A fast and robust geometric point cloud registration model for orthopedic surgery with noisy and incomplete data.

Purpose: Accurate registration of partial-to-partial point clouds is crucial in computer-assisted orthopedic surgery but faces challenges due to incomplete data, noise, and partial overlap. This paper proposes a novel geometric fast registration (GFR) model that addresses these issues through three core modules: point extractor registration (PER), dual attention transformer (DAT), and geometric feature matching (GFM).

Methods: PER operates within the frequency domain to enhance point cloud data by attenuating noise and reconstructing incomplete regions. DAT augments feature representation by correlating independent features from source and target point clouds, improving model expressiveness. GFM identifies geometrically consistent point pairs, completing missing data and refining registration accuracy.

Results: We conducted experiments using the clinical bone dataset of 1432 distinct human skeletal samples, comprising ribs, scapulae, and fibula. The proposed model exhibited remarkable robustness and versatility, demonstrating consistent performance across diverse bone structures. When evaluated to noisy, partial-to-partial point clouds with incomplete bone data, the model achieved a mean squared error of 3.57 for rotation and a mean absolute error of 1.29. The mean squared error for translation was 0.002, with a mean absolute error of 0.038.

Conclusion: Our proposed GFR model exhibits exceptional speed and universality, effectively handling point clouds with defects, noise, and partial overlap. Extensive experiments conducted on bone datasets demonstrate the superior performance of our model compared to state-of-the-art methods. The code is publicly available at https://github.com/xzh128/PER .