Robust Rigid Bone Registration Using Depth-Constrained Surface Optimization for Head-Mounted AR Surgical Systems
Keywords:
Rigid registration, orthopedic AR navigation, depth-based tracking, curvature feature extraction, surface optimization, markerless guidanceAbstract
Rigid anatomical structures such as vertebrae and long bones require sub-millimeter registration accuracy in AR-guided orthopedic procedures. This work introduces a depth-constrained surface optimization method that enhances markerless rigid alignment using curvature-aware feature matching. A two-stage pipeline is designed. First, curvature-weighted keypoints are extracted from depth maps using multi-scale Laplacian surface analysis. Second, a robust RANSAC-enhanced rigid transformation model is optimized with a depth-consistency constraint to reject outliers caused by occlusion. Evaluation was performed on 18 cadaveric spine datasets and 10 synthetic CT-to-depth alignment tasks. Mean rotational error decreased from 1.92° to 0.84°,and translational error reduced from 2.16 mm to 0.93 mm compared with baseline depth-ICP. The approach achieved stable tracking at 45 FPS on an AR headset, supporting intraoperative bone navigation without fiducial markers.
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