# Real-time 2D/3D Registration via CNN Regression

arXiv: https://arxiv.org/abs/1507.07505 · HTML: https://mlsanity.com/paper/1507.07505/

## Key facts

- Cited by 15 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 27 Jul 2015.

## Built on by (most cited first, 15 of 15)

- [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)
- [X-Ray to CT Rigid Registration Using Scene Coordinate Regression](https://mlsanity.com/paper/2311.15087/) (2023)
- [Deep Iterative 2D/3D Registration](https://mlsanity.com/paper/2107.10004/) (2021)
- [Embedded Feature Similarity Optimization with Specific Parameter Initialization for 2D/3D Medical Image Registration](https://mlsanity.com/paper/2305.06252/) (2023)
- [A Transformer-based Network for Deformable Medical Image Registration](https://mlsanity.com/paper/2202.12104/) (2022)
- [Medical Image Registration and Its Application in Retinal Images: A Review](https://mlsanity.com/paper/2403.16502/) (2024)
- [Learning the Update Operator for 2D/3D Image Registration](https://mlsanity.com/paper/2102.02861/) (2021)
- [Automatic Multi-View X-Ray/CT Registration Using Bone Substructure Contours](https://mlsanity.com/paper/2506.13292/) (2025)
- [Intraoperative 2D/3D Registration via Spherical Similarity Learning and Differentiable Levenberg-Marquardt Optimization](https://mlsanity.com/paper/2509.06890/) (2025)
- [Modeling the Nonsmoothness of Modern Neural Networks](https://mlsanity.com/paper/2103.14731/) (2021)
- [The Impact of Loss Functions and Scene Representations for 3D/2D Registration on Single-view Fluoroscopic X-ray Pose Estimation](https://mlsanity.com/paper/2308.00214/) (2023)
- [The Devil is in the Details: Boosting Guided Depth Super-Resolution via Rethinking Cross-Modal Alignment and Aggregation](https://mlsanity.com/paper/2401.08123/) (2024)
- [4D Virtual Imaging Platform for Dynamic Joint Assessment via Uni-Plane X-ray and 2D-3D Registration](https://mlsanity.com/paper/2508.16138/) (2025)
- [LDRNet: Large Deformation Registration Model for Chest CT Registration](https://mlsanity.com/paper/2602.01812/) (2026)
- [Patient-Agnostic Synthetic Pretraining for Efficient Patient-Specific Intraoperative 2D/3D Registration](https://mlsanity.com/paper/2607.23343/) (2026)

## Abstract (arXiv)

In this paper, we present a Convolutional Neural Network (CNN) regression approach for real-time 2-D/3-D registration. Different from optimization-based methods, which iteratively optimize the transformation parameters over a scalar-valued metric function representing the quality of the registration, the proposed method exploits the information embedded in the appearances of the Digitally Reconstructed Radiograph and X-ray images, and employs CNN regressors to directly estimate the transformation parameters. The CNN regressors are trained for local zones and applied in a hierarchical manner to break down the complex regression task into simpler sub-tasks that can be learned separately. Our experiment results demonstrate the advantage of the proposed method in computational efficiency with negligible degradation of registration accuracy compared to intensity-based methods.