title: GaussianObject: Just Taking Four Images to Get A High-Quality 3D Object
    with Gaussian Splatting
summary: Reconstructing and rendering 3D objects from highly sparse views is of
  critical importance for promoting applications of 3D vision techniques and
  improving user experience. However, images from sparse views only contain very
  limited 3D information, leading to two significant challenges: 1) Difficulty in
  building multi-view consistency as images for matching are too few; 2)
  Partially omitted or highly compressed object information as view coverage is
  insufficient. To tackle these challenges, we propose GaussianObject, a
  framework to represent and render the 3D object with Gaussian splatting, that
  achieves high rendering quality with only 4 input images. We first introduce
  techniques of visual hull and floater elimination which explicitly inject
  structure priors into the initial optimization process for helping build
  multi-view consistency, yielding a coarse 3D Gaussian representation. Then we
  construct a Gaussian repair model based on diffusion models to supplement the
  omitted object information, where Gaussians are further refined. We design a
  self-generating strategy to obtain image pairs for training the repair model.
  Our GaussianObject is evaluated on several challenging datasets, including
  MipNeRF360, OmniObject3D, and OpenIllumination, achieving strong reconstruction
  results from only 4 views and significantly outperforming previous
  state-of-the-art methods.
paper_authors._id: 65d2ebe60cb9d915d37a3b19
paper_authors.name: Chen Yang
paper_authors.status: claimed_verified
paper_authors.statusLastChangedAt: 2024-02-19T09:33:51.212Z
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paper_authors.avatarUrl: /avatars/b76919a4aeaedc8c735a1bcfc50136c1.svg
paper_authors.fullname: Chen Yang
paper_authors.type: user
paper_authors.user: chen-yang-ln
paper_authors._id: 65d2ebe60cb9d915d37a3b1a
paper_authors.name: Sikuang Li
paper_authors.status: extracted_confirmed
paper_authors.statusLastChangedAt: 2024-02-20T08:22:48.591Z
paper_authors._id: 643cad37b409fef15e0d0819
paper_authors.avatarUrl: /avatars/5dd28c5b9f5a608dfd584d6affccbdd0.svg
paper_authors.fullname: UranusITS
paper_authors.type: user
paper_authors.user: UranusITS
paper_authors._id: 65d2ebe60cb9d915d37a3b1b
paper_authors.name: Jiemin Fang
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2024-02-19T11:17:03.313Z
paper_authors._id: 652cf49cc6857682d3168f5a
paper_authors.avatarUrl: /avatars/9ccffa04832429aad37320a301ea8e36.svg
paper_authors.fullname: Jiemin Fang
paper_authors.type: user
paper_authors.user: JieminFang
paper_authors._id: 65d2ebe60cb9d915d37a3b1c
paper_authors.name: Ruofan Liang
paper_authors._id: 65d2ebe60cb9d915d37a3b1d
paper_authors.name: Lingxi Xie
paper_authors._id: 65d2ebe60cb9d915d37a3b1e
paper_authors.name: Xiaopeng Zhang
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2024-02-19T11:17:28.101Z
paper_authors._id: 640bea9cb4f73086cddbe0a6
paper_authors.avatarUrl: /avatars/ae460dd971de1d04660b266b22f81f9d.svg
paper_authors.fullname: Xiaopeng Zhang
paper_authors.type: user
paper_authors.user: xpzhang
paper_authors._id: 65d2ebe60cb9d915d37a3b1f
paper_authors.name: Wei Shen
paper_authors._id: 65d2ebe60cb9d915d37a3b20
paper_authors.name: Qi Tian
paper_title: GaussianObject: Just Taking Four Images to Get A High-Quality 3D Object
    with Gaussian Splatting
paper_summary: Reconstructing and rendering 3D objects from highly sparse views is of
  critical importance for promoting applications of 3D vision techniques and
  improving user experience. However, images from sparse views only contain very
  limited 3D information, leading to two significant challenges: 1) Difficulty in
  building multi-view consistency as images for matching are too few; 2)
  Partially omitted or highly compressed object information as view coverage is
  insufficient. To tackle these challenges, we propose GaussianObject, a
  framework to represent and render the 3D object with Gaussian splatting, that
  achieves high rendering quality with only 4 input images. We first introduce
  techniques of visual hull and floater elimination which explicitly inject
  structure priors into the initial optimization process for helping build
  multi-view consistency, yielding a coarse 3D Gaussian representation. Then we
  construct a Gaussian repair model based on diffusion models to supplement the
  omitted object information, where Gaussians are further refined. We design a
  self-generating strategy to obtain image pairs for training the repair model.
  Our GaussianObject is evaluated on several challenging datasets, including
  MipNeRF360, OmniObject3D, and OpenIllumination, achieving strong reconstruction
  results from only 4 views and significantly outperforming previous
  state-of-the-art methods.
paper_ai_keywords: Gaussian splatting multi-view consistency Gaussian repair model diffusion models visual hull floater elimination structure priors self-generating strategy reconstruction state-of-the-art methods