BAD-Gaussians: Bundle Adjusted Deblur Gaussian Splatting

• URL: http://arxiv.org/abs/2403.11831v2
• Date: Tue, 19 Mar 2024 11:31:44 GMT
• Title: BAD-Gaussians: Bundle Adjusted Deblur Gaussian Splatting
• Authors: Lingzhe Zhao, Peng Wang, Peidong Liu,
• Abstract summary: BAD-Gaussians is a novel approach to handle severe motion-blurred images with inaccurate camera poses. Our method achieves superior rendering quality compared to previous state-of-the-art deblur neural rendering methods.
• Score: 8.380954205255104
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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/

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