Accurate registration of CAD models to CT scans is essential for establishing ground truth geometry in volumetric imaging. Obtaining reliable object masks is of growing importance in machine learning settings; as recent architectures grow more capable, huge datasets are required to fully utilise their capabilities. Traditional intensity-based methods fail when CT grayscale values lack calibration references, while point-based algorithms (e.g., ICP, RANSAC) require feature correspondence unavailable between idealized CAD geometry and noisy volumetric CT data. We propose a two-stage geometric registration method for cylindrical objects (ionization chambers) that takes advantage of the distinctive geometric features of the objects. First, we estimate the 3D rotation axis by detecting elliptical cross-sections across CT slices, fitting ellipses to edge-detected contours, and performing PCA on the fitted ellipse centers after RANSAC outlier removal. Second, we voxelize the CAD model, orient it along the detected axis, and maximize volumetric overlap with the CT scan through translational adjustment. This approach achieves robust registration with tilt and orientation errors below $0.1^\circ$ without intensity calibration or feature matching. Once registered, the aligned CAD model provides ground truth geometry for applications including machine learning-based object localization and automated analysis in industrial CT workflows.

翻译：实现CAD模型与CT扫描的精确配准对于体积成像中建立几何真值至关重要。在机器学习背景下，获取可靠的物体掩膜日益重要——随着最新架构能力不断增强，需要大规模数据集才能充分发挥其性能。当CT灰度值缺乏校准参考时，传统基于强度的方法会失效；而基于点的算法（如ICP、RANSAC）需要在理想化CAD几何与含噪体积CT数据之间建立特征对应关系，这在实际中难以实现。针对圆柱形物体（电离室），我们提出一种利用其独特几何特征的两阶段配准方法：首先，通过检测CT切片中的椭圆截面，对边缘检测轮廓进行椭圆拟合，并在RANSAC离群点剔除后对拟合椭圆中心执行主成分分析（PCA），从而估计三维旋转轴；其次，对CAD模型进行体素化处理，使其沿检测到的轴线定向，并通过平移调整最大化与CT扫描的体积重叠。该方法无需强度校准或特征匹配，即可实现倾斜与定向误差低于0.1°的稳健配准。配准完成后，对齐的CAD模型可为机器学习目标定位和工业CT流程自动化分析等应用提供几何真值。