GS-EVT: Cross-Modal Event Camera Tracking based on Gaussian Splatting
<|reference_start|>GS-EVT: Cross-Modal Event Camera Tracking based on Gaussian Splatting: 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.<|reference_end|>
@article{liu2024gs-evt:,
    title={GS-EVT: Cross-Modal Event Camera Tracking based on Gaussian Splatting},
    author={Tao Liu, Runze Yuan, Yi'ang Ju, Xun Xu, Jiaqi Yang, Xiangting Meng,
  Xavier Lagorce, Laurent Kneip},
    journal={arXiv preprint arXiv:2409.19228},
    year={2024},
    archivePrefix={arXiv},
    eprint={2409.19228},
    primaryClass={cs.CV}
}