用事件相机实现无需运动重建的3D高斯点云去模糊
DeblurSplat: SfM-free 3D Gaussian Splatting with Event Camera for Robust Deblurring
- 跳过传统运动重建，直接用立体模型生成初始点云
- 融合事件流与模糊图像，提升场景重建精度和渲染效率
- 适合动态场景去模糊与高效3D重建任务
本文提出首个无需结构光重建（SfM）的3D高斯点云去模糊方法DeblurSplat，利用事件相机实现鲁棒去模糊。首先，借助预训练的稠密立体模块DUSt3R，直接从模糊图像生成准确的初始点云，避免了相机位姿误差传播至点云位置的问题。其次，引入事件流以捕捉动态变化，通过解码事件流与模糊图像中的潜在清晰图像，为场景重建提供细粒度监督信号。大量实验表明，DeblurSplat在多种场景下不仅生成高质量新视角，且相比现有最优方法在渲染效率上显著提升。
原文摘要 · Abstract (English)
In this paper, we propose the first Structure-from-Motion (SfM)-free deblurring 3D Gaussian Splatting method via event camera, dubbed DeblurSplat. We address the motion-deblurring problem in two ways. First, we leverage the pretrained capability of the dense stereo module (DUSt3R) to directly obtain accurate initial point clouds from blurred images. Without calculating camera poses as an intermediate result, we avoid the cumulative errors transfer from inaccurate camera poses to the initial point clouds' positions. Second, we introduce the event stream into the deblur pipeline for its high sensitivity to dynamic change. By decoding the latent sharp images from the event stream and blurred images, we can provide a fine-grained supervision signal for scene reconstruction optimization. Extensive experiments across a range of scenes demonstrate that DeblurSplat not only excels in generating high-fidelity novel views but also achieves significant rendering efficiency compared to the SOTAs in deblur 3D-GS.
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