FDGaussian: Fast Gaussian Splatting from Single Image via
  Geometric-aware Diffusion Model
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/.
65f815f9a46363a9fbab9997
Qijun Feng
65f815f9a46363a9fbab9998
Zhen Xing
claimed_verified
2024-12-12T09:00:17.137Z
6332cd69fdc3d1fc8058515c
/avatars/83682bcac1f841adc4de50e2961ec624.svg
zhen xing
user
Chenhsing
65f815f9a46363a9fbab9999
Zuxuan Wu
65f815f9a46363a9fbab999a
Yu-Gang Jiang
FDGaussian: Fast Gaussian Splatting from Single Image via
  Geometric-aware Diffusion Model
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/.
A two-stage framework using orthogonal plane decomposition and epipolar attention addresses issues in 3D reconstruction from single images, improving consistency and quality.
2D diffusion models 3D reconstruction single-image 3D reconstruction orthogonal plane decomposition Gaussian Splatting epipolar attention image consistency high-quality 3D objects