首个支持昼夜场景的头戴式3D视觉基准数据集，推动真实环境下的视觉重建研究。
Seeing in the Dark: Benchmarking Egocentric 3D Vision with the Oxford Day-and-Night Dataset
- 用Meta ARIA眼镜采集头戴视角视频，结合多会话SLAM重建3D点云与相机位姿
- 覆盖超30公里轨迹、4万平方米区域，包含白天到夜晚的极端光照变化
- 专为新视角合成与视觉重定位设计，适合做真实世界3D视觉模型评估
我们提出Oxford Day-and-Night数据集，一个大规模、头戴式视角的3D视觉基准数据集，用于在挑战性光照条件下进行新视角合成（NVS）和视觉重定位。现有数据集通常缺少关键组合：真值3D几何、广泛光照变化以及完整的6自由度运动。Oxford Day-and-Night通过Meta ARIA眼镜采集头戴视频，并采用多会话SLAM技术估计相机位姿、重建3D点云，对不同光照条件（包括白天与黑夜）下拍摄的序列进行对齐。数据集涵盖超过30公里的轨迹记录，覆盖面积达40,000平方米，为头戴式3D视觉研究提供丰富基础。该数据集支持两项核心基准任务：新视角合成与视觉重定位，为模型在真实多样环境中的评估提供独特平台。
原文摘要 · Abstract (English)
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.
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