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.
虽然神经渲染在3D场景重建和新视角合成方面展示了令人印象深刻的能力，但它严重依赖于高质量清晰图像和准确的相机姿态。许多方法已被提出来用运动模糊图像训练神经辐射场（NeRF），这是在实际场景中常遇到的情况，比如低光照或长时间曝光条件。然而，NeRF的隐式表示难以从严重运动模糊的图像中准确恢复出复杂的细节，且无法实现实时渲染。相比之下，最近在3D高斯喷溅方面的进展通过显式优化点云为高斯球，实现了高质量的3D场景重建和实时渲染。 在本文中，我们介绍了一种新颖的方法，名为BAD-Gaussians（Bundle Adjusted Deblur Gaussian Splatting），它利用显式高斯表示，并能处理严重运动模糊图像及不准确的相机姿态，以实现高质量的场景重建。我们的方法模拟了运动模糊图像的物理成像过程，并在曝光时间内共同学习高斯参数，同时恢复相机运动轨迹。 在我们的实验中，我们展示了BAD-Gaussians不仅在合成和真实数据集上相比之前的最先进去模糊神经渲染方法实现了更优越的渲染质量，而且还启用了实时渲染能力。