title: FDGaussian: Fast Gaussian Splatting from Single Image via
    Geometric-aware Diffusion Model
summary: Reconstructing detailed 3D objects from single-view images remains a
  challenging task due to the limited information available. In this paper, we
  introduce FDGaussian, a novel two-stage framework for single-image 3D
  reconstruction. Recent methods typically utilize pre-trained 2D diffusion
  models to generate plausible novel views from the input image, yet they
  encounter issues with either multi-view inconsistency or lack of geometric
  fidelity. To overcome these challenges, we propose an orthogonal plane
  decomposition mechanism to extract 3D geometric features from the 2D input,
  enabling the generation of consistent multi-view images. Moreover, we further
  accelerate the state-of-the-art Gaussian Splatting incorporating epipolar
  attention to fuse images from different viewpoints. We demonstrate that
  FDGaussian generates images with high consistency across different views and
  reconstructs high-quality 3D objects, both qualitatively and quantitatively.
  More examples can be found at our website https://qjfeng.net/FDGaussian/.
paper_authors._id: 65f815f9a46363a9fbab9997
paper_authors.name: Qijun Feng
paper_authors._id: 65f815f9a46363a9fbab9998
paper_authors.name: Zhen Xing
paper_authors.status: claimed_verified
paper_authors.statusLastChangedAt: 2024-12-12T09:00:17.137Z
paper_authors._id: 6332cd69fdc3d1fc8058515c
paper_authors.avatarUrl: /avatars/83682bcac1f841adc4de50e2961ec624.svg
paper_authors.fullname: zhen xing
paper_authors.type: user
paper_authors.user: Chenhsing
paper_authors._id: 65f815f9a46363a9fbab9999
paper_authors.name: Zuxuan Wu
paper_authors._id: 65f815f9a46363a9fbab999a
paper_authors.name: Yu-Gang Jiang
paper_title: FDGaussian: Fast Gaussian Splatting from Single Image via
    Geometric-aware Diffusion Model
paper_summary: Reconstructing detailed 3D objects from single-view images remains a
  challenging task due to the limited information available. In this paper, we
  introduce FDGaussian, a novel two-stage framework for single-image 3D
  reconstruction. Recent methods typically utilize pre-trained 2D diffusion
  models to generate plausible novel views from the input image, yet they
  encounter issues with either multi-view inconsistency or lack of geometric
  fidelity. To overcome these challenges, we propose an orthogonal plane
  decomposition mechanism to extract 3D geometric features from the 2D input,
  enabling the generation of consistent multi-view images. Moreover, we further
  accelerate the state-of-the-art Gaussian Splatting incorporating epipolar
  attention to fuse images from different viewpoints. We demonstrate that
  FDGaussian generates images with high consistency across different views and
  reconstructs high-quality 3D objects, both qualitatively and quantitatively.
  More examples can be found at our website https://qjfeng.net/FDGaussian/.
paper_ai_keywords: FDGaussian 3D reconstruction single-view images pre-trained 2D diffusion models multi-view inconsistency geometric fidelity orthogonal plane decomposition 3D geometric features epipolar attention Gaussian Splatting