EvaGaussians: Event Stream Assisted Gaussian Splatting from Blurry Images
<|reference_start|>EvaGaussians: Event Stream Assisted Gaussian Splatting from Blurry Images: 3D Gaussian Splatting (3D-GS) has demonstrated exceptional capabilities in 3D scene reconstruction and novel view synthesis. However, its training heavily depends on high-quality, sharp images and accurate camera poses. Fulfilling these requirements can be challenging in non-ideal real-world scenarios, where motion-blurred images are commonly encountered in high-speed moving cameras or low-light environments that require long exposure times. To address these challenges, we introduce Event Stream Assisted Gaussian Splatting (EvaGaussians), a novel approach that integrates event streams captured by an event camera to assist in reconstructing high-quality 3D-GS from blurry images. Capitalizing on the high temporal resolution and dynamic range offered by the event camera, we leverage the event streams to explicitly model the formation process of motion-blurred images and guide the deblurring reconstruction of 3D-GS. By jointly optimizing the 3D-GS parameters and recovering camera motion trajectories during the exposure time, our method can robustly facilitate the acquisition of high-fidelity novel views with intricate texture details. We comprehensively evaluated our method and compared it with previous state-of-the-art deblurring rendering methods. Both qualitative and quantitative comparisons demonstrate that our method surpasses existing techniques in restoring fine details from blurry images and producing high-fidelity novel views.<|reference_end|>
@article{yu2024evagaussians:,
    title={EvaGaussians: Event Stream Assisted Gaussian Splatting from Blurry
  Images},
    author={Wangbo Yu, Chaoran Feng, Jiye Tang, Xu Jia, Li Yuan, Yonghong Tian},
    journal={arXiv preprint arXiv:2405.20224},
    year={2024},
    archivePrefix={arXiv},
    eprint={2405.20224},
    primaryClass={cs.CV}
}