mlsanityData to 14 Oct 2026

APCReg: Anatomical-Prior-Guided Coarse-to-Fine CBCT--IOS Registration via Multi-View Projection and Reliability-Controlled Residual Correction

arXiv 2608.09993 ↗First posted 7 Aug 2026eess.IV cs.CVcited by 0

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Abstract (arXiv)

Registration between cone-beam computed tomography (CBCT) and intraoral scans (IOS) is essential for patient-specific surgical planning. However, disparate imaging modalities, limited overlap, and large pose offsets make automated registration unreliable. Consequently, clinical registration remains dependent on conventional geometry pipelines and manual clinician adjustment. To address these challenges, we propose APCReg, an anatomical-prior-guided coarse-to-fine framework for global registration and reliability-controlled residual correction. Specifically, multi-view anatomical coarse registration (MACR) performs ordered orthogonal projection alignment (buccal, proximal, and occlusal) to decompose the six-degree-of-freedom search before three-dimensional refinement. Overlap-aware residual registration (OARR) combines shared KPConv features, a folded arch-length cue, overlap-gated cross-attention, and Sinkhorn matching. Finally, dental-arch-structured hypothesis selection evaluates diverse poses on held-out reliable correspondences, while a ground-truth-free coarse-retention guard conditionally retains a geometrically reliable coarse pose. On 60 held-out jaw pairs, APCReg achieves a submillimeter mean Chamfer distance of 0.87 mm and a Hausdorff distance of 2.92 mm under this evaluation protocol, and ranks first across the six reported metrics among the evaluated open-source baselines.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 24 of its 37 references that are in the map. First posted on arXiv 7 Aug 2026.

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References · 24

  1. 2017PointNet++: Deep Hierarchical Feature Learning on Point Sets in a Metric Spacecited by 4,401
  2. 2018Dynamic Graph CNN for Learning on Point Cloudscited by 2,223
  3. 20163DMatch: Learning Local Geometric Descriptors from RGB-D Reconstructionscited by 336
  4. 2019Deep Closest Point: Learning Representations for Point Cloud Registrationcited by 321
  5. 2020PREDATOR: Registration of 3D Point Clouds with Low Overlapcited by 290
  6. 2020TEASER: Fast and Certifiable Point Cloud Registrationcited by 274
  7. 2019PointNetLK: Robust & Efficient Point Cloud Registration using PointNetcited by 233
  8. 2020Deep Global Registrationcited by 229
  9. 2020RPM-Net: Robust Point Matching using Learned Featurescited by 182
  10. 2018PPFNet: Global Context Aware Local Features for Robust 3D Point Matchingcited by 181
  11. 2020D3Feat: Joint Learning of Dense Detection and Description of 3D Local Featurescited by 177
  12. 2018The Perfect Match: 3D Point Cloud Matching with Smoothed Densitiescited by 169
12 more
  1. 2021CoFiNet: Reliable Coarse-to-fine Correspondences for Robust Point Cloud Registrationcited by 152
  2. 2021PointDSC: Robust Point Cloud Registration using Deep Spatial Consistencycited by 142
  3. 20193DRegNet: A Deep Neural Network for 3D Point Registrationcited by 96
  4. 2023GeoTransformer: Fast and Robust Point Cloud Registration with Geometric Transformercited by 84
  5. 2019DeepICP: An End-to-End Deep Neural Network for 3D Point Cloud Registrationcited by 77
  6. 2019PCRNet: Point Cloud Registration Network using PointNet Encodingcited by 63
  7. 20233DTeethSeg'22: 3D Teeth Scan Segmentation and Labeling Challengecited by 14
  8. 2021Fully automatic integration of dental CBCT images and full-arch intraoral impressions with stitching error correction via individual tooth segmentation and identificationcited by 5
  9. 2019Automatic Registration between Cone-Beam CT and Scanned Surface via Deep-Pose Regression Neural Networks and Clustered Similaritiescited by 4
  10. 2025PSReg: Prior-guided Sparse Mixture of Experts for Point Cloud Registrationcited by 2
  11. 2025MICCAI STSR 2025 Challenge: Semi-Supervised Teeth and Pulp Segmentation and CBCT-IOS Registrationcited by 2
  12. 2025MCI-Net: A Robust Multi-Domain Context Integration Network for Point Cloud Registrationcited by 1

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