arXiv (All)
open access
Deep Learning-based 3D Oral Cavity Reconstruction Using 2D Intraoral Images
Oral 3D modelling is one of the most essential stages in dentistry, and many different approaches, such as impression taking and intraoral scanning, are commonly used for this phase, each with notable limitations. Impression taking, which involves placing alginate or silicone material in a tray and
Conceptio Open Archive
Analyze & explore related research in Conceptio
Connect this record directly into your research workflow or AI agent. Explore citations across open repositories, query full text, and export structured proof bundles.
This document is indexed with metadata only: full text is not available in the archive for this record. Open the official source ↗
Related documents
- Classification of dental implants using supervised deep learning from dental radiographs: A scoping review.#351474
- Multimodal transformer-enhanced deep learning for accurate assessment of mandibular third molar–inferior alveolar canal contact using panoramic radiography and CBCT slices.#617123
- Dental Odontogenic Lesion CBCT and Histopathology Integrated Dataset for Benchmarking Deep Learning Algorithms.#214219
- Artificial Intelligence to Detect Missed Root Canals in Previously Treated Lower Back Teeth Using 3D Dental Imaging (CBCT)#1112003
- A Dual-Branch Deep Learning Framework with Explainability for Dental Caries Classification Using Intra-Oral Photographs and Radiographs.#232337
Record · ID 1076097
Retrieved via Conceptio: every document is proof-bundled with source, license, and retrieval metadata.
Retrieved via Conceptio: every document is proof-bundled with source, license, and retrieval metadata.
Bring this data into your agent: Connect via MCP or API · 20 free credits to start · no card required
Read the Docs → · View CLI source on GitHub · Conceptio Open Knowledge Archive
Read the Docs → · View CLI source on GitHub · Conceptio Open Knowledge Archive