{
  "id": "2401.17857",
  "version": "v3",
  "published": "2024-01-31T14:19:03.000Z",
  "updated": "2024-05-17T19:02:20.000Z",
  "title": "SAGD: Boundary-Enhanced Segment Anything in 3D Gaussian via Gaussian Decomposition",
  "authors": [
    "Xu Hu",
    "Yuxi Wang",
    "Lue Fan",
    "Junsong Fan",
    "Junran Peng",
    "Zhen Lei",
    "Qing Li",
    "Zhaoxiang Zhang"
  ],
  "categories": [
    "cs.CV"
  ],
  "abstract": "3D Gaussian Splatting has emerged as an alternative 3D representation for novel view synthesis, benefiting from its high-quality rendering results and real-time rendering speed. However, the 3D Gaussians learned by 3D-GS have ambiguous structures without any geometry constraints. This inherent issue in 3D-GS leads to a rough boundary when segmenting individual objects. To remedy these problems, we propose SAGD, a conceptually simple yet effective boundary-enhanced segmentation pipeline for 3D-GS to improve segmentation accuracy while preserving segmentation speed. Specifically, we introduce a Gaussian Decomposition scheme, which ingeniously utilizes the special structure of 3D Gaussian, finds out, and then decomposes the boundary Gaussians. Moreover, to achieve fast interactive 3D segmentation, we introduce a novel training-free pipeline by lifting a 2D foundation model to 3D-GS. Extensive experiments demonstrate that our approach achieves high-quality 3D segmentation without rough boundary issues, which can be easily applied to other scene editing tasks.",
  "revisions": [
    {
      "version": "v3",
      "updated": "2024-05-17T19:02:20.000Z"
    }
  ],
  "analyses": {
    "keywords": [
      "3d gaussian",
      "boundary-enhanced segment",
      "approach achieves high-quality 3d segmentation",
      "rough boundary",
      "achieve fast interactive 3d segmentation"
    ],
    "note": {
      "typesetting": "TeX",
      "pages": 0,
      "language": "en",
      "license": "arXiv",
      "status": "editable"
    }
  }
}