We present a novel deep learning-based framework: Embedded Feature Correlation Optimization with Specific Parameter Initialization (COSPI) for 2D/3D registration which is a most challenging problem due to the difficulty such as dimensional mismatch, heavy computation load and lack of golden evaluating standard. The framework we designed includes a parameter specification module to efficiently choose initialization pose parameter and a fine-registration network to align images. The proposed framework takes extracting multi-scale features into consideration using a novel composite connection encoder with special training techniques. The method is compared with both learning-based methods and optimization-based methods to further evaluate the performance. Our experiments demonstrate that the method in this paper has improved the registration performance, and thereby outperforms the existing methods in terms of accuracy and running time. We also show the potential of the proposed method as an initial pose estimator.

翻译：我们提出了一种基于深度学习的新型框架：特定参数初始化的嵌入特征相关性优化（COSPI），用于解决2D/3D配准这一极具挑战性的问题。该问题因维度不匹配、计算负载高以及缺乏黄金评估标准而困难重重。我们设计的框架包含一个参数规范模块，用于高效选择初始位姿参数，以及一个精细配准网络用于图像对齐。所提出的框架通过采用一种新颖的复合连接编码器及特殊训练技术，将多尺度特征提取纳入考虑。该方法与基于学习的方法及基于优化的方法进行了对比，以进一步评估其性能。我们的实验表明，本文方法在配准性能上有所提升，从而在精度和运行时间方面优于现有方法。我们还展示了所提方法作为初始位姿估计器的潜力。