Motion-aware 3D Gaussian Splatting for Efficient Dynamic Scene Reconstruction
<|reference_start|>Motion-aware 3D Gaussian Splatting for Efficient Dynamic Scene Reconstruction: 3D Gaussian Splatting (3DGS) has become an emerging tool for dynamic scene reconstruction. However, existing methods focus mainly on extending static 3DGS into a time-variant representation, while overlooking the rich motion information carried by 2D observations, thus suffering from performance degradation and model redundancy. To address the above problem, we propose a novel motion-aware enhancement framework for dynamic scene reconstruction, which mines useful motion cues from optical flow to improve different paradigms of dynamic 3DGS. Specifically, we first establish a correspondence between 3D Gaussian movements and pixel-level flow. Then a novel flow augmentation method is introduced with additional insights into uncertainty and loss collaboration. Moreover, for the prevalent deformation-based paradigm that presents a harder optimization problem, a transient-aware deformation auxiliary module is proposed. We conduct extensive experiments on both multi-view and monocular scenes to verify the merits of our work. Compared with the baselines, our method shows significant superiority in both rendering quality and efficiency.<|reference_end|>
@article{guo2024motion-aware,
    title={Motion-aware 3D Gaussian Splatting for Efficient Dynamic Scene
  Reconstruction},
    author={Zhiyang Guo, Wengang Zhou, Li Li, Min Wang, Houqiang Li},
    journal={arXiv preprint arXiv:2403.11447},
    year={2024},
    archivePrefix={arXiv},
    eprint={2403.11447},
    primaryClass={cs.CV}
}