从模糊图像中重建清晰场景，实现高精度定位与建图。
Deblur Gaussian Splatting SLAM
- 通过建模模糊成像过程，融合帧间与帧到模型方法，恢复子帧轨迹。
- 在合成与真实模糊数据上均达到最佳清晰地图与轨迹重建效果。
- 适合移动端、低光照或高速运动下的鲁棒三维重建应用。
我们提出 Deblur-SLAM，一种针对运动模糊输入的鲁棒彩色SLAM流水线，旨在恢复清晰重建。该方法结合帧间与帧到模型方法的优势，建模导致模糊的子帧相机轨迹，实现在运动模糊场景下的高保真重建。此外，流水线集成在线回环检测与全局束调整，实现稠密且精确的全局轨迹。通过建模运动模糊图像的物理成像过程，最小化观测模糊图像与由平均锐利虚拟子帧图像生成的渲染模糊图像之间的误差。同时，结合单目深度估计与高斯的在线变形，确保精准映射与增强图像去模糊。所提流水线整合所有组件以提升性能。在合成与真实世界模糊输入数据上，均实现了最先进的清晰地图估计与子帧轨迹恢复效果。
原文摘要 · Abstract (English)
We present Deblur-SLAM, a robust RGB SLAM pipeline designed to recover sharp reconstructions from motion-blurred inputs. The proposed method bridges the strengths of both frame-to-frame and frame-to-model approaches to model sub-frame camera trajectories that lead to high-fidelity reconstructions in motion-blurred settings. Moreover, our pipeline incorporates techniques such as online loop closure and global bundle adjustment to achieve a dense and precise global trajectory. We model the physical image formation process of motion-blurred images and minimize the error between the observed blurry images and rendered blurry images obtained by averaging sharp virtual sub-frame images. Additionally, by utilizing a monocular depth estimator alongside the online deformation of Gaussians, we ensure precise mapping and enhanced image deblurring. The proposed SLAM pipeline integrates all these components to improve the results. We achieve state-of-the-art results for sharp map estimation and sub-frame trajectory recovery both on synthetic and real-world blurry input data.
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