While neural rendering has demonstrated impressive capabilities in 3D scene reconstruction and novel view synthesis, it heavily relies on high-quality sharp images and accurate camera poses. Numerous approaches have been proposed to train Neural Radiance Fields (NeRF) with motion-blurred images, commonly encountered in real-world scenarios such as low-light or long-exposure conditions. However, the implicit representation of NeRF struggles to accurately recover intricate details from severely motion-blurred images and cannot achieve real-time rendering. In contrast, recent advancements in 3D Gaussian Splatting achieve high-quality 3D scene reconstruction and real-time rendering by explicitly optimizing point clouds as Gaussian spheres. 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. Our project page and source code is available at https://lingzhezhao.github.io/BAD-Gaussians/

翻译：尽管神经渲染在三维场景重建和新视角合成方面展现出了令人印象深刻的能力，但其严重依赖于高质量的清晰图像和准确的相机位姿。已有多种方法被提出，用于训练含运动模糊图像的神经辐射场（NeRF），这种图像在现实场景中（如低光照或长曝光条件下）普遍存在。然而，NeRF的隐式表示难以从严重运动模糊的图像中精确恢复复杂细节，并且无法实现实时渲染。相比之下，三维高斯泼溅的最新进展通过将点云显式优化为高斯球，实现了高质量的三维场景重建和实时渲染。在本文中，我们提出了一种名为BAD-Gaussians（光束法平差去模糊高斯泼溅）的新方法，该方法利用显式高斯表示，处理具有不准确相机位姿的严重运动模糊图像，以实现高质量的场景重建。我们的方法对运动模糊图像的物理成像过程进行建模，并在联合学习高斯参数的同时，恢复曝光时间内的相机运动轨迹。在实验中，我们证明BAD-Gaussians不仅在合成数据集和真实数据集上均取得了优于先前最先进去模糊神经渲染方法的渲染质量，还实现了实时渲染能力。我们的项目页面和源代码可在 https://lingzhezhao.github.io/BAD-Gaussians/ 获取。