WildGaussians: 3D Gaussian Splatting in the Wild
<|reference_start|>WildGaussians: 3D Gaussian Splatting in the Wild: While the field of 3D scene reconstruction is dominated by NeRFs due to their photorealistic quality, 3D Gaussian Splatting (3DGS) has recently emerged, offering similar quality with real-time rendering speeds. However, both methods primarily excel with well-controlled 3D scenes, while in-the-wild data - characterized by occlusions, dynamic objects, and varying illumination - remains challenging. NeRFs can adapt to such conditions easily through per-image embedding vectors, but 3DGS struggles due to its explicit representation and lack of shared parameters. To address this, we introduce WildGaussians, a novel approach to handle occlusions and appearance changes with 3DGS. By leveraging robust DINO features and integrating an appearance modeling module within 3DGS, our method achieves state-of-the-art results. We demonstrate that WildGaussians matches the real-time rendering speed of 3DGS while surpassing both 3DGS and NeRF baselines in handling in-the-wild data, all within a simple architectural framework.<|reference_end|>
@article{kulhanek2024wildgaussians:,
    title={WildGaussians: 3D Gaussian Splatting in the Wild},
    author={Jonas Kulhanek, Songyou Peng, Zuzana Kukelova, Marc Pollefeys, Torsten
  Sattler},
    journal={arXiv preprint arXiv:2407.08447},
    year={2024},
    archivePrefix={arXiv},
    eprint={2407.08447},
    primaryClass={cs.CV}
}