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K-U-KAN reconstructs 3D dental models from single X-rays

Researchers have developed K-U-KAN, a novel three-stage pipeline for reconstructing 3D dental models from single panoramic X-ray images. This method utilizes Kolmogorov-Arnold Networks (U-KAN) enhanced with Koopman operator theory to efficiently recover depth information, outperforming existing neural representations in training speed and robustness. K-U-KAN achieves comparable signal and structure metrics to transformer and implicit baselines while offering improved perceptual quality and interpretability, making it a more practical tool for clinical dental pipelines. AI

IMPACT Introduces a more efficient and robust method for 3D dental reconstruction from single X-rays, potentially improving clinical workflows.

RANK_REASON The cluster contains an academic paper detailing a new method for 3D dental reconstruction. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.AI →

AI-generated summary · Google Gemini · from 1 sources. How we write summaries →

K-U-KAN reconstructs 3D dental models from single X-rays

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The cluster contains an academic paper detailing a new method for 3D dental reconstruction. [lever_c_demoted from research: ic=1 ai=1.0]
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COVERAGE [1]

  1. arXiv cs.AI TIER_1 English(EN) · Bikram Keshari Parida, Abhijit Sen, Wonsang You ·

    K-U-KAN: Koopman-Enhanced U-KAN for 3D Dental Reconstruction from a Single Panoramic X-ray Radiograph

    arXiv:2605.25163v1 Announce Type: cross Abstract: A panoramic X-ray compresses a 3D jaw into a 2D strip; we aim to recover the missing depth cleanly and fast. Existing implicit neural representations render realistic volumes but are slow to train, sensitive to sampling and positi…