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Med. Biol."],"published-print":{"date-parts":[[2026,10,14]]},"abstract":"<jats:title>Abstract<\/jats:title>\n                  <jats:p>\n                    <jats:italic>Objective.<\/jats:italic>\n                    To develop GeoCM-Pose, a geometry-aware monocular dental 2D\/3D registration method that predicts metric model-to-camera 6DoF pose from one image under weak texture, repetitive anatomy and partial visibility.\n                    <jats:italic>Approach.<\/jats:italic>\n                    GeoCM-Pose comprises cross-modal adaptation (CMA) and geometry-aware pose regression (GAPR). CMA uses fixed-Gaussian frequency separation and a high-frequency structure-preservation module with multi-scale edge extraction, spatial\u2013channel gating and cross-resolution refinement. GAPR uses a ResNet-50\/U-Net to predict dense object-coordinate maps; hierarchical geometric feature aggregation and geometry-aware channel refinement feed decoupled unit-quaternion and translation heads. Joint 20-point pose-matching and dense-coordinate losses train the network. The study comprised 30\n                    <jats:italic>in vitro<\/jats:italic>\n                    training cases, five\n                    <jats:italic>in vivo<\/jats:italic>\n                    validation cases, five\n                    <jats:italic>in vivo<\/jats:italic>\n                    internal-test cases, 10 fully held-out Teeth3DS+ dental surface models and a 200-frame benchmark generated from a single three-dimensional dental model.\n                    <jats:italic>Main Results.<\/jats:italic>\n                    GAPR\/V0 generated pose estimates for all 200 query frames, with median reprojection, rotation and translation-vector errors of 2.156 px, 1.252\u00b0 and 0.653 mm, respectively, and a &lt;5 px pass rate of 91.5% across all attempts. It used no inference-time reference-image bank. At R01, scale-invariant feature transform and ORB generated 85\/200 and 89\/200 valid estimates, with &lt;5 px all-attempt pass rates of 40.5% and 40.0%; at R20, each generated 197\/200 valid estimates, with pass rates of 98.0% and 95.5%. Thus, conventional performance increased with the available reference bank, whereas GAPR\/V0 provided complete query coverage from a single image without inference-time reference images. Median GAPR\/V0 inference latency, including image I\/O, was 11.459 ms.\n                    <jats:italic>Significance.<\/jats:italic>\n                    GeoCM-Pose integrates CMA, dense object-coordinate supervision and metric pose regression for monocular dental 2D\/3D registration. Further case-level evaluation using intraoral endoscopic images acquired from independent patient cases and additional image domains, together with robot-integrated experiments, is required to assess generalisation and system-level performance.\n                  <\/jats:p>","DOI":"10.1088\/1361-6560\/aeaa9e","type":"journal-article","created":{"date-parts":[[2026,9,21]],"date-time":"2026-09-21T22:52:17Z","timestamp":1790031137000},"page":"195019","update-policy":"https:\/\/doi.org\/10.1088\/crossmark-policy","source":"Crossref","is-referenced-by-count":0,"title":["GeoCM-Pose: geometry-aware monocular dental 2D\/3D registration benchmarked against reference-assisted methods"],"prefix":"10.1088","volume":"71","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-6580-5449","authenticated-orcid":true,"given":"Zhixian","family":"Qiu","sequence":"first","affiliation":[{"name":"Key Laboratory of Advanced Manufacturing and Intelligent Technology, Ministry of Education, Harbin University of Science and Technology"}],"role":[{"vocabulary":"crossref","role":"author"},{"role":"conceptualization","vocabulary":"credit"},{"role":"methodology","vocabulary":"credit"},{"role":"software","vocabulary":"credit"},{"role":"writing-original-draft","vocabulary":"credit"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0491-9236","authenticated-orcid":true,"given":"Jingang","family":"Jiang","sequence":"additional","affiliation":[{"name":"Key Laboratory of Advanced Manufacturing and Intelligent Technology, Ministry of Education, Harbin University of Science and Technology"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jie","family":"Pan","sequence":"additional","affiliation":[{"name":"Peking University School of Stomatology"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Xinrui","family":"Cheng","sequence":"additional","affiliation":[{"name":"Computer Science and Information Technology, Royal Melbourne Institute of Technology (RMIT) University"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0003-9792-8170","authenticated-orcid":true,"given":"Qunsong","family":"Qiu","sequence":"additional","affiliation":[{"name":"Key Laboratory of Advanced Manufacturing and Intelligent Technology, Ministry of Education, Harbin University of Science and Technology"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jiawei","family":"Zhang","sequence":"additional","affiliation":[{"name":"Key Laboratory of Advanced Manufacturing and Intelligent Technology, Ministry of Education, Harbin University of Science and Technology"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jingchao","family":"Wang","sequence":"additional","affiliation":[{"name":"The Second Affiliated Hospital of Harbin Medical University"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Guang","family":"Yu","sequence":"additional","affiliation":[{"name":"Tsinghua University"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"266","published-online":{"date-parts":[[2026,10,5]]},"reference":[{"key":"pmbaeaa9ebib1","doi-asserted-by":"publisher","DOI":"10.1016\/j.compmedimag.2019.101684","type":"journal-article","article-title":"MedGAN: medical image translation using GANs","volume":"79","author":"Armanious","year":"2020","journal-title":"Comput. 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All rights, including for text and data mining, AI training, and similar technologies, are reserved.","name":"copyright_information","label":"Copyright Information"},{"value":"2026-05-19","name":"date_received","label":"Date Received","group":{"name":"publication_dates","label":"Publication dates"}},{"value":"2026-09-21","name":"date_accepted","label":"Date Accepted","group":{"name":"publication_dates","label":"Publication dates"}},{"value":"2026-10-05","name":"date_epub","label":"Online publication date","group":{"name":"publication_dates","label":"Publication dates"}}]}}