Regression-based Pelvic Pose Initialization for Fast and Robust 2D/3D Pelvis Registration
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arXiv
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| Format: | Preprint |
| Published: |
2025
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| _version_ | 1866909904586932224 |
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| author | Suh, Yehyun Martin, J. Ryan Moyer, Daniel |
| author_facet | Suh, Yehyun Martin, J. Ryan Moyer, Daniel |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2503_07767 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Regression-based Pelvic Pose Initialization for Fast and Robust 2D/3D Pelvis Registration Suh, Yehyun Martin, J. Ryan Moyer, Daniel Computer Vision and Pattern Recognition 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. |
| title | Regression-based Pelvic Pose Initialization for Fast and Robust 2D/3D Pelvis Registration |
| topic | Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2503.07767 |