Physics‐informed multimodal learning for snapshot dental spectral reflectance prediction

Abstract Accurate color matching is essential to achieving aesthetically realistic outcomes in dental crown and bridge restorations. Traditional visual methods, however, are often affected by lighting variations and observer subjectivity. These limitations can lead to metamerism and inconsistent clinical outcomes. In this paper, we propose a deep learning‐based snapshot spectral reflectance prediction framework by incorporating multimodal optical priors into a physics‐informed modular reconstruction pipeline. Architecturally, it employs dual subnetworks integrated with attention mechanisms to enhance both spectral and spatial fidelity. A dedicated dataset consisting of over 4000 RGB–hyperspectral image pairs of dental samples is constructed under diverse lighting conditions using a custom‐built acquisition setup, enabling robust reflectance restoration. Quantitative evaluations demonstrate that the mean squared error of the reconstructed spectral reflectance is 0.002, with the structural similarity index measure achieving 0.8496. The proposed spectral reflectance reconstruction framework is fast, reliable, and robust, offering a physically consistent and clinically applicable solution for accurate color matching. Furthermore, its modular design provides flexibility, suggesting potential for adaptation and broader applicability across various biomedical imaging domains.