For the past three years, Kaggle has been hosting the Image Matching
Challenge, which focuses on solving a 3D image reconstruction problem using a
collection of 2D images. Each year, this competition fosters the development of
innovative and effective methodologies by its participants. In this paper, we
introduce an advanced ensemble technique that we developed, achieving a score
of 0.153449 on the private leaderboard and securing the 160th position out of
over 1,000 participants. Additionally, we conduct a comprehensive review of
existing methods and techniques employed by top-performing teams in the
competition. Our solution, alongside the insights gathered from other leading
approaches, contributes to the ongoing advancement in the field of 3D image
reconstruction. This research provides valuable knowledge for future
participants and researchers aiming to excel in similar image matching and
reconstruction challenges.

该研究介绍了一种先进的集成技术，用于解决 3D 图像重建问题，并综合审查了参与竞赛的顶级团队采用的现有方法和技术，为未来的参与者和研究人员在类似的图像匹配和重建挑战中取得优异成绩提供了宝贵的知识。

IMC 2024 方法与解决方案综述

IMC 2024 Methods & Solutions Review

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。

SADIR：面向形状的扩散模型用于 3D 图像重建

SADIR: Shape-Aware Diffusion Models for 3D Image Reconstruction

3D image reconstruction from a set of X-ray projections is an important image
reconstruction problem, with applications in medical imaging, industrial
inspection and airport security. The innovation of X-ray emitter arrays allows
for a novel type of X-ray scanners with multiple simultaneously emitting
sources. However, two or more sources emitting at the same time can yield
measurements from overlapping rays, imposing a new type of image reconstruction
problem based on nonlinear constraints. Using traditional linear reconstruction
methods, respective scanner geometries have to be implemented such that no rays
overlap, which severely restricts the scanner design. We derive a new type of
3D image reconstruction model with nonlinear constraints, based on measurements
with overlapping X-rays. Further, we show that the arising optimization problem
is partially convex, and present an algorithm to solve it. Experiments show
highly improved image reconstruction results from both simulated and real-world
measurements.

使用发射器阵列的 X 射线扫描仪可同时产生多个发射源，但可能存在测量值重叠的非线性约束。本文提出了一种新的基于测量值重叠的非线性约束的 3D 图像重建模型，并提出了一种可解决该问题的算法。实验结果表明，模型在模拟和真实测量中均具有高度优异的图像重建效果。