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DEGS:来自RGB和事件流的可变形基于事件的3D高斯分裂
IEEE transactions on visualization and computer graphics
|October 10, 2025
概括
本研究引入了一种新的框架,用于使用低率RGB视频和事件流来重建动态3D高斯分片 (3DGS). 通过利用事件运动先验,该方法有效指导动态3DGS重建的优化.
科学领域:
- 计算机视觉 计算机视觉
- 三维重建的3D重建
- 机器人技术 机器人技术 机器人技术
背景情况:
- 从低率视频中重建动态3D场景是很困难的,因为动作不确定性.
- 事件摄像头提供高速,运动模糊强大的数据,但缺乏色彩信息.
- 为3DGS集成RGB和事件数据是具有挑战性的,因为模式差异.
研究的目的:
- 从RGB视频和事件流中共同优化动态3D高斯分片 (3DGS) 的新型框架的开发.
- 利用事件运动先验来指导动态3DGS中变形场的优化.
- 克服整合不同RGB和事件数据模式的挑战.
主要方法:
- 从事件流中提取动作先验,使用LoCM无监督微调进行场景调整.
- 开发一个对事件-高斯运动对应的几何感知数据关联方法.
- 使用运动分解和框架间伪标签策略.
主要成果:
- 拟议的框架成功地通过联合优化RGB和事件数据来重建动态3DGS.
- 事件运动先验显著提高了动态3DGS的准确性和稳定性.
- 该方法在合成和现实数据上优于现有的仅基于图像和基于事件的方法.
结论:
- 将低率RGB视频与事件流相结合,为动态3DGS提供了强大的解决方案.
- 新的框架有效地利用事件数据来增强3DGS重建,特别是在动态场景中.
- 这种方法推进了动态3D场景重建的最新技术.
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