首个多视角第一人称动态场景重建数据集，助力沉浸式社交记录。
MultiEgo: A Multi-View Egocentric Video Dataset for 4D Scene Reconstruction
- 用AR眼镜采集五人同步第一人称视频，实现毫秒级时间对齐。
- 涵盖会议、演出等5类真实社交场景，支持自由视角视频生成。
- 适合研究第一人称视觉、4D场景重建与虚拟现实的学者使用。
多视角第一人称动态场景重建在社交互动全息记录中具有重要价值，但现有数据集多聚焦静态多视角或单第一人称视角，缺乏用于动态场景重建的多视角第一人称数据集。为此，我们提出MultiEgo，首个面向4D动态场景重建的多视角第一人称数据集。数据集包含5个典型社交场景：会议、表演和演示。每个场景由佩戴AR眼镜的参与者拍摄5段真实的第一人称视频。我们设计了基于硬件的数据采集系统与处理流程，实现了跨视角亚毫秒级时间同步，并提供精确的姿态标注。实验验证表明，该数据集在自由视角视频（FVV）应用中具备实际效用与有效性，为多视角第一人称动态场景重建研究提供了基础资源。
原文摘要 · Abstract (English)
Multi-view egocentric dynamic scene reconstruction holds significant research value for applications in holographic documentation of social interactions. However, existing reconstruction datasets focus on static multi-view or single-egocentric view setups, lacking multi-view egocentric datasets for dynamic scene reconstruction. Therefore, we present MultiEgo, the first multi-view egocentric dataset for 4D dynamic scene reconstruction. The dataset comprises five canonical social interaction scenes: meetings, performances, and a presentation. Each scene provides five authentic egocentric videos captured by participants wearing AR glasses. We design a hardware-based data acquisition system and processing pipeline, achieving sub-millisecond temporal synchronization across views, coupled with accurate pose annotations. Experiment validation demonstrates the practical utility and effectiveness of our dataset for free-viewpoint video (FVV) applications, establishing MultiEgo as a foundational resource for advancing multi-view egocentric dynamic scene reconstruction research.
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