# Robust and Accurate Multi-view 2D/3D Image Registration with Differentiable X-ray Rendering and Dual Cross-view Constraints

arXiv: https://arxiv.org/abs/2506.22191 · HTML: https://mlsanity.com/paper/2506.22191/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 12 of its 34 references that are in the map.
- First posted on arXiv 27 Jun 2025.

## Builds on (most cited first, 12 of 12)

- [DeepDRR -- A Catalyst for Machine Learning in Fluoroscopy-guided Procedures](https://mlsanity.com/paper/1803.08606/) (2018)
- [Fast Auto-Differentiable Digitally Reconstructed Radiographs for Solving Inverse Problems in Intraoperative Imaging](https://mlsanity.com/paper/2208.12737/) (2022)
- [SE(3) Diffusion Model-based Point Cloud Registration for Robust 6D Object Pose Estimation](https://mlsanity.com/paper/2310.17359/) (2023)
- [Automatic Annotation of Hip Anatomy in Fluoroscopy for Robust and Efficient 2D/3D Registration](https://mlsanity.com/paper/1911.07042/) (2019)
- [Intraoperative 2D/3D Image Registration via Differentiable X-ray Rendering](https://mlsanity.com/paper/2312.06358/) (2023)
- [X-ray-transform Invariant Anatomical Landmark Detection for Pelvic Trauma Surgery](https://mlsanity.com/paper/1803.08608/) (2018)
- [The Impact of Machine Learning on 2D/3D Registration for Image-guided Interventions: A Systematic Review and Perspective](https://mlsanity.com/paper/2108.02238/) (2021)
- [Multiview 2D/3D Rigid Registration via a Point-Of-Interest Network for Tracking and Triangulation (POINT2)](https://mlsanity.com/paper/1903.03896/) (2019)
- [Self-Supervised 2D/3D Registration for X-Ray to CT Image Fusion](https://mlsanity.com/paper/2210.07611/) (2022)
- [Pose Estimation of Periacetabular Osteotomy Fragments with Intraoperative X-Ray Navigation](https://mlsanity.com/paper/1903.09339/) (2019)
- [X-Ray to CT Rigid Registration Using Scene Coordinate Regression](https://mlsanity.com/paper/2311.15087/) (2023)
- [Multi-view X-ray Image Synthesis with Multiple Domain Disentanglement from CT Scans](https://mlsanity.com/paper/2404.11889/) (2024)

## Abstract (arXiv)

Robust and accurate 2D/3D registration, which aligns preoperative models with intraoperative images of the same anatomy, is crucial for successful interventional navigation. To mitigate the challenge of a limited field of view in single-image intraoperative scenarios, multi-view 2D/3D registration is required by leveraging multiple intraoperative images. In this paper, we propose a novel multi-view 2D/3D rigid registration approach comprising two stages. In the first stage, a combined loss function is designed, incorporating both the differences between predicted and ground-truth poses and the dissimilarities (e.g., normalized cross-correlation) between simulated and observed intraoperative images. More importantly, additional cross-view training loss terms are introduced for both pose and image losses to explicitly enforce cross-view constraints. In the second stage, test-time optimization is performed to refine the estimated poses from the coarse stage. Our method exploits the mutual constraints of multi-view projection poses to enhance the robustness of the registration process. The proposed framework achieves a mean target registration error (mTRE) of 0.79 ± 2.17 mm on six specimens from the DeepFluoro dataset, demonstrating superior performance compared to state-of-the-art registration algorithms.