Event3DGS: Event-Based 3D Gaussian Splatting for High-Speed Robot Egomotion
<|reference_start|>Event3DGS: Event-Based 3D Gaussian Splatting for High-Speed Robot Egomotion: By combining differentiable rendering with explicit point-based scene representations, 3D Gaussian Splatting (3DGS) has demonstrated breakthrough 3D reconstruction capabilities. However, to date 3DGS has had limited impact on robotics, where high-speed egomotion is pervasive: Egomotion introduces motion blur and leads to artifacts in existing frame-based 3DGS reconstruction methods. To address this challenge, we introduce Event3DGS, an {\em event-based} 3DGS framework. By exploiting the exceptional temporal resolution of event cameras, Event3GDS can reconstruct high-fidelity 3D structure and appearance under high-speed egomotion. Extensive experiments on multiple synthetic and real-world datasets demonstrate the superiority of Event3DGS compared with existing event-based dense 3D scene reconstruction frameworks; Event3DGS substantially improves reconstruction quality (+3dB) while reducing computational costs by 95\%. Our framework also allows one to incorporate a few motion-blurred frame-based measurements into the reconstruction process to further improve appearance fidelity without loss of structural accuracy.<|reference_end|>
@article{xiong2024event3dgs:,
    title={Event3DGS: Event-Based 3D Gaussian Splatting for High-Speed Robot
  Egomotion},
    author={Tianyi Xiong, Jiayi Wu, Botao He, Cornelia Fermuller, Yiannis
  Aloimonos, Heng Huang, Christopher A. Metzler},
    journal={arXiv preprint arXiv:2406.02972},
    year={2024},
    archivePrefix={arXiv},
    eprint={2406.02972},
    primaryClass={cs.CV}
}