  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/
