# A Coarse-to-Fine Framework with Curvature Feature Learning for Robust Point Cloud Registration in Spinal Surgical Navigation

## TL;DR

This paper introduces a new framework for aligning 3D scans in spinal surgery, improving accuracy and robustness despite noise and poor overlap.

## Contribution

A coarse-to-fine registration framework with curvature feature learning for robust point cloud alignment in spinal navigation.

## Key findings

- The proposed method achieves average rotation and translation errors of 0.34° and 0.27 mm in noise-free conditions.
- Under noisy conditions, the method shows average rotation and translation errors of 7.28° and 9.08 mm.
- The algorithm outperforms existing methods in accuracy, speed, and robustness on pre- and intra-operative datasets.

## Abstract

In surgical navigation-assisted pedicle screw fixation, cross-source pre- and intra-operative point clouds registration faces challenges like significant initial pose differences and low overlapping ratio. Classical algorithms based on feature descriptor have high computational complexity and are less robust to noise, leading to a decrease in accuracy and navigation performance. To address these problems, this paper proposes a coarse-to-fine registration framework. In the coarse registration stage, a Point Matching algorithm based on Curvature Feature Learning (CFL-PM) is proposed. Through CFL-PM and Farthest Point Sampling (FPS), the coarse registration of overlapping regions between the two point clouds is achieved. In the fine registration stage, the Iterative Closest Point (ICP) is used for further optimization. The proposed method effectively addresses the challenges of noise, initial pose and low overlapping ratio. In noise-free point cloud registration experiments, the average rotation and translation errors reached 0.34° and 0.27 mm. Under noisy conditions, the average rotation error of the coarse registration is 7.28°, and the average translation error is 9.08 mm. Experiments on pre- and intra-operative point cloud datasets demonstrate the proposed algorithm outperforms the compared algorithms in registration accuracy, speed, and robustness. Therefore, the proposed method can achieve the precise alignment of the surgical navigation-assisted pedicle screw fixation.