Neural Residual Correction for 3D Tooth Point Cloud Canonicalization

Background: Statistical shape modeling and generative tooth synthesis require dental point clouds in canonical poses. This study compared canonicalization methods and proposed a hybrid pipeline pairing principal-axis alignment with a neural orientation guide and a trained residual correction. Methods: Seven classical, neural, and hybrid methods were evaluated on 9060 upper tooth point clouds across seven classes from 3DTeethSeg (891 patients, 1805 held out for validation) and 1465 external first molars from FDI16. Alignment was measured by Chamfer Distance to per-sample target poses (CD Target, validation only), Chamfer Distance to class templates (CD Template, both sets), and geodesic rotation error. Results: Neural-guided PCA selection with residual refinement (gPCA-rPointNet) reached the lowest CD Target (0.62 ± 2.43 × 10^-3) and geodesic rotation error (3.3 ± 14.5 degrees), with 98.2% of predictions below 15 degrees. On the external set, the four PCA-based methods gave a lower CD Template than methods without geometric initialization. Conclusions: A neural orientation guide placed before principal-axis candidate selection resolved the PCA eigenvector sign ambiguity responsible for 180-degree failures on near-symmetric tooth crowns. Residual correction further reduced rotation error. The same pipeline produced consistent canonical poses for first molars on the external dataset, with validation on other tooth classes remaining limited.