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/.

翻译：从单视角图像重建精细三维物体因信息有限而极具挑战性。本文提出FDGaussian，一种新颖的两阶段单图像三维重建框架。现有方法通常利用预训练的二维扩散模型从输入图像生成合理的多视角图像，但存在多视角不一致或几何保真度不足的问题。为克服这些挑战，我们提出正交平面分解机制，从二维输入中提取三维几何特征，从而生成一致的多视角图像。此外，我们进一步加速了基于极线注意力融合多视角图像的最新高斯泼溅方法。定性与定量实验表明，FDGaussian能生成高度一致的多视角图像，并重建高质量的三维物体。更多示例请访问我们的网站https://qjfeng.net/FDGaussian/。