An Efficient and Accurate Registration for Structured Light Based Intraoral Scanning

In oral healthcare and digital dentistry, structured-light-based 3D reconstruction is widely used for tooth scanning; however, the lack of distinctive textures and the high similarity among tooth morphologies make point-cloud registration particularly challenging, often leading to tracking loss. We present a robust and efficient global relocalization framework designed for tracking-loss recovery. It first enhances surface texture with adaptive histogram equalization so that ORB can reliably extract rich 2D keypoints; these keypoints are then lifted to produce a sparse, high-quality set of 3D candidate points. Building on this sampling strategy, we compute FPFH descriptors guided by ORB salient features and replace costly k-d tree queries with a grid-based indexing scheme and depth-consistency checks. This reduces neighborhood queries for M ORB-lifted 3D keypoints from O(M logN) to O(M), where M ≪ N. Finally, we employ TurboReg, a linear-complexity graph-based solver, to obtain deterministic, millisecond-level backend pose estimates. Experiments on dental datasets simulating severe tracking loss demonstrate an over 11× acceleration of the full pipeline while preserving high local accuracy, with a 0.020 mm inlier RMSE and up to 83.8% inlier ratio under large viewpoint variations.