用3D高斯点云生成逼真事件数据，解决合成数据多样性不足问题
GS2E: Gaussian Splatting is an Effective Data Generator for Event Stream Generation
- 先用3D高斯点云重建真实场景，再模拟事件流
- 生成的事件数据在多视角、光照下保持几何一致性和时间稠密性
- 适合做事件视觉任务的基准测试，尤其3D重建场景
我们提出GS2E（Gaussian Splatting to Event），一个大规模合成事件数据集，用于高保真事件视觉任务，其数据源自真实世界稀疏多视角RGB图像。现有事件数据集通常由密集RGB视频合成，缺乏视角多样性和几何一致性，或依赖昂贵难扩展的硬件。GS2E通过先用3D高斯点云重建逼真静态场景，再采用新型物理启发式事件模拟流程克服上述限制。该流程结合自适应轨迹插值与物理一致的事件对比度阈值建模，能在多种运动和光照条件下生成时间稠密且几何一致的事件流，并确保与场景结构强对齐。在基于事件的3D重建实验中，GS2E展现出优异泛化能力，具备推动事件视觉研究的实际价值。
原文摘要 · Abstract (English)
We introduce GS2E (Gaussian Splatting to Event), a large-scale synthetic event dataset for high-fidelity event vision tasks, captured from real-world sparse multi-view RGB images. Existing event datasets are often synthesized from dense RGB videos, which typically lack viewpoint diversity and geometric consistency, or depend on expensive, difficult-to-scale hardware setups. GS2E overcomes these limitations by first reconstructing photorealistic static scenes using 3D Gaussian Splatting, and subsequently employing a novel, physically-informed event simulation pipeline. This pipeline generally integrates adaptive trajectory interpolation with physically-consistent event contrast threshold modeling. Such an approach yields temporally dense and geometrically consistent event streams under diverse motion and lighting conditions, while ensuring strong alignment with underlying scene structures. Experimental results on event-based 3D reconstruction demonstrate GS2E's superior generalization capabilities and its practical value as a benchmark for advancing event vision research.
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