3D image reconstruction from a limited number of 2D images has been a
long-standing challenge in computer vision and image analysis. While deep
learning-based approaches have achieved impressive performance in this area,
existing deep networks often fail to effectively utilize the shape structures
of objects presented in images. As a result, the topology of reconstructed
objects may not be well preserved, leading to the presence of artifacts such as
discontinuities, holes, or mismatched connections between different parts. In
this paper, we propose a shape-aware network based on diffusion models for 3D
image reconstruction, named SADIR, to address these issues. In contrast to
previous methods that primarily rely on spatial correlations of image
intensities for 3D reconstruction, our model leverages shape priors learned
from the training data to guide the reconstruction process. To achieve this, we
develop a joint learning network that simultaneously learns a mean shape under
deformation models. Each reconstructed image is then considered as a deformed
variant of the mean shape. We validate our model, SADIR, on both brain and
cardiac magnetic resonance images (MRIs). Experimental results show that our
method outperforms the baselines with lower reconstruction error and better
preservation of the shape structure of objects within the images.

利用扩散模型提出一种基于形状感知网络的三维图像重建方法 (SADIR)，通过学习形状先验知识降低三维重建中的伪迹，并在脑和心脏核磁共振图像上的实验证明了模型相对基准方法具有更低的重建误差和更好的形状结构保留能力。