Dataset |
| topic | Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2506.04224 |