基于体素的极端运动模糊3D场景重建
Splat-based 3D Scene Reconstruction with Extreme Motion-blur
- KAIST（韩国科学技术院）
- HYPERGRAM（超图）
机构由 AI 辅助整理，请以论文原文为准。
AI总结：
研究针对低光照下RGB-D输入的极端运动模糊问题，提出结合高斯体素框架的相机姿态估计与图像去模糊方法，通过对齐帧、调整高斯位置等步骤，提升3D重建质量，优于现有方法，有广泛应用意义。
AI中文摘要：
我们提出一种基于体素的3D场景重建方法，用于处理RGB-D输入中的极端运动模糊，这在低光照环境中是一个常见挑战。在昏暗照明下，RGB帧常因曝光时间长而出现严重运动模糊，导致传统相机姿态估计方法失败，影响3D重建质量。尽管近期技术有成果，但在快速运动或光照不佳时相机轨迹估计困难。我们引入结合相机姿态估计和图像去模糊的方法，利用高斯体素和深度输入增强场景表示。先通过光流和ICP对齐连续RGB-D帧，再调整高斯位置优化深度对齐来细化相机姿态和3D几何。通过比较输入与一系列清晰渲染帧去模糊图像。实验表明该方法优于现有方法，对机器人、自主导航和增强现实中的3D映射应用有广泛意义，代码和数据集公开。
英文摘要：
We propose a splat-based 3D scene reconstruction method from RGB-D input that effectively handles extreme motion blur, a frequent challenge in low-light environments. Under dim illumination, RGB frames often suffer from severe motion blur due to extended exposure times, causing traditional camera pose estimation methods, such as COLMAP, to fail. This results in inaccurate camera pose and blurry color input, compromising the quality of 3D reconstructions. Although recent 3D reconstruction techniques like Neural Radiance Fields and Gaussian Splatting have demonstrated impressive results, they rely on accurate camera trajectory estimation, which becomes challenging under fast motion or poor lighting conditions. Furthermore, rapid camera movement and the limited field of view of depth sensors reduce point cloud overlap, limiting the effectiveness of pose estimation with the ICP algorithm. To address these issues, we introduce a method that combines camera pose estimation and image deblurring using a Gaussian Splatting framework, leveraging both 3D Gaussian splats and depth inputs for enhanced scene representation. Our method first aligns consecutive RGB-D frames through optical flow and ICP, then refines camera poses and 3D geometry by adjusting Gaussian positions for optimal depth alignment. To handle motion blur, we model camera movement during exposure and deblur images by comparing the input with a series of sharp, rendered frames. Experiments on a new RGB-D dataset with extreme motion blur show that our method outperforms existing approaches, enabling high-quality reconstructions even in challenging conditions. This approach has broad implications for 3D mapping applications in robotics, autonomous navigation, and augmented reality. Both code and dataset are publicly available on https://github.com/KAIST-VCLAB/gs-extreme-motion-blur.
