BAD-Gaussians: Bundle Adjusted Deblur Gaussian Splatting
<|reference_start|>BAD-Gaussians: Bundle Adjusted Deblur Gaussian Splatting: 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/<|reference_end|>
@article{zhao2024bad-gaussians:,
    title={BAD-Gaussians: Bundle Adjusted Deblur Gaussian Splatting},
    author={Lingzhe Zhao, Peng Wang, Peidong Liu},
    journal={arXiv preprint arXiv:2403.11831},
    year={2024},
    archivePrefix={arXiv},
    eprint={2403.11831},
    primaryClass={cs.CV}
}