CAD-to-CT Registration of Cylindrical Objects via Ellipse-Based Axis Estimation
Quick Answer
The proposed two-stage geometric registration method for cylindrical objects achieves robust CAD-to-CT alignment with tilt and orientation errors below 0.1°, enhancing machine learning applications in industrial CT workflows without requiring intensity calibration or feature matching.
Quick Take
The proposed two-stage geometric registration method for cylindrical objects achieves robust CAD-to-CT alignment with tilt and orientation errors below 0.1°, enhancing machine learning applications in industrial CT workflows without requiring intensity calibration or feature matching.
Key Points
- Estimates 3D rotation axis using elliptical cross-sections from CT slices.
- Applies PCA on fitted ellipse centers after RANSAC outlier removal.
- Voxelizes CAD model and maximizes volumetric overlap with CT scan.
- Achieves robust registration without intensity calibration or feature matching.
- Provides ground truth geometry for machine learning-based object localization.
Paper Resources
Article Content
From source RSS / original summaryarXiv:2606. 02935v1 Announce Type: new Abstract: 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.
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