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BAD-Gaussians: Bundle Adjusted Deblur Gaussian Splatting
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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/
Forward citations
Cited by 6 Pith papers
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GS2E: Gaussian Splatting is an Effective Data Generator for Event Stream Generation
A pipeline that turns sparse multi-view RGB images into a claimed 1,150-scene synthetic event dataset using 3D Gaussian Splatting rendering plus a stochastic event simulator.
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Deblur-Avatar: Animatable Avatars from Motion-Blurred Monocular Videos
Deblur-Avatar reconstructs sharp, animatable human avatars from motion-blurred monocular video by optimizing SMPL start and end poses and averaging rendered virtual frames inside 3D Gaussian Splatting.
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CoCoGaussian: Leveraging Circle of Confusion for Gaussian Splatting from Defocused Images
CoCoGaussian reconstructs sharp 3D scenes from defocused multi-view photos by modeling the circle of confusion with extra 3D Gaussians.
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MBA-SLAM: Motion Blur Aware Gaussian Splatting SLAM
A dense RGB-D SLAM system that models camera motion during exposure and re-blurs rendered images, improving tracking and mapping on motion-blurred and sharp video.
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Beyond Gaussians: Fast and High-Fidelity 3D Splatting with Linear Kernels
3DLS replaces Gaussian kernels with bounded linear kernels plus distribution alignment and gradient scaling, yielding slightly better fidelity and faster rendering than 3DGS on some scenes.
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USP-Gaussian: Unifying Spike-based Image Reconstruction, Pose Correction and Gaussian Splatting
USP-Gaussian jointly optimizes spike-to-image reconstruction, camera poses, and 3D Gaussian Splatting, reducing cascaded errors and improving 3D reconstruction quality on synthetic and real-world spike-camera data.
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