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Deep learning reconstructs 3D oral models from 2D images

Researchers have developed a novel deep learning method to reconstruct 3D models of oral cavities using only 2D intraoral images. This approach aims to reduce costs and patient discomfort associated with traditional dental modeling techniques like impression taking and expensive intraoral scanners. The model, trained on the Dental3DS dataset, utilizes MobileNetV2 and Multi-head Attention to achieve 77.49% accuracy, though it notes uneven point distribution in reconstructions. AI

IMPACT Offers a lower-cost, less invasive alternative for dental 3D modeling, potentially increasing adoption.

RANK_REASON The cluster contains an academic paper detailing a new deep learning model for a specific application.

Read on arXiv cs.CV →

AI-generated summary · Google Gemini · from 2 sources. How we write summaries →

COVERAGE [2]

  1. arXiv cs.CV TIER_1 English(EN) · Jihun Cho, Soo-Yeon Jeong, Eun-Jeong Bae, Sun-Young Ihm ·

    Deep Learning-based 3D Oral Cavity Reconstruction Using 2D Intraoral Images

    arXiv:2606.05998v1 Announce Type: new Abstract: 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,…

  2. arXiv cs.CV TIER_1 English(EN) · Sun-Young Ihm ·

    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 mat…