BAD-Gaussians: Bundle Adjusted Deblur Gaussian Splatting
创新点：核心创新点与同名作者的论文《BAD-NeRF: Bundle Adjusted Deblur Neural Radiance Fields》相似。
In this paper, we introduce a novel approach, named BAD-Gaussians (Bundle Adjusted
Deblur Gaussian Splatting), which leverages explicit Gaussian representation and handles severe motion-blurred images with inaccurate camera poses to achieve high-quality scene reconstruction. Our method models the physical image formation process of motion-blurred images and jointly learns the parameters of Gaussians while recovering camera motion trajectories during exposure time. In our experiments, we demonstrate that BAD-Gaussians not only achieves superior rendering quality compared to previous state-of-the-art deblur neural rendering methods on both synthetic and real datasets but also enables real-time rendering capabilities.
Deblur-NeRF: Neural Radiance Fields from Blurry Images
https://zhuanlan.zhihu.com/p/506885359?utm_id=0
使用了一个稀疏的核，仿真一个像素接受很多rey的光子造成模糊的物理过程，二者联合优化，得到了更clear的NeRF。在推理阶段把前半部分去掉，使用一个正常的图像的ray作为输入即可得到更清晰的渲染。