用事件相机+高斯点云实现鲁棒运动追踪
GS-EVT: Cross-Modal Event Camera Tracking based on Gaussian Splatting
- 基于高斯点云构建帧式摄像头地图，通过事件相机进行跨模态追踪
- 利用一阶动态参数化实现差分图像渲染，在多序列数据上精度稳定
- 适合需要抗光照变化与高速运动场景的机器人定位任务
可靠的自定位是众多智能移动平台的基础能力。本文探索了事件相机在运动追踪中的应用，提供了一种在复杂动态和光照条件下具有内在鲁棒性的解决方案。为克服事件相机建图的挑战，该方法采用跨模态框架：追踪由帧式摄像头直接生成的地图表示。具体而言，所提方法基于高斯点云（Gaussian Splatting）这一前沿表示方式，其支持高效且逼真的新视角合成。核心在于一种新型位姿参数化策略，结合参考位姿与一阶动态项，用于局部差分图像渲染，并在分阶段粗到精优化中与积分事件图像对比。实验结果表明，高斯点云的真实感渲染能力使系统在多种公开及新采集的数据序列上均实现稳定、精确的追踪。
原文摘要 · Abstract (English)
Reliable self-localization is a foundational skill for many intelligent mobile platforms. This paper explores the use of event cameras for motion tracking thereby providing a solution with inherent robustness under difficult dynamics and illumination. In order to circumvent the challenge of event camera-based mapping, the solution is framed in a cross-modal way. It tracks a map representation that comes directly from frame-based cameras. Specifically, the proposed method operates on top of gaussian splatting, a state-of-the-art representation that permits highly efficient and realistic novel view synthesis. The key of our approach consists of a novel pose parametrization that uses a reference pose plus first order dynamics for local differential image rendering. The latter is then compared against images of integrated events in a staggered coarse-to-fine optimization scheme. As demonstrated by our results, the realistic view rendering ability of gaussian splatting leads to stable and accurate tracking across a variety of both publicly available and newly recorded data sequences.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。