用事件相机实现大场景新视角合成，效果更优且速度更快。
E-3DGS: Event-Based Novel View Rendering of Large-Scale Scenes Using 3D Gaussian Splatting
- 首次将3D高斯泼溅用于事件相机的新视角合成
- 在真实与合成数据上均比基线模型提升11-25% PSNR
- 适合关注事件相机、大场景重建的研究者
新视角合成技术多依赖RGB相机，存在光照不足、运动模糊和动态范围受限等问题。事件相机虽更具鲁棒性，但在大场景下的应用研究较少，现有方法主要针对正面或360度物体级场景。本文首次引入3D高斯泼溅进行事件相机的新视角合成，可重建大规模无界场景并保持高视觉质量。我们构建了首个专为此任务设计的真实与合成事件数据集。实验表明，该方法在新视角合成上表现优异，相比基线模型EventNeRF的PSNR提升11-25 dB，且重建与渲染速度提升数个数量级。
原文摘要 · Abstract (English)
Novel view synthesis techniques predominantly utilize RGB cameras, inheriting their limitations such as the need for sufficient lighting, susceptibility to motion blur, and restricted dynamic range. In contrast, event cameras are significantly more resilient to these limitations but have been less explored in this domain, particularly in large-scale settings. Current methodologies primarily focus on front-facing or object-oriented (360-degree view) scenarios. For the first time, we introduce 3D Gaussians for event-based novel view synthesis. Our method reconstructs large and unbounded scenes with high visual quality. We contribute the first real and synthetic event datasets tailored for this setting. Our method demonstrates superior novel view synthesis and consistently outperforms the baseline EventNeRF by a margin of 11-25% in PSNR (dB) while being orders of magnitude faster in reconstruction and rendering.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。