FDGaussian: Fast Gaussian Splatting from Single Image via
  Geometric-aware Diffusion Model
Qijun Feng Zhen Xing Zuxuan Wu Yu-Gang Jiang
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/.