EvTrajGS：基于无姿态事件流的精确高效3D高斯溅射（Gaussian Splatting）方法
EvTrajGS: Accurate and Efficient 3D Gaussian Splatting from Unposed Event Streams
- School of Computer Science and Engineering, Sun Yat-sen University（中山大学计算机科学与工程学院）
- Guangdong Province Key Laboratory of Information Security Technology（广东省信息安全技术重点实验室）
- Key Laboratory of Machine Intelligence and Advanced Computing, Ministry of Education（机器智能与先进计算教育部重点实验室）
- College of Computing and Data Science, Nanyang Technological University（南洋理工大学计算与数据科学学院）
- School of Computing and Information Technology, Great Bay University（大湾区大学计算与信息技术学院）
机构由 AI 辅助整理，请以论文原文为准。
AI总结：
本文提出EvTrajGS框架，针对无姿态事件流实现精确高效的3D高斯溅射，通过连续时间轨迹参数化、时间耦合姿态更新及损失加权事件采样，在提升重建与姿态精度的同时降低计算开销，性能优于现有方法。
AI中文摘要：
具备高时间分辨率、高动态范围和异步感知特性的事件相机，在密集3D重建领域展现出巨大潜力。基于现成姿态估计的传统重建方法效率高，但因不准确的姿态初始化会引入累积重建误差，导致结果保真度低；相比之下，近期SLAM式方法通过增量跟踪与建图稳定了姿态-场景联合优化，虽重建保真度更高，但需付出大量计算开销。为解决该权衡问题，本文提出EvTrajGS，一种针对无姿态事件流的精确高效3D高斯溅射（Gaussian Splatting）框架。该方法可基于粗略姿态先验初始化可靠的姿态-场景联合优化，无需计算开销高昂的SLAM式流水线；将相机运动参数化为从离散相机姿态初始化的连续时间轨迹，为姿态优化提供统一表示；随后将相邻轨迹状态聚合为时间耦合姿态，在联合优化期间促进时间一致的姿态更新；此外，引入损失加权的事件采样策略，自适应强调时间上重建不足的区间。在合成数据集与真实世界数据集上的大量实验表明，EvTrajGS在几何重建质量和姿态估计精度上均优于现有方法，实现了3.8 dB更高的PSNR、0.1更高的SSIM，ATE RMSE降低超过40%，同时保持了高计算效率。
英文摘要：
Event cameras, with high temporal resolution, high dynamic range, and asynchronous sensing characteristics, have shown great potential for dense 3D reconstruction. Traditional reconstruction methods based on off-the-shelf pose estimates achieve high efficiency but produce low-fidelity results, as inaccurate pose initialization introduces cumulative reconstruction errors. In contrast, recent SLAM-style methods stabilize joint pose-scene optimization through incremental tracking and mapping, yielding higher reconstruction fidelity at the expense of considerable computational overhead. To address this trade-off, this paper presents EvTrajGS, an accurate and efficient 3D Gaussian Splatting framework for unposed event streams. Our method enables reliable joint pose-scene optimization initialized from coarse pose priors, eliminating the need for computationally expensive SLAM-style pipelines. EvTrajGS parameterizes camera motion as a continuous-time trajectory initialized from discrete camera poses, providing a unified representation for pose refinement. We then aggregate adjacent trajectory states into a temporally coupled pose, promoting temporally consistent pose updates during joint optimization. Additionally, we introduce a loss-reweighted event sampling strategy to adaptively emphasize temporally under-reconstructed intervals. Extensive experiments on both synthetic and real-world datasets demonstrate that EvTrajGS outperforms state-of-the-art methods in terms of both geometric reconstruction quality and pose estimation accuracy, achieving 3.8 dB higher PSNR, 0.1 higher SSIM, and over 40\% lower ATE RMSE while retaining high computational efficiency.
