{
  "id": "2407.08447",
  "version": "v1",
  "published": "2024-07-11T12:41:32.000Z",
  "updated": "2024-07-11T12:41:32.000Z",
  "title": "WildGaussians: 3D Gaussian Splatting in the Wild",
  "authors": [
    "Jonas Kulhanek",
    "Songyou Peng",
    "Zuzana Kukelova",
    "Marc Pollefeys",
    "Torsten Sattler"
  ],
  "comment": "https://wild-gaussians.github.io/",
  "categories": [
    "cs.CV"
  ],
  "abstract": "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.",
  "revisions": [
    {
      "version": "v1",
      "updated": "2024-07-11T12:41:32.000Z"
    }
  ],
  "analyses": {
    "keywords": [
      "3d gaussian splatting",
      "wildgaussians",
      "in-the-wild data",
      "method achieves state-of-the-art results",
      "3d scene reconstruction"
    ],
    "tags": [
      "github project"
    ],
    "note": {
      "typesetting": "TeX",
      "pages": 0,
      "language": "en",
      "license": "arXiv",
      "status": "editable"
    }
  }
}