发表机构
School of Mechatronic Engineering and Automation, Shanghai University; Shanghai Baoshan Shangda General Intelligent Robotics Research Institute; School of Computer Science and Technology, Tongji University（上海大学机电工程与自动化学院; 上海宝山尚大通用智能机器人研究院; 同济大学计算机科学与技术学院）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
研究相机快速移动曝光时模糊问题，提出JADE-GS方法，通过像素自适应路由门融合互补先验，2D恢复器与3D高斯点云渲染双向循环耦合，正则化恢复器，在合成和真实基准测试中获最佳感知质量，训练高效且保留实时渲染。
AI 中文摘要
当相机在曝光期间快速移动时，模糊会破坏3D模型恢复清晰场景所需的曝光内运动，而事件相机能以微秒分辨率捕捉此信号。将其转化为可靠的3D监督面临两个障碍：基于物理的事件积分先验保留边缘但累积漂移，学习网络恢复纹理但扭曲边界；现有管道单向运行，原始事件噪声或固定2D伪标签偏差未经校正进入几何结构。JADE-GS通过像素自适应路由门融合互补先验，2D恢复器与3D高斯点云渲染学生在双向循环中耦合， detached、多视图一致渲染和基于物理的重新模糊约束对恢复器进行正则化，使其成为几何感知预测器。在合成和真实基准测试中，JADE-GS获得最佳感知质量，在两个基准测试中领先LPIPS和CLIP-IQA，具有有竞争力的PSNR和SSIM，在单个消费级GPU上5GB下约一小时训练完成并保留实时渲染。
英文摘要
Neural radiance fields and 3D Gaussian Splatting assume that each training image is a sharp and geometrically consistent observation of the scene. Motion blur violates this assumption because a single exposure integrates a continuous range of camera poses. Exposure integration also removes the temporal information needed to recover the corresponding sharp observation. Event cameras preserve this information at microsecond resolution and therefore provide a natural complement to conventional images. Existing event-assisted reconstruction methods predominantly obtain image supervision through analytical inversion of the Event Double Integral. Learned restoration from frames and events offers a second prior. Although weaker when used alone, it fails in different regions and provides complementary evidence. We present JADE-GS, which formulates the combination of these priors as spatial evidence allocation. A lightweight Spatial Prior Router predicts a pixelwise allocation using only the blurry frame and event stream, then fuses the two fixed restorations into an additional supervision target. The router is trained without a sharp reference using consistency with the scene under reconstruction and the measured exposure, and is removed after optimization. Experiments show that JADE-GS achieves leading perceptual quality on both benchmarks, attains the best fidelity on the real benchmark, and remains competitive on the synthetic one. It requires substantially lower training overhead than diffusion-based alternatives and preserves native 3DGS rendering with no generative decoding at inference.
