Regression-based Pelvic Pose Initialization for Fast and Robust 2D/3D Pelvis Registration
This work addresses a domain-specific problem for clinical applications in medical imaging, offering an incremental improvement over existing methods.
The paper tackles the problem of 2D/3D pelvis registration by using a learned initialization function to improve convergence and accuracy in optimization-based pose estimators, resulting in enhanced robustness and computational efficiency, particularly in challenging cases with extreme pose variation.
This paper presents an approach for improving 2D/3D pelvis registration in optimization-based pose estimators using a learned initialization function. Current methods often fail to converge to the optimal solution when initialized naively. We find that even a coarse initializer greatly improves pose estimator accuracy, and improves overall computational efficiency. This approach proves to be effective also in challenging cases under more extreme pose variation. Experimental validation demonstrates that our method consistently achieves robust and accurate registration, enhancing the reliability of 2D/3D registration for clinical applications.