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E-Sem3DGS:通过事件辅助的语义3DGS进行单眼人与场景重建

Xiaoting Yin¹, Hao Shi^1,2, Kailun Yang³

¹State Key Laboratory of Extreme Photonics and Instrumentation, National Engineering Research Center of Optical Instrumentation, Zhejiang University, Hangzhou 310027, China.

Sensors (Basel, Switzerland)

|January 10, 2026

概括

这项研究介绍了E-Sem3DGS,这是一种使用事件和强度摄像头重建3D人体和场景从动作模糊视频的新框架. 它显著提高了重建质量和细节,克服了以前方法的局限性.

科学领域:

计算机视觉计算机视觉
神经染是一种神经染.
三维重建的3D重建

背景情况:

从单眼,动作模糊视频中重建可动画的人类和场景是当前神经染技术面临的重大挑战.
现有的方法通常在运动模糊下失败,并提供不完整的场景建模,主要关注前景人类重建.
事件摄像头提供高时间分辨率和运动模糊强度,补充标准视频传感器.

研究的目的:

开发一个新的框架,E-Sem3DGS,从混合事件强度流中共同3D重建人类化身和静态场景.
解决现有方法在处理动作模糊和实现完整场景建模方面的局限性.
为了利用3D高斯标准中的语义属性,有效地分离动态和静态内容.

主要方法:

E-Sem3DGS采用了一个语义增强的3D高斯分片框架,利用混合事件和强度数据.
它使用皮肤多人线性 (SMPL) 模型前置和场景采样对人类和场景高斯初始化,分别优化几何,外观和语义.
来自事件的光流用于监督基于图像的光流,强制执行时间连贯性并减轻运动模糊.

主要成果:

E-Sem3DGS显著提高了运动模糊数据集的重建质量,在ZJU-MoCap-Blur (21.75至32.56) 上实现了49.7%的PSNR增加.
该方法提高了人形和背景场景重建的准确性和细节性.
在MMHPSD-Blur数据集上也表现出性能改善,PSNR增加了13.48% (25.23至28.63).

结论:

通过混合事件强度数据,E-Sem3DGS有效地从动作模糊视频中重建可动化的人类和静态场景.
3D高斯的语义增强和事件衍生的光流是克服运动模糊挑战的关键.
这一框架代表了从具有挑战性的真实世界视频数据的3D重建的重大进步.

关键词:

3D高斯斯普拉特 3D高斯斯斯普拉特 3D重建重建的3D重建基于事件的视觉视觉.

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