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arXiv · 2409.19228

GS-EVT: Cross-Modal Event Camera Tracking based on Gaussian Splatting

Tao Liu·Runze Yuan·Yi'ang Ju·Xun Xu·Jiaqi Yang·Xiangting Meng·Xavier Lagorce·Laurent Kneip
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Abstract

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.

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Tao Liu, Runze Yuan, Yi'ang Ju, Xun Xu, Jiaqi Yang, Xiangting Meng, Xavier Lagorce, Laurent Kneip. 2024-09-28. GS-EVT: Cross-Modal Event Camera Tracking based on Gaussian Splatting. https://arxiv.org/abs/2409.19228

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