A Landmark-Free 3D–2D Rigid Liver Registration via Point Cloud Matching for Laparoscopic Surgery

Abstract

Real-time registration of preoperative 3D liver models to intraoperative 2D laparoscopic images is essential for augmented reality navigation in minimally invasive liver surgery. However, 3D–2D registration typically depends on anatomical landmarks extraction and pose estimation based on iterative projection-based landmark distance computation, which is time-consuming. Unlike iterative pose refinement strategies, our method treats liver pose estimation as a partial-to-complete point matching problem. First, our method leverages monocular depth estimation to reconstruct partial intraoperative point clouds from a single RGB image. Then, a two-stage point matching framework establishes dense 3D–3D correspondences, ultimately inferring the 6-DoF rigid pose by solving a weighted SVD over the matched point pairs. The experiments on the P2ILF dataset have a reprojection error of 126.37 ± 48.98 pixels and a target registration error of 25.20 mm on the LLR-LUS dataset. These results indicate that our method achieves promising accuracy and efficiency in aligning preoperative models to intraoperative scenes, suggesting its potential for practical rigid alignment in near real-time laparoscopic liver AR navigation.